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
This action is in response to the amended filing on 07/27/2026. Claims 1-20 are pending for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, Claim 1 recites: assigning, via an artificial intelligence (AI) algorithm executed by the at least one processor, each data item included in the first set of data items to at least one from among a first subset that includes at least one numeric feature and a second subset that includes at least one categorical feature. It is unclear what “assigning… each data item included in the first set of data items to at least one from among a first subset [and second subset]” is intended to refer, as there appears to be a word or words missing between “one” and “from”. For the purposes of examination, this limitation is interpreted as: assigning... each data item included in the first set of data items to at least one group from among a first subset that includes at least one numeric feature and a second subset that includes at least one categorical feature.
Regarding Claims 10 and 19, Claims 10 and 19 are apparatus and computer-readable storage medium claims corresponding to the method of Claim 1. As such, they are rejected for the same reasons.
Regarding Claims 2-9, 11-18, and 20, Claims 2-9, 11-18, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending on an indefinite parent claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Beharie et al. (US 20230419345 A1, hereinafter Beharie) in view of KUNNUMPURATH et al. (WO 2023151799 A1, hereinafter Kunnumpurath) in further view of Vinov et al. (US 20230025731 A1, hereinafter Vinov).
Beharie and Kunnumpurath were cited in the previous office action.
Regarding Claim 1, Beharie teaches: receiving, by the at least one processor, a first set of data items that relate to respective characteristics of a first situation; (Paragraph [0090] In a sixth aspect, there is provided a computer-implemented system for providing explainable predictions, comprising: a user input device, the user input device receiving a prediction objective from a user; a memory, the memory comprising at least one data set from at least one data source; a processor in communication with the user input device and the memory, the processor configured to: determine at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generate at least one channel attribution model from the at least one feature of the at least one activity, the at least one channel attribution model operative to provide a channel prediction and at least one prediction rationale; and generate an explainable prediction comprising the channel prediction and the at least one prediction rationale corresponding to the channel prediction, the at least one prediction rationale determined based on the prediction objective received from the user and the at least one channel attribution model.(Since the system determines at least one activity from one dataset, the data must be receieved))
generating, by the at least one processor, a first prediction that corresponds to an undesirable outcome with respect to the first situation based on the first set of data items; (Paragraph [0011] In a first aspect, there is provided a computer-implemented method for providing explainable predictions, comprising: receiving a prediction objective from a user; providing, at a memory, at least one data set from at least one data source; determining, at a processor in communication with the memory, at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating, at the processor, at least one attribution model from the at least one feature of the at least one activity, the at least one attribution model operative to provide a prediction and at least one prediction rationale; and generating, at the processor, an explainable prediction comprising the prediction and the at least one prediction rationale corresponding to the prediction, the at least one prediction rationale determined based on the prediction objective received from the user and the at least one attribution model. (Generating prediction at user’s request, including something undesirable))
generating, by the at least one processor, a second prediction that corresponds to a desirable outcome with respect to the first situation; (Paragraph [0011] In a first aspect, there is provided a computer-implemented method for providing explainable predictions, comprising: receiving a prediction objective from a user; providing, at a memory, at least one data set from at least one data source; determining, at a processor in communication with the memory, at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating, at the processor, at least one attribution model from the at least one feature of the at least one activity, the at least one attribution model operative to provide a prediction and at least one prediction rationale; and generating, at the processor, an explainable prediction comprising the prediction and the at least one prediction rationale corresponding to the prediction, the at least one prediction rationale determined based on the prediction objective received from the user and the at least one attribution model. (Generating prediction at user’s request, including based on the outcome of the first situation))
However, Beharie fails to disclose: wherein the first counterfactual explanation is a global counterfactual explanation that is applicable to each of the first set of data items simultaneously, and
In the same field of endeavor Kunnumpurath teaches: wherein the first counterfactual explanation is a global counterfactual explanation that is applicable to each of the first set of data items simultaneously, and (Page 10 Line 4-9 The counterfactual tree may be generated using the C4.5 statistical classifier machine- learning algorithm, for example, using as training data a data set that includes sets of input parameters for the ML model of the network, each set corresponding to a particular modeled instance of time, along with corresponding “favorable” and “unfavorable” outcomes. In this example, the features of the decision tree, taken from the ML model of the network, are shown in Table 3. Page 11 Lines 1-3 Figure 5 illustrates details of a portion of an example Bayesian inference network, which is applied here to evaluate the reliability of one or more recourse actions defined by the rules in the decision tree generated by the counterfactual algorithm. Page 18 Lines 15-25 With some embodiments of the techniques described herein, counterfactual explainers indicate feature-level prescriptions for further recourse. These feature-level prescriptions can potentially indicate data-level drifts or biases in the model. In the example of a machine-learning algorithm applied to human transactions, for instance, these prescriptions might reveal a preference in the model for a specific gender or race for a favorable outcome, e.g., if there are hidden race-specific or gender-specific features embodied in the machinelearning algorithms model. (A counterfactual tree is generated based on an entire data set as its input, allowing for the complete viewing of the data set with favorable and unfavorable outcomes along with changes necessary to achieve these outcomes. This amounts to a global counterfactual explanation, as a GCE is a generalized explanation allowing for a machine model to make decisions across an entire dataset, so too does the counterfactual data tree.))
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated the concept of a first counterfactual explanation being a global counterfactual explanation that is applicable to each of the first set of data items simultaneously as suggested by Kunnumpurath into the reference of Beharie because both of these references address the field of predictions through the use of machine learning models. Doing so would improve Beharie by allowing for optimization and the ability to answer different types of queries most suitable for different types of predictions (Kunnumpurath Page 2).
The combination of Beharie and Kunnumpurath does not teach: assigning, via an artificial intelligence (AI) algorithm executed by the at least one processor, each data item included in the first set of data items to at least one from among a first subset that includes at least one numeric feature and a second subset that includes at least one categorical feature; converting, via the Al algorithm executed by the at least one processor, each categorical feature included in the second subset into a respective vector item that includes a corresponding direction and a corresponding magnitude; and determining, via the Al algorithm executed by the at least one processor, a first counterfactual explanation that indicates a potential change to at least one data item included in the first set of data items that modifies the first prediction to the second prediction; wherein the potential change to the at least one data item determined includes a modification of the Al algorithm that changes a magnitude of a vector of the at least one data item.
In the same field of endeavor, Vinov teaches: assigning, via an artificial intelligence (AI) algorithm executed by the at least one processor, each data item included in the first set of data items to at least one from among a first subset that includes at least one numeric feature and a second subset that includes at least one categorical feature; (Paragraph [0043] Model 112 may be any suitable machine learning model that is configured to make a prediction based on an input datapoint, namely—to map the input to a certain output based on the model's learned association between input-output pairs in a training stage. One example of such model is a machine learning classifier, which classifies input into one or more different classes (groups). In the Census Income example, the model may be trained to predict (estimate), based on multiple feature values of a certain person, whether that person earns more than $50,000 a year, or less than (or exactly) $50,000 a year. (Input data is classified into a group, such as one depending on categorical features))
converting, via the Al algorithm executed by the at least one processor, each categorical feature included in the second subset into a respective vector item that includes a corresponding direction and a corresponding magnitude; and (Paragraph [0045] The term “features,” as referred to herein, may relate to those individual, measurable properties (or “characteristics”) of a datapoint. For example, in a computer vision model, features of a datapoint (image) may include various edges, ridges, blobs, etc. In a speech recognition model aimed at recognizing phonemes, features may include noise ratios, length of sounds, relative power, etc. In spam (junk email) detection models, features may include the presence or absence of certain email headers, the language, the frequency of specific terms, the grammatical correctness of the text, etc. In a business data analysis model, such as a classification model for the Census Income dataset, features may include a person's age, income, marital status, etc. Typically, the value of each feature is given as a number, but in certain special cases may be provided as a string (of characters, symbols, and/or numbers). The collection of all feature values of a datapoint is often referred to as a “feature vector.” Paragraph [0062] Notably, the gradients provide both direction (positive/negative) and magnitude (numerical value). For example, a negative change (−0.676) in marital status (e.g., being unmarried as opposed to married) will have the greatest impact on the error calculated by the modified loss function, followed by a positive change (+0.674) in the hours worked per week, and so on. Conversely, increasing the age feature value (−0.0037 gradient) will only have a small impact on the error. Paragraph [0063] Fourth, the calculated gradients may be translated into a set of candidate model constraints that propose changes to feature values in view of these gradients. (Input features which are expressed as (i.e. converted into) feature vectors, the vectors each being associated with a gradient loss function that acts to give these vectors direction and magnitude when calculating potential changes to be made when generating a counterfactual explanation.))
determining, via the Al algorithm executed by the at least one processor, a first counterfactual explanation that indicates a potential change to at least one data item included in the first set of data items that modifies the first prediction to the second prediction (Paragraph [0084] In an optional step 214, the raw solution given by the solver in step 212, which is typically a mathematical representation, may be translated into a textual solution that can be more readily understood by the user as a counterfactual explanation. As a simplistic example, if the raw solution includes a value of “5%” for feature number 15, in datapoint number 100, for class 1, its translation may be to the following sentence: “a 5% increase in the ratio of whisker length to nose size in image no. 100 would have caused the model to classify the object in the image as a cat.” In this sentence, “5%” is the extent of perturbation, “ratio of whisker length to nose size” is the name of feature number 15 in the feature vector, and “cat” is class no. 1. Of course, this is just one example of how numerical/mathematical results of the CSP may be translated into natural language for better human comprehension. (Determining an explanation indicating a change to a data item (i.e. ratio of whisker length to nose size) that would change a first prediction (not a cat) to a second prediction (a cat)))
wherein the potential change to the at least one data item determined includes a modification of the Al algorithm that changes a magnitude of a vector of the at least one data item. (Paragraph [0051] In a step 208, a second set of CSP rules may be generated, this time based on model 112. These CSP rules may define one or more perturbation candidates among the features of one of the datapoints, as well as the extent (amount, magnitude) of perturbation. (A change in the data item to produce a counterfactual means a change in its magnitude))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concepts of assigning, via an AI algorithm executed by at least one processor, data items included in a first set of data items to at least one from among a first subset that includes at least one numeric feature and a second subset including at least one categorical feature, conversion, via the AI algorithm executed by the at least one processor, of categorical features in the second subset into a respective vector that includes corresponding direction and magnitude, determining, via the AI algorithm executed by the at least one processor, a first counterfactual explanation indicating a potential change in at least one data item in the first set of data items that modifies a first and second prediction, and the potential change includes a modification that changes a magnitude of the vector of at least one data item as taught by Vinov into the combination of Beharie and Kunnumpurath as all three references are in the same field of generation of counterfactual explanations and doing so would help to increase the trust users may have in machine learning models by allowing these models to explain their decision making process (Vinov Paragraph [0003]).
Regarding Claim 2, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: AI algorithm that uses a machine learning technique to analyze each data item included in the first set of data items. (Beharie Paragraph [0019] In one or more embodiments, the method may further include: determining an initiation model for each of a plurality of initiating subjects, each initiation model based on the at least one activity of the corresponding initiating subject and comprising a regression model; generating a predicted metric for a future time period based on the initiation model for the corresponding initiating subject; using an explanatory algorithm to generate a prediction explanation based on the at least one attribution model; and wherein the predicted metric may include a numerical prediction and the prediction explanation. (Explanatory algorithm generates a prediction based on input data.))
Regarding Claim 3, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 2 including: wherein when the potential change relates to a first categorical feature included in the second subset, the method further comprises using the Al algorithm to express the potential change as a set of if/then rules with exactly one then condition. (Beharie Paragraph [0026] In one or more embodiments, the determining the segment label may include: determining an embedding vector based on data from the at least one data source associated with the initiating subject; and generating at least one matching seed, the at least one matching seed based on the embedding vector, the at least one matching seed corresponding to a predicted segment label Paragraph [0027] In one or more embodiments, the method may further include identifying a distance metric for each of the at least one matching seed; and ranking the at least one matching seed based on the distance metric. Paragraph [0369] The attribution labelling task 2310 may generate labels that describe the effect of channels in the dataset. Channels in marketing data may refer to channels where advertisements are served such as a call or an e-mail. The attribution labelling task 2310 may output the number of conversions resulting from an action and the change in conversion rate caused by an action. The attribution labelling task may be performed using Shapley value attribution, feature importance or permutation importance. The attribution labelling task 2310 may send objective attribution labels to database 2306. The attribution labelling task 2310 may further send data to the preset orchestrator task 2312.)
Regarding Claim 4, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: wherein the set of if/then rules includes the at least one potential change to the corresponding magnitude of the vector of the first categorical feature included in the at least one data item. (Vinov Paragraph [0013] In some embodiments, the generating of the second set of CSP comprises, when a loss function of the machine learning model is inaccessible: calculating an anchor for the one datapoint using the machine learning model, wherein the anchor is a definition of conditions that are a likely cause of a certain classification by the machine learning model; and translating the conditions in the second set of CSP rules. Paragraph [0078] This anchor is essentially a definition of conditions (discrete feature values or ranges of values) that are the likely cause of the certain classification. For example, as shown in Table 3 of Ribeiro et al., which is based on the Census Income dataset, there are three feature values/ranges that strongly affect classification as ‘>50K’: native country is ‘United States’, marital status is ‘married’, and work hours per week is >45. Paragraph [0079] The feature values/ranges contained in the calculated anchor may serve as a basis for deciding what features in the selected datapoint to perturbate, and by how much. (A set of conditions (if/then rules) which determine a perturbation (change in magnitude) of the anchored feature))
Regarding Claim 5 the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: calculating a first metric that relates to a reliability of the first counterfactual explanation, wherein the reliability varies directly with an accuracy of the first counterfactual explanation, and (Kunnumpurath Page 3-4 Lines 22 - 3 The example method further comprises generating a counterfactual algorithm, e.g., by generating a tree-based classification algorithm, based on the stored favorability statuses and corresponding values of network parameters, to derive rules for producing a favorable status, based on one or more of the network parameters. The method still further comprises identifying a proposed recourse action comprising a change in at least one of the network parameters, based on the derived rules in the counterfactual algorithm, generating a decision network and determining a confidence level estimating a reliability of achieving a favorable status by changing the at least one network parameter, and determining whether to implement the proposed recourse action on the communication network, based on the confidence level.)
the reliability varies inversely with a recourse cost associated with the first counterfactual explanation. (Kunnumpurath Abstract: A proposed recourse action comprising a change in at least one of the network parameters is identified, based on the rules, and a decision network, such as a Bayesian inference network, is generated for determining a confidence level estimating a reliability of achieving a favorable status by changing the network parameter(s).).
Regarding Claim 6, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: calculating a second metric that relates to a computational time efficiency of the first counterfactual explanation, wherein the computational time efficiency that relates to a reduction in computation time that is required for generating the first counterfactual explanation. (Kunnumpurath Pg. 14-15, Lines 12-5 In one embodiment, as shown at block 710, the method of Figure 7 includes the step of identifying a favorability status of each of a plurality of predictions and/or decisions of a machine-learning algorithm acting on at least a portion of a communication network. In an alternative embodiment only a portion of individual predictions and/or decisions of a plurality of predictions and/or decisions of a machine-learning algorithm acting on at least a portion of a communication network may have their favorability status identified. For example, the method in one embodiment may select at random 75% of individual predictions and/or decisions and have their favorability status identified. Thus, the method monitors the outputs of a machine-learning algorithm, and records assessments of these outputs. As shown at block 720, the method further comprises storing these favorability statuses, along with corresponding values of network parameters used as input features in the machine-learning algorithm. Again, the term “favorability status” should be understood as referring to an indication of a degree of favorability for a prediction, decision, or other output of the machine-learning algorithm, e.g., a degree of closeness to one or more ideal values or ranges of values for one or more metrics or states characterizing the system. Identifying the favorability status might comprise calculating or computing the favorability status, in some embodiments, e.g., by comparing a numerical parameter to a threshold value to determine a binary favorable/unfavorable status, or by comparing a numerical parameter to each of several thresholds, to determine a numerical favorability status, or by calculating a metric representing a normalized degree of closeness of a numerical parameter or parameters to a target level, etc. For instance, if reducing power is a goal and the algorithm is predicting power, then a predicted power value that is greater than the 50.sup.th percentile within a cluster or network might be labeled as (i.e., identified as) unfavorable, and otherwise labeled as favorable. In other embodiments or instances, the favorability status may be input by a user, e.g., based on an objective or subjective evaluation of some aspect of network performance by the user. The identified favorability status might be a binary one (“unfavorable” vs. “favorable”), for example, or a ranking or score, e.g., favorability ranked on a scale of 0-10.)
Regarding Claim 7, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: analyzing the first counterfactual explanation to determine whether at least one from among a first bias that relates to a gender and a second bias that relates to race is indicated by the first counterfactual explanation. (Kunnumpurath Pg. 18 Lines 15-23 Another advantage of some embodiments of the techniques described herein is that data biases in the machine-learning algorithm can be detected by counterfactual explanation methods. With some embodiments of the techniques described herein, counterfactual explainers indicate feature-level prescriptions for further recourse. These feature-level prescriptions can potentially indicate data-level drifts or biases in the model. In the example of a machine-learning algorithm applied to human transactions, for instance, these prescriptions might reveal a preference in the model for a specific gender or race for a favorable outcome, e.g., if there are hidden race-specific or gender-specific features embodied in the machinelearning algorithms model. Corresponding biases might similarly be detected in communication networks, such that these biases can be mitigated with a human in the loop.)
Regarding Claim 8, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: modifying an underlying model associated with the first counterfactual explanation so as to reduce an effect of the at least one from among the first bias and the second bias. (Kunnumpurath Pg. 18 Lines 15-23 Another advantage of some embodiments of the techniques described herein is that data biases in the machine-learning algorithm can be detected by counterfactual explanation methods. With some embodiments of the techniques described herein, counterfactual explainers indicate feature-level prescriptions for further recourse. These feature-level prescriptions can potentially indicate data-level drifts or biases in the model. In the example of a machine-learning algorithm applied to human transactions, for instance, these prescriptions might reveal a preference in the model for a specific gender or race for a favorable outcome, e.g., if there are hidden race-specific or gender-specific features embodied in the machine learning algorithms model. Corresponding biases might similarly be detected in communication networks, such that these biases can be mitigated with a human in the loop.)
Regarding Claim 9, the combination of Beharie, Kunnumpurath, and Vinov teaches the invention as claimed in Claim 1 including: wherein the first situation relates to at least one from among a credit risk, a default risk with respect to a customer payment, and a recidivism risk with respect to criminal activity. (Kunnumpurath Pg. 18 Lines 25-30 The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure.)
Regarding Claims 10-20, they are machine and a product-of-manufacture claims that corresponding to the method of claims 1-9. Therefore, they are rejected for the same reason as claims 1-9 above.
Response to Arguments
Applicant’s arguments filed on 07/27/2026 on the remark (page 10-12) with respect to claims 1-20 under 35 U.S.C. 112(b) have been considered but are moot because the new ground of rejection (see rejection above).
Applicant’s arguments filed 07/27/2026, pages 2-9, with respect to claims 1-20 under 35 U.S.C 101 rejection have been fully considered and are persuasive. Therefore, the rejection of claims 1-20 under 35 U.S.C 101 is withdrawn.
Applicant’s arguments filed on 07/27/2026 on the remark (pages 16-19) with respect to claim 1-20 under 35 U.S.C 103 have been considered but are moot because the new ground of rejection (see rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chamarthy et al. (US 20220147852 A1) discloses mitigating biases in machine learning models and predictions.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN A CARDOSO whose telephone number is (571)272-8512. The examiner can normally be reached M-F 7:30 - 5:00, alternate Friday's off.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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