# US 2026/0099765 A1 · Meta-data centric approach for predicted class reassignment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0099765 A1 - Document ID: US20260099765A1 - Status: Published application - Kind: A1 - Application number: 18/967,020 - Filed: 2024-12-03 - Published: 2026-04-09 - Earliest claimed: 2024-10-09 - Assignees: Hewlett Packard Enterprise Development LP - Applicants: Hewlett Packard Enterprise Development LP - Inventors: Madhusoodhana Chari Sesha; Pradeep Kumar Surendran; Ankush Anshuman; Akshay Jain; Surya Thankamony Somanathan - Web page: https://patentgrep.com/patent/US20260099765A1 ## CPC - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods are provided for reassigning classifications based on performance metrics obtained from prior instances of classifications. Examples include obtaining performance metrics of instances of classifications performed by a classification model in classifying data samples and generating a confusion matrix for the classification model that clusters the instances of classifications into a plurality of groups. For each group of the plurality of groups, examples derive a threshold from the performance metrics for instances of data samples constituting the respective group. The examples determine whether or not a classification predicted for an input data sample is correct based on a prediction performance metric of the predicted classification and one or more of the thresholds for the plurality of groups of the confusion matrix. Examples can update the predicted classification based on the determination. ## Claims ### Claim 1 A method comprising: obtaining performance metrics of instances of classifications performed by a classification model in classifying data samples, wherein the classification model is trained to classify the data samples as belonging to a class; generating a confusion matrix for the classification model, wherein the confusion matrix comprises the instances clustered into a plurality of groups, the plurality of groups comprising a true positive group, a false positive group, a true negative group, and a false negative group; for each group of the plurality of groups, deriving a threshold from the performance metrics for instances of data samples constituting the respective group; predicting a classification for an input data sample by applying the input data sample to the classification model; determining whether or not the predicted classification is correct based on a prediction performance metric of the predicted classification and one or more of the thresholds for the plurality of groups of the confusion matrix; and updating the predicted classification based on determining whether or not the predicted classification is correct or not. ### Claim 2 (depends on 1) The method of claim 1, wherein the performance metrics comprise a confidence score of the instances. ### Claim 3 (depends on 1) The method of claim 1, wherein the prediction performance metric comprises a confidence score of the predicted classification. ### Claim 4 (depends on 1) The method of claim 1, further comprising: for each group of the confusion matrix, clustering the instances into the plurality of groups based on the performance metrics; and deriving one or more centroids from feature sets of the instances of the data samples constituting the respective group, wherein determining whether or not the predicted classification is correct is based on the one or more centroids. ### Claim 5 (depends on 4) The method of claim 4, further comprising: for each group of the plurality of groups, computing a threshold distance based on the one or more centroids of an adjacent group of the plurality of groups. ### Claim 6 (depends on 5) The method of claim 5, wherein the threshold distance for each group is based on a distance between the input data sample and the one or more centroids of the adjacent group. ### Claim 7 (depends on 5) The method of claim 5, wherein determining whether or not the predicted classification is correct comprises: for each group of the plurality of groups, determining a prediction distance from the one or more centroids of a respective group to the input data sample; comparing the prediction distance of a respective group to the threshold distance of the respective group; and updating the predicted classification based on the comparison. ### Claim 8 (depends on 6) The method of claim 6, wherein updating the predicted classification based on the comparison comprises: responsive to a determination that the input data sample exceeds the threshold distance of the true positive group, comparing the input data sample to the threshold distance of the false negative group; and responsive to a determination that the input data sample satisfies the threshold distance of the false negative group, classifying the input data sample as a belonging to the class. ### Claim 9 (depends on 6) The method of claim 6, wherein updating the predicted classification based on the comparison comprises: responsive to a determination that the input data sample exceeds the threshold distance of the true positive group, comparing the input data sample to the threshold distance of the false negative group; and responsive to a determination that the input data sample does not satisfy the threshold distance of the false negative group, comparing the input data sample to the threshold distance of the true negative group; and reclassifying the input data sample as a belonging to another class based on the comparison. ### Claim 10 A system, comprising: a memory storing instructions; and at least one processor communicatively connected to the memory and configured to execute the instructions to: obtain performance metrics of instances of classifications performed by a classification model in classifying data samples, wherein the classification model is trained to classify the data samples as belonging to a class; generate a confusion matrix for the classification model, wherein the confusion matrix comprises the instances clustered into a plurality of groups, the plurality of groups comprising a true positive group, a false positive group, a true negative group, and a false negative group; for each group of the plurality of groups, derive a threshold from the performance metrics for instances of data samples constituting the respective group; predict a classification for an input data sample by applying the input data sample to the classification model; predict a classification for an input data sample by applying the input data sample to the classification model; and update the predicted classification based on determining whether or not the predicted classification is correct or not. ### Claim 11 (depends on 10) The system of claim 10, wherein the processor is further configured to execute the instructions to: for each group of the confusion matrix, cluster the instances into the plurality of groups based on the performance metrics; and derive one or more centroids from feature sets of the classification instances of the data samples constituting the respective group, wherein determining whether or not the predicted classification is correct is based on the one or more centroids. ### Claim 12 (depends on 11) The system of claim 11, wherein the processor is further configured to execute the instructions to: for each group of the plurality of groups, compute a threshold distance based on the one or more centroids of an adjacent group of the plurality of groups. ### Claim 13 (depends on 12) The system of claim 12, wherein the threshold distance for each group is based on a distance between the input data sample and the one or more centroids of the adjacent group. ### Claim 14 (depends on 12) The system of claim 12, wherein determining whether or not the predicted classification is correct comprises: for each group of the plurality of groups, determining prediction distance from the one or more centroids of a respective group to the input data sample; comparing the prediction distance of a respective group to the threshold distance of the respective group; and updating the predicted classification based on the comparison. ### Claim 15 (depends on 12) The system of claim 12, wherein updating the predicted classification based on the comparison comprises: responsive to a determination that the input data sample exceeds the threshold distance of the true positive group, comparing the input data sample to the threshold distance of the false negative group; and responsive to a determination that the input data sample satisfies the threshold distance of the false negative group, classifying the input data sample as a belonging to the class. ### Claim 16 (depends on 12) The system of claim 12, wherein updating the predicted classification based on the comparison comprises: responsive to a determination that the input data sample exceeds the threshold distance of the true positive group, comparing the input data sample to the threshold distance of the false negative group; and responsive to a determination that the input data sample does not satisfy the threshold distance of the false negative group, comparing the input data sample to the threshold distance of the true negative group; and reclassifying the input data sample as a belonging to another class based on the comparison. ### Claim 17 A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to: extract metadata associated with instances of classifying data by a machine learning model trained to classify data as belonging to a class, the metadata comprising confidence score and a tag indicative of a correctness for each instance; cluster the instances into a plurality of groups based on the tags; derive a plurality of thresholds for each group of the plurality of groups based on confidence scores associated with instances clustered into a respective group; evaluate a predicted classification, by the machine learning model, for an input data sample based on a comparison of confidence score for the predicted classification to one or more of the plurality of thresholds; and generate a label for the input data sample based on the evaluation, wherein the label is indicative of whether or not the input data sample belongs to the class. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the instructions, when executed by the processor, further cause the processor to: determine a first plurality of thresholds of the plurality of thresholds from the confidence scores associated with instances clustered into the plurality of groups; compute one or more centroids for each group of the plurality of groups based on features sets for the instances constituting a respective group; for each group of the plurality of groups, determine a second threshold of the plurality of thresholds based on distances from the input data sample to the one or more centroids of another group of the plurality of groups; and determine a prediction distance from a set of features of the input data sample to the one or more centroids of the respective group, wherein evaluating the predicted classification comprises one or more of comparing the confidence score for the predicted classification to one or more of the first plurality of thresholds and comparing the prediction distance of a respective group to the second threshold of the respective group. ### Claim 19 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the confidence score comprise a probability that data of an instance corresponds to the class. ### Claim 20 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the confidence score for each instance are determined during a training phase of the machine learning model.