Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/29/2026 has been entered.
DETAILED ACTION
3. This Office Action is in response to the filing with the office dated 01/29/2026.
Claims 1, 8 and 15 have been amended. Claim 16 has been cancelled. Claim 21 has been added. Claims 1, 8 and 15 are independent claims. Claims 1-15, 17-21 are presented for examination.
Response to amendment/arguments
4. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) have been fully considered but are moot because the arguments are directed towards amended claims, thus necessitated the new ground of rejection as presented in this Office action.
Claim Rejections - 35 U.S.C. § 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 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 1-10, 12-15, 17-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen; Christopher (US 20230359942 A1) in view of Sarlos; Tamas (US 20090210470 A1), Zohrevand; Zahra (US 20220309360 A1) and further in view of HETHERINGTON; TAYLER (US 20200302318 A1).
Regarding independent claim 1, Nguyen; Christopher (US 20230359942 A1) teaches, a computer-implemented method for automated document classification, the method comprising: maintaining a decision tree comprising a set of decision nodes, the set of decision nodes including one or more rule-based nodes (Paragraph [0043] The knowledge model 210 stores rules based on domain expertise. In an embodiment, the knowledge model 210 is a rule-based system. The rules may be provided by a domain expert. The rules may incorporate thresholds specified by experts that may be used to predict values or take actions. For example, if certain input is above a predetermined threshold value, certain action should be performed) and one or more machine learning (ML) model-based nodes (Paragraph [0044] The generalized model 220 is a trained machine learning based model that makes predictions based on input data), wherein each rule-based node is configured to generate a node classification of a document by assessing information of the document against one or more corresponding rules (Paragraph [0043] The knowledge model 210 stores rules based on domain expertise. In an embodiment, the knowledge model 210 is a rule-based system. The rules may be provided by a domain expert. The rules may incorporate thresholds specified by experts that may be used to predict values or take actions. For example, if certain input is above a predetermined threshold value, certain action should be performed), and wherein each ML model-based node is configured to generate the node classification and a probability of the document indicating a confidence level in the node classification (Paragraph [0045] Each of the knowledge model 210 and the generalized model 220 makes a prediction and also outputs a measure of accuracy (or confidence score) associated with the predicted output);
receiving an unclassified set of documents (Paragraph [0133] The system receives 1410 an input text for classification, Also see Paragraph [0142]);
wherein the evaluation of each document at each rule-based node is determined by comparing outcomes of logical conditions within the rule-based node, wherein the evaluation of each document at each ML model-based node is determined based on the confidence level satisfying corresponding evaluation thresholds at the ML model-based node (Paragraph [0135] The system executes 1430 the knowledge model to generate a first output representing a first category for the input. The system executes 1440 the machine learning based model to generate a second output representing a second category for the input text. The system may determine a measure of accuracy of the category determined by the knowledge model and the ML model), and using the evaluations, assigning a proposed classification to each document of the unclassified set of documents; and using the proposed classification of each document of the unclassified set of documents, generating a set of classified documents (Paragraph [0136] The system provides the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system executes the ensemble model to determine 1450 a final category for the input text based on the first category determined by the knowledge model and the second category determined by the ML model. Also see Paragraphs [0078], [0142], [0146]. Also see Claim 2).
Nguyen et al fails to explicitly teach, receiving an unclassified set of documents; classifying each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes.
Sarlos; Tamas (US 20090210470 A1) teaches, receiving an unclassified set of documents; classifying each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes, wherein the evaluation of each document at each rule-based node is determined by comparing outcomes of logical conditions within the rule-based node (Figs. 1, 3, 4, 6, 7 Paragraphs [0032], [0033] discloses receiving set of documents to be analyzed/ classified. The plurality of located web documents or other objects may then be analyzed by a rule based or decision tree system to determine a "goodness" or relevance ranking. Also see Paragraphs [0043], [0044], [0057]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al by receiving an unclassified set of documents; classifying each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes, wherein the evaluation of each document at each rule-based node is determined by comparing outcomes of logical conditions within the rule-based node, as taught by Sarlos et al (Paragraphs [0032]-[0033])..
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the present invention include mechanisms to efficiently store numerical values of features that are to be analyzed by a rule based or decision tree system. The numerical values are mapped to a subset of integer values based on how they are to be analyzed by the rule based or decision tree system such that the results that are to be generated by the rule based or decision tree system are the same with or without such mapping operation being performed on the feature values. The subset of integer values to which the numerical values are mapped can have a significantly lower cardinality and utilize a smaller memory size than the original set of numerical values. Accordingly, the dataset of feature values can be significantly reduced in terms of storage size. as taught by Sarlos et al (Paragraph [0027]).
Nguyen et al and Sarlos et al fails to explicitly teach, Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree.
Zohrevand; Zahra (US 20220309360 A1) teaches, receiving an unclassified set of documents (Paragraph [0176] training of various embodiments of ML model BB may be supervised or unsupervised, where unsupervised training means that text documents 111-113 are initially unclassified (i.e. unlabeled). However after unsupervised training, ML model BB may classify text documents 111-113 into classes 121-123);
Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree (Paragraph [0166] classification of a text document by an autoencoder may entail the autoencoder inferring a respective score such as a probability for each of classes 121-123. When ML model BB is untrained, ML model BB is unable to recognize that the text document corresponds to a class. Thus when untrained, ML model BB may infer more or less similar probabilities for each of classes 121-123 for the text document (i.e., for each unclassified document inferring respective set of probabilities which are then sent to the ML model to classify the documents. Also see [0173], [0174]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al and Sarlos et al by providing Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree, as taught by Zohrevand et al (Paragraph [0166]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, unsupervised training may be easier to adopt because a human expert is not needed to class training examples in advance. Thus, unsupervised training saves human labor as taught by Zohrevand et al (Paragraphs [0236], [0237).
Nguyen et al, Sarlos et al and Zohrevand et al fails to explicitly teach, the one or more ML model-based nodes operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree.
HETHERINGTON; TAYLER (US 20200302318 A1) teaches, wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes (Paragraph [0033] discloses, node classification generated by a trained ML model. Examiner interprets the spilt value is based on satisfying a or not satisfying a condition . Also see [0079], [0088]) operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree (Paragraphs [0035]-[0037] discloses, the node classification is evaluated by using a rule/ condition that indicates classification by traversing down the decision tree to determine a traversal path of the document through the decision tree).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al, Sarlos et al and Zohrevand et al by providing the one or more ML model-based nodes operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree, as taught by HETHERINGTON et al (Paragraphs [0033]-[0037]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the techniques herein improve the fidelity of the optimized ruleset with the black-box model, improve BETA's optimization runtime, and increase the interpretability of the optimized ruleset by generating a fewer number of representative candidate rules as input to the ruleset-based global MLX technique as taught by HETHERINGTON et al (Paragraph [0023]).
Regarding dependent claim 2, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
HETHERINGTON et al further teaches, wherein traversing through the decision tree further comprises: iteratively refining the node classification of each document using a plurality of ML model-based nodes, wherein the node classification of a subsequent ML model-based node is progressively narrower than the node classification of a previous ML model-based node (Figs. 1, 5 shows that the node classification is progressively narrower than the previous node).
Regarding dependent claim 3, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
Nguyen et al further teaches, wherein the unclassified set of documents includes one or more of: structured metadata or unstructured content for each document (Fig. 13F, Paragraph [0034] The K-Translator is a tool that uses natural language processing to extract useful domain knowledge from conversational text and translate that knowledge into a form that can then be used to build both logical and K1st models in a semi-automated fashion. This form is a knowledge language, a domain-specific language (DSL) for capturing, storing and managing expert knowledge. The knowledge language may also be referred to herein as a rules language), further comprising: identifying, via a ML model-based node within the set of decision nodes, new information from the unstructured content within a particular document, wherein the new information is related to one or more of the logical conditions within the one or more rule-based nodes; structuring the new information in accordance with the corresponding logical conditions of the corresponding rule-based nodes; and evaluating the particular document at the corresponding rule-based nodes (Paragraphs [0031], [0032] The knowledge model can also have many forms and be adapted to suit many use-cases. The simplest implementations are logical operations on the input data to either output a boolean classification or more detailed categorical labels).
Sarlos et al also teaches, wherein the unclassified set of documents includes one or more of: structured metadata or unstructured content for each document (Paragraph [0034] structured metadata by filling in missing pieces in a form. Also see Paragraph [0052]), further comprising: identifying, via a ML model-based node within the set of decision nodes, new information from the unstructured content within a particular document, wherein the new information is related to one or more of the logical conditions within the one or more rule-based nodes; (Paragraphs [0031]-[0033] discloses, entering a query in the input box is in the form of unstructured content associated to a document. The plurality of located web documents or other objects may then be analyzed by a rule based or decision tree system to determine a "goodness" or relevance ranking. For instance, the documents or objects are ranked in order from most relevant to least relevant based on a plurality of feature values of the documents/objects, the user who initiated the search with a search request, and/or the search request);
structuring the new information in accordance with the corresponding logical conditions of the corresponding rule-based nodes; and evaluating the particular document at the corresponding rule-based nodes (Fig. 4, Paragraphs [0035]-[0039] discloses, structuring the query to the corresponding conditional elements/ logical conditions corresponding to the rule based nodes and evaluating the document).
Regarding dependent claim 4, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
Sarlos et al further teaches, further comprising: in response to the confidence level at a particular ML model-based node of a particular document being less than the corresponding evaluation threshold, cascading the document to a first subsequent decision node within the set of decision nodes; and in response to the confidence level at a particular ML model-based node of a particular document being greater than the corresponding evaluation threshold, cascading the document to a second subsequent decision node within the set of decision nodes, wherein the first subsequent decision node is different from the second subsequent decision node (Fig. 4, Paragraphs [0035]-[0039] discloses, in response to the confidence level/ threshold being less cascading the document to first set of decision nodes and in response to the confidence level/ threshold being greater, cascading the document to second set of decision nodes, wherein the first subsequent decision node is different from the second subsequent decision node).
Regarding dependent claim 5, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
Nguyen et al further teaches, further comprising: in response to the confidence level at a particular ML model-based node of a particular document being greater than the corresponding evaluation threshold, assigning the node classification as the proposed classification to the document (Paragraph [0046] If the ensembled oracle 230 determines that the prediction of the generalized model 220 is accurate (having accuracy above a threshold value or having a confidence score above a threshold value), the ensembled oracle 230 uses the prediction of the generalized model 220 (i.e., if the confidence score is above the threshold assigning the document/ object to the respective class).
Regarding dependent claim 6, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
Nguyen et al further teaches, wherein the unclassified set of documents includes one or more of: structured metadata or unstructured content for each document, wherein each rule-based node is configured to generate the node classification of each document using the structured metadata against the corresponding rule (Paragraph [0010] A system performs classified text inputs using a machine learning model combined with a knowledge model. The system receives an input text for classification based on a hierarchy of categories. The system provides the input text to a knowledge model. The knowledge model is a rule-based model comprising rules for classifying text), and wherein each ML model-based node is configured to generate the node classification and the probability for the node classification of each document using one or more of: the structured metadata or the unstructured content (Paragraph [0010] he system provides the input text to a machine learning based model is trained to classify text).
Regarding dependent claim 7, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
Nguyen et al further teaches, further comprising: recording indicators of one or more of: corresponding rule-based nodes or corresponding ML model-based nodes associated with the traversal through the decision tree of each document (Paragraph [0045] the knowledge model 210/ rule based nodes uses boolean rules, for example, rules specified as if-then-else statements that compare input data with thresholds to determine the result. Also see Paragraph [0078]).
Regarding independent claim 8, Nguyen; Christopher (US 20230359942 A1) teaches, a system for dynamically managing network selection of wireless devices comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor (Fig. 16 Paragraphs [0148]-[0150]), cause the system to: maintain a decision tree comprising a set of decision nodes, the set of decision nodes including one or more rule-based nodes (Paragraph [0043] The knowledge model 210 stores rules based on domain expertise. In an embodiment, the knowledge model 210 is a rule-based system. The rules may be provided by a domain expert. The rules may incorporate thresholds specified by experts that may be used to predict values or take actions. For example, if certain input is above a predetermined threshold value, certain action should be performed) and one or more machine learning (ML) model-based nodes (Paragraph [0044] The generalized model 220 is a trained machine learning based model that makes predictions based on input data), wherein each rule-based node is configured to generate a node classification of a document using one or more corresponding rules, and wherein each ML model-based node is configured to generate the node classification and a probability of the document indicating a confidence level in the node classification (Paragraph [0135] The system executes 1430 the knowledge model to generate a first output representing a first category for the input. The system executes 1440 the machine learning based model to generate a second output representing a second category for the input text. The system may determine a measure of accuracy of the category determined by the knowledge model and the ML model);
receive an unclassified set of documents (Paragraph [0133] The system receives 1410 an input text for classification, Also see Paragraph [0142]);
using the evaluations, assigning a proposed classification to each document of the unclassified set of documents; and using the proposed classification of each document of the unclassified set of documents, generate a set of classified documents (Paragraph [0136] The system provides the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system executes the ensemble model to determine 1450 a final category for the input text based on the first category determined by the knowledge model and the second category determined by the ML model. Also see Paragraphs [0078], [0142], [0146]. Also see Claim 2).
Nguyen et al fails to explicitly teach, receive an unclassified set of documents; classify each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes.
Sarlos; Tamas (US 20090210470 A1) teaches, receive an unclassified set of documents; classify each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes (Figs. 1, 3, 4, 6, 7 Paragraphs [0032], [0033] discloses receiving set of documents to be analyzed/ classified. The plurality of located web documents or other objects may then be analyzed by a rule based or decision tree system to determine a "goodness" or relevance ranking. Also see Paragraphs [0043], [0044], [0057]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al by receiving an unclassified set of documents; classifying each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes, wherein the evaluation of each document at each rule-based node is determined by comparing outcomes of logical conditions within the rule-based node, as taught by Sarlos et al (Paragraphs [0032]-[0033])..
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the present invention include mechanisms to efficiently store numerical values of features that are to be analyzed by a rule based or decision tree system. The numerical values are mapped to a subset of integer values based on how they are to be analyzed by the rule based or decision tree system such that the results that are to be generated by the rule based or decision tree system are the same with or without such mapping operation being performed on the feature values. The subset of integer values to which the numerical values are mapped can have a significantly lower cardinality and utilize a smaller memory size than the original set of numerical values. Accordingly, the dataset of feature values can be significantly reduced in terms of storage size. as taught by Sarlos et al (Paragraph [0027]).
Nguyen et al and Sarlos et al fails to explicitly teach, Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree.
Zohrevand; Zahra (US 20220309360 A1) teaches, receiving an unclassified set of documents (Paragraph [0176] training of various embodiments of ML model BB may be supervised or unsupervised, where unsupervised training means that text documents 111-113 are initially unclassified (i.e. unlabeled). However after unsupervised training, ML model BB may classify text documents 111-113 into classes 121-123);
Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree (Paragraph [0166] classification of a text document by an autoencoder may entail the autoencoder inferring a respective score such as a probability for each of classes 121-123. When ML model BB is untrained, ML model BB is unable to recognize that the text document corresponds to a class. Thus when untrained, ML model BB may infer more or less similar probabilities for each of classes 121-123 for the text document (i.e., for each unclassified document inferring respective set of probabilities which are then sent to the ML model to classify the documents. Also see [0173], [0174]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al and Sarlos et al by providing Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree, as taught by Zohrevand et al (Paragraph [0166]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, unsupervised training may be easier to adopt because a human expert is not needed to class training examples in advance. Thus, unsupervised training saves human labor as taught by Zohrevand et al (Paragraphs [0236], [0237).
Nguyen et al, Sarlos et al and Zohrevand et al fails to explicitly teach, the one or more ML model-based nodes operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree.
HETHERINGTON; TAYLER (US 20200302318 A1) teaches, wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes (Paragraph [0033] discloses, node classification generated by a trained ML model. Examiner interprets the spilt value is based on satisfying a or not satisfying a condition . Also see [0079], [0088]) operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree (Paragraphs [0035]-[0037] discloses, the node classification is evaluated by using a rule/ condition that indicates classification by traversing down the decision tree to determine a traversal path of the document through the decision tree).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al, Sarlos et al and Zohrevand et al by providing the one or more ML model-based nodes operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree, as taught by HETHERINGTON et al (Paragraphs [0033]-[0037]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the techniques herein improve the fidelity of the optimized ruleset with the black-box model, improve BETA's optimization runtime, and increase the interpretability of the optimized ruleset by generating a fewer number of representative candidate rules as input to the ruleset-based global MLX technique as taught by HETHERINGTON et al (Paragraph [0023]).
Regarding dependent claim 9, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the system of claim 8.
HETHERINGTON et al further teaches, wherein traversing through the decision tree further comprises: iteratively refine the node classification of each document using a plurality of ML model-based nodes, wherein the node classification of a subsequent ML model-based node is progressively narrower than the node classification of a previous ML model-based node (Figs. 1, 5 shows that the node classification is progressively narrower than the previous node).
Regarding dependent claim 10, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the system of claim 8.
HETHERINGTON et al further teaches, wherein the instructions further cause the system to: dynamically determine, for at least one ML model-based node, a specific ML model from a plurality of ML models for a corresponding decision node within the decision tree based on confidence levels of each of the plurality of ML models, wherein ML models with higher confidence levels are prioritized over ML models with lower confidence levels (Fig. 5 shows, dynamically determining a node for a corresponding decision node based on confidence level and high confidence levels are prioritized (Examiner interprets confidence levels as GINI metric). See Paragraphs [0111]-[0116]).
Regarding dependent claim 12, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the system of claim 8.
HETHERINGTON et al further teaches, wherein at least one ML model-based node is trained using previous sets of classified documents to determine patterns or features indicative of each category (Figs 1, 5 show the nodes are trained using previous sets to determine the patterns or features indicative of each category).
Regarding dependent claim 13, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the system of claim 8.
Nguyen et al further teaches, wherein at least one ML model-based node is configured to receive multi-modal inputs, wherein the multi-modal inputs include one or more of: text, image, audio, or video data (Paragraph [0133] The system receives 1410 an input text for classification. The input text may represent articles retrieved from a website. The classification may map the text to a category selected from a hierarchy of categories. Although the process is described in connection with classification of text, the process can be used for classifying any type of input including images, videos, audio signals, and so on).
Regarding dependent claim 14, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the system of claim 8.
Sarlos et al further teaches, wherein at least one decision node in the set of decision nodes generates a plurality of node classifications for the document (Fig. 4 Paragraph [0033] discloses shows a node classification described by their attributes based on decision tree system).
Regarding independent claim 15, Nguyen; Christopher (US 20230359942 A1) teaches, a non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system (Fig. 13, Paragraphs [0148]-[0150]), cause the system to: maintain a decision tree comprising a set of decision nodes, the set of decision nodes including one or more rule-based nodes (Paragraph [0043] The knowledge model 210 stores rules based on domain expertise. In an embodiment, the knowledge model 210 is a rule-based system. The rules may be provided by a domain expert. The rules may incorporate thresholds specified by experts that may be used to predict values or take actions. For example, if certain input is above a predetermined threshold value, certain action should be performed) and one or more machine learning (ML) model-based nodes (Paragraph [0044] The generalized model 220 is a trained machine learning based model that makes predictions based on input data), wherein each rule-based node is configured to generate a node classification of a document using one or more corresponding rules (Paragraph [0043] The knowledge model 210 stores rules based on domain expertise. In an embodiment, the knowledge model 210 is a rule-based system. The rules may be provided by a domain expert. The rules may incorporate thresholds specified by experts that may be used to predict values or take actions. For example, if certain input is above a predetermined threshold value, certain action should be performed), and wherein each ML model-based node is configured to generate the node classification and a probability of the document indicating a confidence level in the node classification (Paragraph [0045] Each of the knowledge model 210 and the generalized model 220 makes a prediction and also outputs a measure of accuracy (or confidence score) associated with the predicted output);
obtain an unclassified set of documents (Paragraph [0133] The system receives 1410 an input text for classification, Also see Paragraph [0142]);
and using the evaluations, assigning a proposed classification to each document of the unclassified set of documents (Paragraph [0136] The system provides the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system executes the ensemble model to determine 1450 a final category for the input text based on the first category determined by the knowledge model and the second category determined by the ML model. Also see Paragraphs [0078], [0142], [0146]. Also see Claim 2).
Nguyen et al fails to explicitly teach, obtain an unclassified set of documents; classify each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes.
Sarlos; Tamas (US 20090210470 A1) teaches, obtain an unclassified set of documents; classify each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes (Figs. 1, 3, 4, 6, 7 Paragraphs [0032], [0033] discloses receiving set of documents to be analyzed/ classified. The plurality of located web documents or other objects may then be analyzed by a rule based or decision tree system to determine a "goodness" or relevance ranking. Also see Paragraphs [0043], [0044], [0057]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al by receiving an unclassified set of documents; classifying each document of the unclassified set of documents by: traversing through the decision tree by evaluating each document of the unclassified set of documents at corresponding decision nodes, wherein the evaluation of each document at each rule-based node is determined by comparing outcomes of logical conditions within the rule-based node, as taught by Sarlos et al (Paragraphs [0032]-[0033])..
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the present invention include mechanisms to efficiently store numerical values of features that are to be analyzed by a rule based or decision tree system. The numerical values are mapped to a subset of integer values based on how they are to be analyzed by the rule based or decision tree system such that the results that are to be generated by the rule based or decision tree system are the same with or without such mapping operation being performed on the feature values. The subset of integer values to which the numerical values are mapped can have a significantly lower cardinality and utilize a smaller memory size than the original set of numerical values. Accordingly, the dataset of feature values can be significantly reduced in terms of storage size. as taught by Sarlos et al (Paragraph [0027]).
Nguyen et al and Sarlos et al fails to explicitly teach, Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree.
Zohrevand; Zahra (US 20220309360 A1) teaches, receiving an unclassified set of documents (Paragraph [0176] training of various embodiments of ML model BB may be supervised or unsupervised, where unsupervised training means that text documents 111-113 are initially unclassified (i.e. unlabeled). However after unsupervised training, ML model BB may classify text documents 111-113 into classes 121-123);
Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree (Paragraph [0166] classification of a text document by an autoencoder may entail the autoencoder inferring a respective score such as a probability for each of classes 121-123. When ML model BB is untrained, ML model BB is unable to recognize that the text document corresponds to a class. Thus when untrained, ML model BB may infer more or less similar probabilities for each of classes 121-123 for the text document (i.e., for each unclassified document inferring respective set of probabilities which are then sent to the ML model to classify the documents. Also see [0173], [0174]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al and Sarlos et al by providing Wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes operate as an input for a subsequent rule-based node or ML-model based within the decision tree, as taught by Zohrevand et al (Paragraph [0166]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, unsupervised training may be easier to adopt because a human expert is not needed to class training examples in advance. Thus, unsupervised training saves human labor as taught by Zohrevand et al (Paragraphs [0236], [0237).
Nguyen et al, Sarlos et al and Zohrevand et al fails to explicitly teach, the one or more ML model-based nodes operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree.
HETHERINGTON; TAYLER (US 20200302318 A1) teaches, wherein a respective node classification and a respective probability of the document generated by a particular ML model-based node within the one or more ML model-based nodes (Paragraph [0033] discloses, node classification generated by a trained ML model. Examiner interprets the spilt value is based on satisfying a or not satisfying a condition . Also see [0079], [0088]) operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree (Paragraphs [0035]-[0037] discloses, the node classification is evaluated by using a rule/ condition that indicates classification by traversing down the decision tree to determine a traversal path of the document through the decision tree).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al, Sarlos et al and Zohrevand et al by providing the one or more ML model-based nodes operate as an input that is evaluated by a subsequent rule-based node or ML model-based node within the decision tree to determine a traversal path of the document through the decision tree, as taught by HETHERINGTON et al (Paragraphs [0033]-[0037]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the techniques herein improve the fidelity of the optimized ruleset with the black-box model, improve BETA's optimization runtime, and increase the interpretability of the optimized ruleset by generating a fewer number of representative candidate rules as input to the ruleset-based global MLX technique as taught by HETHERINGTON et al (Paragraph [0023]).
Regarding dependent claim 17, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the non-transitory, computer-readable storage medium of claim 15.
Nguyen et al further teaches, wherein at least one ML model-based node includes a multinomial model that generates a plurality of classifications and corresponding probabilities for each classification for a corresponding document (Paragraph [0133] The system receives 1410 an input text for classification. The input text may represent articles retrieved from a website. The classification may map the text to a category selected from a hierarchy of categories. Although the process is described in connection with classification of text, the process can be used for classifying any type of input including images, videos, audio signals, and so on).
Regarding dependent claim 18, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the non-transitory, computer-readable storage medium of claim 15.
Nguyen et al further teaches wherein the instructions further cause the system to: redirecting a direction of the traversal through the decision tree by evaluating the document in a previously traversed decision node (Paragraph [0045] discloses, traversing the decision tree. if the confidence score is higher than the threshold).
Sarlos et al also teaches, wherein the instructions further cause the system to: redirecting a direction of the traversal through the decision tree by evaluating the document in a previously traversed decision node (Fig. 4, Paragraph [0037]).
Regarding dependent claim 19, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the non-transitory, computer-readable storage medium of claim 15.
Sarlos et al further teaches, wherein the evaluation of each document at a particular ML model-based node uses outputs of a plurality of ML models, wherein the node classification of the particular ML model-based node is assigned using a combined confidence level of the plurality of ML models, the combined confidence level determined by: assigning a weight to each of the plurality of ML models, calculating the combined confidence level in accordance with the weights and corresponding outputs of the plurality of ML models (Fig. 4, Paragraph The final rank value might be based on any combination (e.g. sum, majority, etc.) of the output of multiple trees or rules. Lastly, the search results are ordered according to their rank value to produce a ranked list of search results. Also see Paragraph [0039]).
Regarding dependent claim 21, Nguyen et al, Sarlos et al, Zohrevand et al and HETHERINGTON et al teach, the computer-implemented method of claim 1.
Zohrevand et al further teaches, Wherein the one or more ML model-based nodes are pre-trained prior to receiving the unclassified set of documents, and wherein the node classification and the probability are generated during runtime classification of the unclassified set of documents (Paragraphs [0164]-[0166]) discloses, the models are pretrained and the classification and the probabilities are generated).
HETHERINGTON et al also further teaches, Wherein the one or more ML model-based nodes are pre-trained prior to receiving the unclassified set of documents (Paragraph [0027] discloses, node are pretrained to receive unclassified documents such as an image, a record, or other detailed object), and wherein the node classification and the probability are generated during runtime classification of the unclassified set of documents (Figs 1 and 5 shows node classification and probability are generated).
6. Claims 11 and 20 is rejected under 35 U.S.C. 103 as being unpatentable over Nguyen; Christopher (US 20230359942 A1) in view of Sarlos; Tamas (US 20090210470 A1), Zohrevand; Zahra (US 20220309360 A1) and in further view of GHALYAN; Najah (US 20240378444 A1).
Regarding dependent claim 11, Nguyen et al, Sarlos et al and Zohrevand et al teach, the system of claim 8.
Nguyen et al further teaches, wherein the unclassified set of documents includes one or more of: structured metadata or unstructured content for each document (Paragraph [0034] The K-Translator is a tool that uses natural language processing to extract useful domain knowledge from conversational text and translate that knowledge into a form that can then be used to build both logical and K1st models in a semi-automated fashion. This form is a knowledge language, a domain-specific language (DSL) for capturing, storing and managing expert knowledge. The knowledge language may also be referred to herein as a rules language),
Nguyen et al, Sarlos et al and Zohrevand et al fails to explicitly teach, wherein the evaluation of each document at each ML model-based node is determined based on the confidence level satisfying corresponding evaluation thresholds at the ML model-based node, further comprising: dynamically adjust the evaluation threshold of at least one ML model-based node based on one or more of: the structured metadata or the unstructured content associated with each document, wherein the adjustment improves classification accuracy of the proposed classification for the document.
GHALYAN; Najah (US 20240378444 A1) teaches, wherein the evaluation of each document at each ML model-based node is determined based on the confidence level satisfying corresponding evaluation thresholds at the ML model-based node, further comprising: dynamically adjust the evaluation threshold of at least one ML model-based node based on one or more of: the structured metadata or the unstructured content associated with each document, wherein the adjustment improves classification accuracy of the proposed classification for the document (Paragraph [0051] discloses the threshold increases as the points tend to cluster and decreases as the points tend to separate. Examiner interprets the points as categories. Also see Paragraph [0039]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al, Sarlos et al and Zohrevand et al by traversing through the decision tree further comprises: iteratively refining the node classification of each document using a plurality of ML model-based nodes, as taught by GHALYAN et al (Paragraphs [0051]-[0051]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, an unsupervised-learning method for deciding how many shots are needed to fine-tune the model's parameters so that the model fits the given use case without overfitting on the training samples used for the fine-tuning as taught by GHALYAN et al (Paragraphs [0037]-[0038]).
Regarding dependent claim 20, Nguyen et al, Sarlos et al and Zohrevand et al teach, the non-transitory, computer-readable storage medium of claim 15.
Nguyen et al, Sarlos et al and Zohrevand et al fails to explicitly teach, wherein the evaluation of each document at each ML model-based node is determined based on the confidence level satisfying corresponding evaluation thresholds at the ML model-based node, wherein the evaluation threshold of a particular ML model-based node is dynamically adjusted based on a number of categories of the document evaluated by the particular ML model-based node, wherein the evaluation threshold is increased in response to a lower number of evaluated categories, and wherein the evaluation threshold is decreased in response to a higher number of evaluated categories.
GHALYAN; Najah (US 20240378444 A1) teaches, wherein the evaluation of each document at each ML model-based node is determined based on the confidence level satisfying corresponding evaluation thresholds at the ML model-based node, wherein the evaluation threshold of a particular ML model-based node is dynamically adjusted based on a number of categories of the document evaluated by the particular ML model-based node, wherein the evaluation threshold is increased in response to a lower number of evaluated categories, and wherein the evaluation threshold is decreased in response to a higher number of evaluated categories ((Paragraph [0051] discloses the threshold increases as the points tend to cluster and decreases as the points tend to separate. Examiner interprets the points as categories. Also see Paragraph [0039]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Nguyen et al, Sarlos et al and Zohrevand et al by traversing through the decision tree further comprises: iteratively refining the node classification of each document using a plurality of ML model-based nodes, as taught by GHALYAN et al (Paragraphs [0051]-[0051]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, an unsupervised-learning method for deciding how many shots are needed to fine-tune the model's parameters so that the model fits the given use case without overfitting on the training samples used for the fine-tuning as taught by GHALYAN et al (Paragraphs [0037]-[0038]).
Closest Prior Art
7. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Lackey; Ronald Jay (US 20240265065 A) teaches, [0058] There is provided a method for classifying a product into a tariff classification, the tariff classification being represented by a node in a tree of nodes, each node being associated with a text string indicative of a semantic description of that node as a sub-class of a parent of that node, the method comprising: [0059] iteratively classifying, at one of the nodes of the tree, the product into one of multiple child nodes of that node; [0060] wherein the classifying comprises: [0061] determining a set of features of the product that are discriminative for that node by extracting the features from the text string indicative of a semantic description of that node; and [0062] determining a feature value for each feature of the product by extracting the feature value from a product characterisation, and [0063] evaluating a decision model of that node for the determined feature values, the decision model being defined in terms of the extracted feature for that node.
Muras; Brian R (US 20220035728 A1) teaches, (Abstract) A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing. Information pertaining to validated or invalidated hypotheses may be provided to a learning engine to maximize reasoning and performance in subsequent discovery processes by adjusting models, vocabularies, dictionaries, parameters utilized by the system in identifying the relationships, concepts, and groupings.
8. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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