DETAILED ACTION
Remarks
Claims 1-20 have been examined and rejected. This Office Action is responsive to the amendment filed on 04/22/2026, which has been entered into in the above identified application.
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 .
Claims 1-20 are presented for examination.
Response to Amendment
Applicant’s amendment filed on 04/22/2026 has been entered. Claims 1, 2, 4, 5, 12-14 and 16-19 are amended. Claims 1-20 are pending in the application.
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.
With respect to claim 1, it is unclear what “the set of fields” [line 12] refers to. Prior to this limitation, the independent claim 1 never recites any set of fields. For the purposes of examination, examiner will interpret the limitation as “a set of fields.”
With respect to claim 12, it is unclear what “the set of fields” [line 13] refers to. Prior to this limitation, the independent claim 12 never recites any set of fields. For the purposes of examination, examiner will interpret the limitation as “a set of fields.”
With respect to claim 16, it is unclear what “the set of fields” [line 15] refers to. Prior to this limitation, the independent claim 16 never recites any set of fields. For the purposes of examination, examiner will interpret the limitation as “a set of fields.”
With respect to claims 2-11, 13-15 and 17-20, they are rejected based on the virtual dependency of claims 1, 12 and 16.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a method, claim 12 is drawn to a non-transitory machine-readable storage medium storing instructions and claim 16 is drawn to an apparatus comprising a processor when executed by the processor, cause the processor to perform the method of claim 1. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1, 12 and 16 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 12 and 16 recite a method of wherein the historical set of cases are vectorized to calculate a score comprising a ratio of a frequency of one or more terms and a log of a ratio of a total number of cases and a number of cases in which the one or more terms are found that under its broadest reasonable interpretation enumerates a mathematical concept. A human can perform the calculation using words or using mathematical symbols/formulas to apply the term frequency-inverse document frequency. Therefore, the step of calculating a score comprising a ratio of a frequency of one or more terms making up the set of fields is nothing more than a mathematical concept (MPEP 2106.04(a)(2)(I)).
Claims 1, 12 and 16 recite a method of in accordance with a determination that the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to evaluate and to make decision to generate a case complexity classification. Therefore, the step of generating a case complexity for the case is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 12 and 16 recite a method of in accordance with a determination that the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to evaluate and to make decision to generate a case complexity classification. Therefore, the step of generating a case complexity for the case is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Claims 1, 12 and 16 recite further a method of training a predictive model with a corpus of training data comprising terms from a historical set of cases that are specific to a technical domain that fails to integrate the abstract idea into a practical application. The step of training a predictive model is a form of insignificant input and output solution activities, where training a predictive model with a corpus of training data is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 12 and 16 recite further a method of acquiring, by a hybrid model that includes the predictive model, a set of rules pertaining to definition of a complexity of a case, the hybrid model communicating with a case intake component of a data center to receive a set of fields corresponding to the case that fails to integrate the abstract idea into a practical application. The step of acquiring a set of rules by a hybrid model is a form of insignificant input and output solution activities, where acquiring a set of rules defining a complexity of the case is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 12 and 16 recite further a method of generating, via the hybrid model, a case complexity classification for the case based on analysis of the set of fields that fails to integrate the abstract idea into a practical application. The step of generating a case complexity classification via the hybrid model is a form of insignificant input and output solution activities, where generating a case complexity classification based on analysis of the set of fields is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 12 and 16 recite further a method of utilizing the case complexity classification, including the calculated score, to route the case for resolution processing that fails to integrate the abstract idea into a practical application. The step of utilizing the case complexity classification is a form of insignificant input and output solution activities, where utilizing the case complexity classification to route the case is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that these additional elements of training a predictive model with a corpus of training data; acquiring a set of rules defining a complexity of the case; generating a case complexity classification based on analysis of the set of fields and utilizing the case complexity classification to route the case to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1, 12 and 16 are not patent eligible.
Dependent claims
Claims 2-11, 13-15 and 17-20 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 1, 12 and 16, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation. Therefore, claims 2-11, 13-15 and 17-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-11 are drawn to a method, claims 13-15 are drawn to a non-transitory machine-readable storage medium storing instructions and claims 17-20 are drawn to an apparatus comprising a processor when executed by the processor, cause the processor to perform the method of claims 2-11. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claims 2, 13 and 17 recite further the mental process by wherein the determination that the set of rules includes one or more respective rules that apply to the case comprises identifying a keyword match between the set of fields and the set of rules, and generating a case complexity classification for the case based on the set of rules without using the predictive model comprises applying an associated rule of the set of rules to generate the case complexity classification for the case those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claims 4 and 18 recite further the mental process by wherein the determination that the set of rules does not include rules that apply to the case comprises failing to identify a keyword match between the set of fields and the set of rules that are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claims 5 and 19 recite further the mental process by wherein generating a case complexity classification for the case based on the predictive model comprises: providing a vectorized representation of the set of fields as input for the predictive model; and outputting, in response to the input of the vectorized representation of the set of fields, the case complexity classification for the case, wherein the vectorized representation of the set of fields comprises term frequency and inverse document frequency (TF-IDF) for a term of the one or more terms, t, in a document, d e document - setD, such that TF-IDF = Tf-idf(t,d,D) = tf (t,d).idf(t,D), where TF = tf (t; d) = log (1 + freq (t; d)), and where IDF= idf(t,D)=log((1+N)/(1+count(de D:te d))) those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 14 recites further the mental process by instructions to repond to failing to identify the keyword match by applying the predictive model to the set of fields, and wherein applying the predictive model comprises: vectorizing the set of fields; providing the vectorized set of fields as input for the predictive model; and outputting, in response to the input of the vectorized set of fields, the case complexity classification for the case those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 3 recites further the insignificant extra solution activities by the keyword match comprises a match of at least one of an error code, an event signature, or a text string. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claims 6, 15 and 20 recite further the insignificant extra solution activities by the predictive model comprises a support vector machine (SVM) with linear kernel and 12 regularization. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 7 recites further the insignificant extra solution activities by the historical set of cases are each labelled with a case complexity by a domain expert. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 8 recites further the insignificant extra solution activities by the set of fields comprise at least a subject field, an issue text field, or a severity field. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 9 recites further the insignificant extra solution activities by the case is managed by a case management system in a technical support environment. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 10 recites further the insignificant extra solution activities by the case complexity classification is to indicate a level of difficulty in resolving a problem of the case. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 11 recites further the insignificant extra solution activities by the set of rules and the predictive model are based on data provided in one or more languages. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 2-11, 13-15 and 17-20 are not patent eligible.
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 1-4, 6-13, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US 20090019171 A1) hereafter Liu, further in view of Quijada et al (US 12039596 B1) hereafter Quijada, further in view of Fontecilla et al (US 11321538 B1) hereafter Fontecilla, further in view of Nunes et al (US 20220114594 A1) hereafter Nunes, and further in view of Hayman et al (US 12229777 B1) hereafter Hayman.
With respect to claim 1, Liu teaches a method (a method for determining a mail class provided that reads a mail head of an email with an unknown class [par. 0008]) comprising:
receive a set of fields corresponding to the case (some fields are included in the mail head of an email, such as From, To, Reply-To, Delivered-To, etc. Part or all of the fields may be selected using the preset condition [par. 0020-0025]);
generating, via the hybrid model, a case complexity classification for the case based on analysis of the set of fields (a behavior model may be used to classify the fields those are vectorized and obtained and stored as parameters in the behavior model. The behavior model may use the mail head to establish a behavior required for determination of a mail class [par. 0026-0030]), and
utilizing the case complexity classification, to route the case for resolution processing (the behavior model may determine the mails to be classified into two classes: junk mails or non-junk mails. The calculation process of the Support Vector Machine (SVM) may classify the mails into two classes labeled with 0 and 1 [par. 0036-0040]).
However, Liu did not disclose training a predictive model with a corpus of training data comprising terms from a historical set of cases that are specific to a technical domain, wherein the historical set of cases are vectorized to calculate a score comprising a ratio of a frequency of one or more terms and a log of a ratio of a total number of cases and a number of cases in which the one or more terms are found; acquiring, by a hybrid model that includes the predictive model, a set of rules pertaining to definition of a complexity of a case, the hybrid model communicating with a case intake component of a data center; generating, including: in accordance with a determination that the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model; and in accordance with a determination that the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model; and utilizing the case complexity classification using the calculated score.
In the same field of endeavor, Quijada teaches calculate a score comprising a ratio of a frequency of one or more terms and a log of a ratio of a total number of cases and a number of cases in which the one or more terms are found (Quijada teaches this exact formula of Term Frequency – Inverse Document Frequency (TF-IDF) as part of its vectorization process. The system analyzes the characteristics of the transaction based on a predetermined set of rules. The system also applies a term weighting to account for term frequencies and/or inverse document frequencies to account for term sequences. Even though Quijada does not teach the historical set of cases are vectorized, the score described is the TF-IDF, a statistical measure used to evaluate the importance of a word within a specific document (or case) relative to a larger corpus [col. 10, line 64 – col. 11, line 30]);
acquiring, by a hybrid model that includes the predictive model, a set of rules pertaining to definition of a complexity of a case, the hybrid model communicating with a case intake component of a data center to receive a set of fields corresponding to the case (The method provided to receive transaction data that is associated with a financial account of a borrower. The transaction data includes one or more data elements representing income transactions those analyzing one or more characteristics, where a characteristic includes a transaction description, an income type label and an income source label. Based on a predetermined set of rules when training one or more computer models, the processor may be developed using transform features as inputs. The transaction analysis used to estimate a net income and to estimate a relationship between net income and growth income those may be based on the historical relationship of the two types of incomes. Processor may apply a term weighting to account for term frequencies and/or inverse document frequencies to account for term sequences [col. 3, line 65 – col. 4, line30; col. 3, lines 5-25; col. 10, line 64 – col. 11, line 30]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of classifying the transaction data based on a certain set of predetermined rules as suggested by Quijada into the concept of classifying mails based on the input fields of the mails as suggested by Liu because both of these systems addressing the process of training one or more algorithm models to classify the inputs and to determine the level of complexity of the inputs. Doing so would be desirable because the system of Liu would be more efficient by incorporating a set of predetermined rules of transaction those associated with the types of income streams to classify the transactions into correct types of income and to generate a gross income distribution (Quijada, [col. 3, line 65 – col. 4, line30]).
However, the combination of Liu and Quijada does not particularly disclose training a predictive model with a corpus of training data comprising terms from a historical set of cases that are specific to a technical domain; the historical set of cases are vectorized; generating, including: in accordance with a determination that the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model; and in accordance with a determination that the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model; and utilizing the case complexity classification, including the calculated score.
In the same field of endeavor, Fontecilla teaches the historical set of cases are vectorized (a feature vector represents each of the first text tokens of at least one sentence, from a document, including the predefined keyword to obtain a first set of textual feature vectors. A similar feature vector represents each of the second text tokens. A text token similarity score between text tokens is determined that uses third natural language processing (NLP) model [col. 1, line 30 – col. 2, line 15; col. 7, lines 10-60]); and
utilizing the case complexity classification including the calculated score (some documents may have low-quality responses based on whether its text token score satisfied a certain threshold condition, wherein a low-quality response may include a specific detail such as regularity constraint, safety, health data, or other information. Such response may be classified as non-compliant. A response document may be scored to identify a level of compliance with the requirements [col. 3, line 60 – col. 4, line 5; col. 4, lines 28-50]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of determining how well a document addresses some requirements set forth in a requirement-specifying document as suggested by Fontecilla into the combination of Liu and Quijada because all of these systems addressing the process of training one or more algorithm models to classify the inputs and to determine the level of complexity of the inputs. Doing so would be desirable because the combination of Liu and Quijada would be more efficient by generating a feature vector for each of the text tokens for the first and second documents, and generating a text token similarity score to classify a document as compliant or non-compliant using the NLP model (Fontecilla, [col. 1, line 30 – col. 2, line 25]).
However, the combination of Liu, Quijada and Fontecilla does not explicitly teach training a predictive model with a corpus of training data comprising terms from a historical set of cases that are specific to a technical domain; and generating, including: in accordance with a determination that the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model; and in accordance with a determination that the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model.
In the same field of endeavor, Nunes teaches training a predictive model with a corpus of training data comprising terms from a historical set of cases that are specific to a technical domain (the voice communication may be a phone call from a user of the customer device and an agent. The feature extraction then extracts user attributes from the voice data. The classification module may then determine an intent of the voice communication based on the user attributes. One or more machine learning-trained classifiers may be selected those correspond to the intents. Different intents have been trained using historic voice data associated with one or more user accounts. The system may classify the customer input into multiple categories with larger datasets of customer input. The labeled dataset can be gathered from customer support agents. The system may utilize different representations including term frequency inverse document frequency [par. 0028, 0144-0152]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of accessing user interaction data associated with an interaction between a first communication device and the service provider server and generating feature representations of users as suggested by Nunes into the combination of Liu, Quijada and Fontecilla because all of these systems addressing the process of training one or more algorithm models to classify the inputs and to determine the level of complexity of the inputs. Doing so would be desirable because the combination of Liu, Quijada and Fontecilla would be more efficient by analyzing and generating different representations based on user’s attributes using term frequency inverse document frequency to select the best performing feature extraction model to extract features from the user inputs (Nunes, [par. 0028]).
However, the combination of Liu, Quijada, Fontecilla and Nunes does not explicitly teach generating, including: in accordance with a determination that the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model; and in accordance with a determination that the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model.
In the same field of endeavor, Hayman teaches generating, including:
in accordance with a determination that the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model (the limitation herein is directed to a particular workflow or architecture using rules first and invoking a predictive model only as a fallback. The limitation is related to the if-then concept with the predictive model. A transaction assessment engine may include a machine learning (ML) classifier and a rule-based classifier used to predict whether a transaction is fraudulent. The rule-based classifier may include one or more “if-then”-style rule that may be manually set or automatically determined. The rule-based classifier may apply these rules to the set of features and may classify the associated transaction as either fraudulent or non-fraudulent. In this case, one or more rules are applied to the features to classify the transactions, and the predictive model is not needed [col. 4, line 25 - col. 5, line 15]); and
in accordance with a determination that the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model (a second fraud indicator is obtained based on the features in step 500 using rule-based classifier that includes one or more rules operating on one or more of the features. If multiple rules are used, they may be structured in sequential order such as in a set of “if-then” rules. These rules may form a decision tree. In an example where the payer is located in a particular geographical region, the likeliness of fraud is high, and therefore a second rule needs to be invoked (or no rules applied), a predictive model may be used. A rule-based classifier is used to predict whether a transaction is fraudulent [col. 4, line 25 - col. 5, line 15; col. 8, lines 45-65; col. 10, line 30 – col. 11, line 20]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of detecting fraudulent financial transactions in IT networks involves obtaining a multitude of features as suggested by Hayman into the combination of Liu, Quijada, Fontecilla and Nunes because all of these systems addressing the process of training one or more algorithm models to classify the inputs and to determine the level of complexity of the inputs. Doing so would be desirable because the combination of Liu, Quijada, Fontecilla and Nunes would be more efficient by using a rule-based classifier to predict the outcome of a transaction with or without using the “if-then”-style rules those may have been manually set or automatically determined (Hayman, [col. 1, line 25 – col. 2, line 10; col. 4, line 25 – col. 5, line 15]).
With respect to claim 2, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the determination that the set of rules includes one or more respective rules that apply to the case comprises identifying a keyword match between the set of fields and the set of rules, and (Quijada, the processor may identify separate words or groups of characters based on the spaces in transaction data and compare them to a predefined set of words and characters such as “deposit” or “payroll”, and the processor may apply a set of predefined rules to parse the information [col. 9, line 60 – col. 10, line 42]), and generating a case complexity classification for the case based on the set of rules without using the predictive model comprises applying an associated rule of the set of rules to generate the case complexity classification for the case (Quijada, the processor may identify a match between words or groups of characters based on the textual pattern matching. The processor may generate a list of words to process the matching through the n-gram levels and generate matching words for each level [col. 9, line 60 – col. 10, line 42]).
With respect to claim 3, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the keyword match comprises a match of at least one of an error code, an event signature, or a text string (Quijada, the processor parses the information from the input transaction data into a list of words (or text strings) to identify a match with a predefined set of words, wherein the parsed words need to go through a predetermined number of n-gram levels [col. 10, lines 15-42]).
With respect to claim 4, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the determination that the set of rules does not include rules that apply to the case comprises failing to identify a keyword match between the set of fields and the set of rules (Quijada, the verification of a borrower may be determined based on the borrower and employment recognizer (BER) including applying a predetermined set of transaction rules. BER may verify the data elements based on detecting a match between one or more characteristics of the data elements. A failed attempt may be based on a determination that a certain text string does not match the text string of a borrower in the database [col. 13, lines 20-65]).
With respect to claim 6, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the predictive model comprises a support vector machine (SVM) with linear kernel and 12 regularization (Liu, the behavior model may be established by Support Vector Machine (SVM). The SVM method may generate a tradeoff between the complexity of the input mails and the behavior model. One of the algorithms of SVM method may construct a linear decision function in the high-dimensional feature space [par. 0029]).
With respect to claim 7, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the historical set of cases are each labelled with a case complexity by a domain expert (Quijada, the one or more characteristics of the one or more data elements may be extracted to generate an income type label and an income source label. The method may identify a cluster of data elements which is associated with a first income source label, wherein the identified cluster may include a plurality of data elements associated with a first income type label [col. 3, line 65 – col. 4, line30]).
With respect to claim 8, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the set of fields comprise at least a subject field, an issue text field, or a severity field (Liu, some of the fields may be common in the mail head of each of the mails: From field, To field, Reply-To field, Delivered-To field, Return-Path field, Date field, etc. [par. 0021]).
With respect to claim 9, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the case is managed by a case management system in a technical support environment (Quijada, an electronic transaction data may be received, processed and managed by a technical support organization, such as a bank [col. 2, line 40 – col. 3, line 5]).
With respect to claim 10, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the case complexity classification is to indicate a level of difficulty in resolving a problem of the case (Quijada, the transaction data may be parsed into different lengths or different n-gram levels to classify into a group of associated words to find the identity of the borrower. For example, if the transaction data information is “John Doe payroll”, the processor may generate a list of words of length three each: “Joh,” “ohn,” “hn_,” “n_D,” “_De,” “Doe,” “e_p,” “_pa,” “pay,” “ayr,” “yro,” “rol,” [col. 10, lines 15-42]).
With respect to claim 11, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein the set of rules and the predictive model are based on data provided in one or more languages (Liu, the behavior model established from the mail head may be useful in determining the mail class regardless of a specific language of the mail body, and that would set a specific set of rules for the determination [par. 0028, 0048, 0060, 0068]).
With respect to claim 12, it is a non-transitory machine-readable claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 13, it is a non-transitory machine-readable claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claimed in claim 2 above.
With respect to claim 15, it is a non-transitory machine-readable claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claimed in claim 6 above.
With respect to claim 16, it is an apparatus claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 17, it is an apparatus claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claimed in claim 2 above.
With respect to claim 18, it is an apparatus claim that corresponding to the method of claim 4. Therefore, it is rejected for the same reason as claimed in claim 4 above.
With respect to claim 20, it is an apparatus claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claimed in claim 6 above.
Claims 5, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US 20090019171 A1) hereafter Liu, further in view of Quijada et al (US 12039596 B1) hereafter Quijada, further in view of Fontecilla et al (US 11321538 B1) hereafter Fontecilla, further in view of Nunes et al (US 20220114594 A1) hereafter Nunes, and further in view of Hayman et al (US 12229777 B1) hereafter Hayman, as claimed in claims 4, 13 and 18 above respectively, and further in view of Choi et al (US 9880994 B1) hereafter Choi.
With respect to claim 5, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches all limitations as claimed in claim 4 above.
The combination of Liu, Quijada, Fontecilla, Nunes and Hayman teaches wherein generating a case complexity classification for the case based on the predictive model comprises:
providing a vectorized representation of the set of fields as input for the predictive model (Liu, a first field extracting unit configured to extract a first field, and a first vectorizing unit configured to vectorize the first field into a first preset number of first feature vectors. A calculating unit configured to input the first feature vectors to a preset predictive algorithm with data stored for a behavior model [par. 0008-0010]); and
outputting, in response to the input of the vectorized representation of the set of fields, the case complexity classification for the case (Liu, the classification process of the mails may use the preset condition of the field extracting unit to output the mail class of each of the mails [par. 0021]).
However, the combination of Liu, Quijada, Fontecilla, Nunes and Hayman does not explicitly teach wherein the vectorized representation of the set of fields comprises term frequency and inverse document frequency (TF-IDF) for a term of the one or more terms, t, in a document, d E document - setD, such that TF-IDF = Tf-idf(t,d,D) = tf (t,d).idf(t,D), where TF = tf (t; d) = log (1 + freq (t; d)), and where IDF = idf(t,D)=log((1+N)/(1+count(de D:te d))).
In the same field of endeavor, Choi teaches wherein the vectorized representation of the set of fields comprises term frequency and inverse document frequency (TF-IDF) for a term of the one or more terms, t, in a document, d E document - setD, such that TF-IDF = Tf-idf(t,d,D) = tf (t,d).idf(t,D), where TF = tf (t; d) = log (1 + freq (t; d)), and where IDF = idf(t,D)=log((1+N)/(1+count(de D:te d))) (TF-IDF may be generated for each of one or more elements in the first page when determining similar pages, wherein a TF-IDF value may be generated by using equation: tfidf(t,d,D)=tf(t,d)x(idf(t,D)). In the other hand, the inverse page frequency idf(t,D) may be generated using the equation: idf(t,D)=log(N/(Math{deD:ted})). The inverse page frequency idf(t,D) may include algorithm base-10 or base-e of a total number of pages with N documents in corpus divided by a number of pages [col. 9, lines 20-60]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of detecting compatible layouts for content-based native ads as suggested by Choi into the combination of Liu, Quijada, Fontecilla, Nunes and Hayman because all of these systems addressing the process of training one or more algorithm models to classify the inputs and to determine the level of complexity of the inputs. Doing so would be desirable because the combination of Liu, Quijada, Fontecilla, Nunes and Hayman would be more efficient by applying the equation of TF-IDF in generating a native advertising content on a page, whereas the sponsored content template may be used to generate from existing and/or otherwise provided advertising a native ad for a destination content page (Choi, [col. 1, lines 15-45, col. 9, lines 20-60]).
With respect to claim 14, it is a non-transitory machine-readable claim that corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claimed in claim 5 above.
With respect to claim 19, it is an apparatus claim that corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claimed in claim 5 above.
Response to Arguments
The examiner respectfully acknowledges the applicant’s amendments to claims 1, 2, 4, 5, 12-14 and 16-19.
Applicant’s amendment field on 04/22/2026 regarding the rejections to claims 1-20 under 35 USC 112(b) have been considered. However, there are new matter regarding the rejections to claims 1-20 under 35 USC 112(b) (see rejection above).
Applicant’s argument filed on 04/22/2026 regarding the rejections to claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argued that “although the claims may arguably recite a mathematical concept, Applicant respectfully submits that the instant claims merely involve an exception and are not directed to the mathematical concept. "[A]n invention is not considered o be ineligible for patenting simply because it involves a judicial exception." MPEP 2106. "That a mathematical equation is required to complete the claimed method and system does not doom the claims to abstraction." Id. citing Thales Visionix Inc. v. United States, 850 F.3d 1343, 1349, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017). While the instant claims may arguably recite a mathematical concept, Applicant respectfully submits that the claims merely involve the mathematical concept and are not directed to the mathematical concept, nor do they preempt the mathematical concept.”
Examiner respectfully disagrees.
Based on what is recited in claim 1, the broadest reasonable interpretation (BRI) in view of Specification of the claim limitation “the historical set of cases are vectorized to calculate a score comprising a ratio of a frequency of one or more terms and a log of a ratio of a total number of cases and a number of cases in which the one or more terms are found” merely recites mathematical relationships and calculations (TF-IDF type calculation) which falls within the mathematical concepts grouping, and the claim limitations “the set of rules includes one or more respective rules that apply to the case, generating a case complexity classification for the case based on the set of rules without using the predictive model” and “the set of rules does not include rules that apply to the case, generating a case complexity classification for the case based on the predictive model” recites evaluation and decision-making steps that could be characterized as mental processes. These limitations involve evaluating information and making a decision based on the evaluation. Such evaluations and judgments may fall within the mental processes grouping, even when performed on a computer. Accordingly, claim 1 recites one or more judicial exceptions under step 2A Prong 1.
Applicant argued that “Applicant notes that the specification gives context as to the limitations and flaws of a conventional technical support environment in which "all case arrive at a lower skill level of support (e.g., a 'Level 1') and then move up ('escalate') to higher skill levels of support (e.g., 'Level 2', 'Level 3', and so on) based on the complexity of the issue in the case." Specification at [0010]. The Specification further notes that "if case complexity could be identified early on in case intake, [] high complexity cases could be moved to the appropriate levels at the outset, skipping the intermediate level and, thus, saving time and improving efficiency." Id. at [0012]
…
First, automated routing of a case to the appropriate destination based on complexity improves on existing solutions by saving time and improving efficiency (e.g., by avoiding sending every case to a lower level, then escalating to a next level, then escalating to a next level), as noted above. Furthermore, automatically generating a case complexity classification using a set of rules without using a predictive model when an appropriate rule is found saves valuable computing resources by avoiding use of the predictive model when possible; while automatically applying the predictive model when an appropriate rule is not found ensures reliable routing of a case to the appropriate resources based on complexity.”
Examiner respectfully disagrees.
Although the Specification describes the asserted technical features, those features are not reflected in the language of claim 1. Applicant’s argument is not persuasive because the cited features are disclosed in the Specification but are not recited in claim 1. The Specification does not import limitations into the claims for purposes of the eligibility analysis.
The additional elements that would include the technological components that implement the mathematical concept and mental processes discussed in step 2A Prong 1 including: a predictive model, a hybrid model, a case intake component, a data center, a set of fields corresponding to the case (simply data gathering), and a corpus of historical cases to train the predictive model. These additional elements are recited at a high level of generality as tools for collecting, analyzing and processing information. The claim does not recite any improvement to the operation of a computer, the predictive model, the hybrid model, the case intake component and/or the data center. Nor does it recite a particular machine integral to the claim, effect a transformation of an article, or otherwise apply the judicial exception in a manner that imposes a meaningful limit on the exception. Instead, the additional elements merely use generic computer components to implement the abstract idea of classifying and routing cases. Accordingly, the judicial exception is not integrated into a practical application.
Therefore, amended claim 1 and its corresponding claims 12 and 16 are not patent eligible for at least the reasons discussed above. Dependent claims 2-11, 13-15 and 17-20, those directly or indirectly depended on claims 1, 12 and 16, are not patent eligible for the same reasons as discussed above.
Applicant’s argument filed on 04/22/2026 regarding the rejections to claims 1-20 under 35 U.S.C. 103 have been fully considered and moot in view of the new ground of rejection (see rejection above).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP 706.07(a). Applicant is remined 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 filled 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 extension fee 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 date of this final action.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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