CTFR 17/886,440 CTFR 97084 DETAILED ACTION Notice of AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment 2. Amendment filed 03/30/2026 has been considered by Examiner. Claims 1, 11, and 18 have been amended. Claims 1-20 are pending, and likewise Claims 1- 20 have been examined. Response to Arguments Applicant’s amendments and arguments filed 03/30/2026, with respect to claim(s) 1-20 have been fully considered. Applicant’s arguments in pages 8-15, filed 03/30/2026, with respect to 35 U.S.C 101 rejections of Claims 1-20 have been fully considered but they are not persuasive. Applicant argued that the claims, as amended herein, recite numerous features that simply cannot be performed in the human mind and each elements recited in claim 1 is directed towards a computing system that improves inaccuracy that is commonly encountered when employing a conventional text classification system. Through the recited features, the underlying technology (e.g., computer-implemented models configured to perform text classification) is modified and improved, resulting in a tangible improvement to the technology or technical field. Applicant further argued that recited additional elements demonstrating that the claim as a whole integrates the exception into a practical application, thereof when the claimed invention improves the functioning of a computer or improves another technology or technical field. Examiner respectfully disagrees. The method of identifying sets of words or individual words at the same time, assigning labels related to topic, updating the index/identifier, still can be done by pen and paper. A person can assign labels to first and second tokens collectively and concurrently. The additional elements, “processor”, “memory”, “computer readable storage medium” , “computer implemented model”, for performing the method are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Applicant used “trained computer implemented model”, but how the model is trained, what unique features of training is causing the improvement, are missing. The specification in para.[0023], [0026], [0065], specifies that the computer implemented model can be a deep neural network such as RNN, CNN, DNN that includes BERT, binary classifier, which are not sufficient to amount to significantly more than the judicial exception. Other than mentioning “trained computer implemented model”, independent claims didn’t specify any specialized or unique technology. The use of a computer does not preclude performance of the invention via pen and paper or in a person’s mind. Also the claims didn’t disclose why the method is being conducted, what system is getting improved and how. No where in the claims it was mentioned that the method or system is directed to improving inaccuracy that is commonly encountered when employing a text classification system or how the improvement was made. Applicant mentioned that the specification has all the details but the claims and only the claims define the metes and bounds of the invention. Claims should reflect the assertions made by the applicant with respect to the improvements. The use of a computer or other machinery in its ordinary capacity to perform a task or simply adding a general purpose computer to an abstract idea, does not integrate a judicial exception into a practical application. Here the computer is the machine that is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. Thus, 35 U.S.C 101 rejections of Claims 1-20 have been maintained. Applicant’s arguments filed 03/30/2026, with respect to amended claim(s) 1-20, under 35 U.S.C. 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the rejections below. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. The Independent claim(s) 1 and 18 recite(s) “A computing system comprising: a processor”; “and memory storing instructions that, when executed by the processor, cause the processor to perform acts comprising: providing tokens as input to a computer-implemented model, wherein the tokens are representative of a sequence of words, and further wherein the computer-implemented model is configured to perform text classification and has been trained to concurrently assign labels to a sequence of tokens and one or more individual tokens of the sequence of tokens, wherein the labels are indicative of the presence of a word or sub-word that pertains to a topic for which the computer implemented model has been trained to identify”; “obtaining, responsive to the input being provided to the computer-implemented model: a first label assigned to a token within the tokens by the computer-implemented model, wherein the first label indicates that a word represented by the token pertains to the topic”; “and a second label assigned collectively to the tokens by the computer-implemented model, wherein the second label indicates that the sequence of words represented by the tokens collectively pertains to the topic”; “wherein the first label and the second label are concurrently assigned by the computer- implemented model” ; “and updating a computer-implemented index based upon the first label and the second label such that the word and the sequence of words are identified in the computer-implemented index as pertaining to the topic”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper. The limitation of " providing ... ", "obtaining ... ", "updating ... ", as drafted covers mental activities. The steps of providing tokens which are representative of a sequence of words that pertains to a certain topic in a model could be done by hand using pen and paper. Similarly, assigning labels to the tokens based on topics is another example of an observation and evaluation that could be performed in the human mind or with the aid of pencil and paper. A human can assign labels to both the words and the sentences based on the topic, at the same time and with training he/she can perform the task more accurately. Additionally, determining and updating index based on the labels could be performed in the human mind or with the aid of pencil and paper. The claims recite the additional limitation of a “processor”, “memory”, “computer readable storage medium” , “computer implemented model”, for performing the method. “Processor”, “memory”, “computer readable storage medium” are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. The current specification in paragraphs [0026],[0036],[0040], [0055] clearly specifies them as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. “ Computer implemented model” as specified in para.[0023], [0026], [0065] of the specification can be a deep neural network such as RNN, CNN, DNN that includes BERT, binary classifier, which are not sufficient to amount to significantly more than the judicial exception. The claims as drafted, are not paten eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claims 1 and 18 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more than the abstract idea. The Independent claim(s) 11 recite(s), “A method performed by a computing system, the method comprising: providing a sequence of tokens as input to a computer-implemented deep neural network , wherein the sequence of tokens is representative of a sentence extracted from text of an electronic document, and further wherein the computer- implemented deep neural network is configured to perform text classification and has been trained to concurrently assign labels to a sequence of tokens and one or more individual tokens of the sequence of tokens, wherein the labels are indicative of the presence of a word or sub-word that pertains to a topic for which the computer implemented model has been trained to identify”; “obtaining, responsive to the input being provided to the computer-implemented deep neural network: a first label for a token in the sequence of tokens, wherein the first label indicates that a word represented by the token pertains to the topic”; “and a second label for the sequence of tokens, wherein the second label indicates that the sentence pertains to the topic”; “ wherein the first label and the second label are concurrently assigned by the computer- implemented model”; “and in a computer-implemented database, mapping the word to the topic based upon at least one of the first label or the second label”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper. The limitation of " providing ... ", "obtaining ... ", "updating ... ", as drafted covers mental activities. The steps of providing tokens which are representative of a sequence of words that pertains to a certain topic in a model could be done by hand using pen and paper. Similarly, assigning labels to the tokens based on topics is another example of an observation and evaluation that could be performed in the human mind or with the aid of pencil and paper. A human can assign labels to both the words and the sentences based on the topic, at the same time and with training he/she can perform the task more accurately. Additionally, determining and updating index based on the labels could be performed in the human mind or with the aid of pencil and paper. The claims recite the additional limitation of a “computer implemented deep neural network”, for performing the method. “ Computer implemented deep neural network” as specified in para.[0023], [0026], [0072],[0077], recited as generic computer, which is not sufficient to amount to significantly more than the judicial exception. The claim as drafted, is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 11 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more than the abstract idea. Claim 2 recites the additional limitations of “the acts further comprising: obtaining the sequence of words; and generating the set of tokens based upon the sequence of words” which are evaluation or judgment steps that could be performed in the human mind or with the aid of pencil and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 2 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 3 recites the additional limitations of “the acts further comprising: extracting text from an electronic document; identifying boundaries of a sentence in the text, wherein the sentence is the sequence of words.” which are evaluation or judgment steps that could be performed in the human mind or with the aid of pencil and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 3 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 4 recites the additional limitations of “wherein the computer-implemented model is a binary classifier”, where binary classifier is an additional elements, that are not sufficient to amount to significantly more than the judicial exception, as claim 4 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claims 5 and 13 recite the additional limitations of “wherein the first label indicates that the word belongs to a predefined category and wherein the second label indicates that the sequence of words includes at least one word that belongs to the predefined category” , which are data representation steps that amount to merely adding insignificant extra-solution activity to a judicial exception which do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 5 and 13 do not recite any additional limitations. The claims as drafted, are not patent eligible. Claims 6, 14 and 20 recite the additional limitations of “wherein the first label indicates that the word represents a hydrocarbon indicator and the second label indicates that the sequence of words comprises a hydrocarbon indicator” , which are data representation steps that amount to merely adding insignificant extra-solution activity to a judicial exception which do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 6, 14 and 20 do not recite any additional limitations. The claims as drafted, are not patent eligible. Claims 7 and 17 recite the additional limitations of “the acts further comprising: subsequent to updating the computer-implemented index, receiving a query, wherein the query identifies the topic”; “wherein the computer-implemented index is searched based on the query”; “and returning at least one of the word or the sequence of words based upon the query” , which are data representation steps that amount to merely adding insignificant extra-solution activity to a judicial exception which do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 7 and 17 do not recite any additional limitations. The claims as drafted, are not patent eligible. Claim 8 recites the additional limitations of “wherein the computer-implemented model is a deep neural network that comprises bidirectional transformer encoders ” , where “deep neural network” and “bidirectional transformer encoders” are additional elements, that are not sufficient to amount to significantly more than the judicial exception, as claim 8 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 9 recites the additional limitations of “wherein the sequence of words is a paragraph that includes a sentence, the acts further comprising: obtaining, from the computer-implemented model, a third label assigned to a subset of the tokens that represents the sentence in the paragraph, wherein the third label indicates that the sentence represented by the subset of the tokens pertains to the topic ” , which are evaluation or judgment steps that could be performed in the human mind or with the aid of pencil and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 9 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 10 recites the additional limitations of “the acts further comprising: obtaining training data, wherein the training data comprises a second sequence of words, and further wherein a second word in the second sequence of words has a third label assigned thereto that indicates that the second word pertains to the topic”; “based upon the third label being assigned to the second word, updating the training data to include a fourth label that is assigned to the second sequence of words, wherein the fourth label indicates that the second sequence of words pertains to the topic”; “and subsequent to updating the training data, training the computer-implemented model based upon the training data such that the computer-implemented model is configured to jointly identify: words that pertain to the topic”; “and sequences of words that pertain to the topic” , which are evaluation or judgment steps that could be performed in the human mind or with the aid of pencil and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 10 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 12 recites the additional limitations of “further comprising mapping the sentence to the topic based upon the second label” , which are evaluation or judgment steps that could be performed in the human mind or with the aid of pencil and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 12 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 15 recites the additional limitations of “wherein the sentence belongs to a paragraph extracted from the text of the electronic document, the method further comprising: providing a super sequence of tokens as input to the computer-implemented neural network, wherein the super sequence of tokens includes the sequence of tokens; and obtaining a third label for the super sequence of tokens from the computer- implemented neural network, wherein the third label indicates that the paragraph pertains to the topic” , which are data representation steps that amount to merely adding insignificant extra-solution activity to a judicial exception which do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 15 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 16 recites the additional limitations of “wherein the computer-implemented deep neural network is a language transformer model” , where “deep neural network” , “language transformer model” are additional elements, which are not sufficient to amount to significantly more than the judicial exception, as claim 16 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim 19 recites the additional limitations of “wherein the sequence of words is a paragraph extracted from a webpage” , which are evaluation or judgment steps that could be performed in the human mind or with the aid of pencil and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 19 does not recite any additional limitations. The claim as drafted, is not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 1, 2, 5, 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. ( US 10965812 B1), hereinafter referenced as Das, in view of Roman et al. (US 20140324894 A1 ), hereinafter referenced as Roman, further in view of Tolman et al. (US 20180032606 A1 ), hereinafter referenced as Tolman . Regarding Claim 1, Das teaches a computing system comprising: a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform acts comprising (Das: Column 6, lines 4-12, the system comprises a server computing device 106 having a memory for storing computer-executable instructions and a processor that executes the computer executable instructions to perform functions for analysis and classification of unstructured computer text for generation of a recommended conversation topic flow): providing tokens as input to a computer-implemented model, wherein the tokens are representative of a sequence of words ( Das: Column 15, lines 40-48, Fig. 2B, column 9, lines 17-23, Fig.1, computing device 106 receives the bitstream corresponding to a current voice call between a user of a first client computing device 102 and an agent of a second client computing device 108 and converts the bitstream into a transcript of unstructured computer text ( tokens)), and further wherein the computer-implemented model is configured to perform text classification ( Das: Column 5, lines 11-16, Fig. 1, The client computing device 102 connects to the communications network 104 in order to communicate with the server computing device 106 to provide input and receive output relating to the process of analysis and classification of unstructured computer text) and has been trained to concurrently assign labels to a sequence of tokens and one or more individual tokens of the sequence of tokens, wherein the labels are indicative of the presence of a word or sub word that pertains to a topic for which the computer implemented model has been trained to identify ( Das: Column 10, lines 10-15, 45-50, Fig. 3, server computing device 106 executes a topic modelling algorithm such as LDA based topic modelling on the transcript(s) to determine a weighted distribution of topics. The general principle behind the LDA algorithm is that each document can be described by a distribution of topics ( labels for sequence of tokens/words) and each topic can be described by a distribution of words ( labels for words). Column 7, lines 19-49, column 15, lines 23-39, Fig. 2A, illustrates training phase for the server computing device 106 generate a classification for the topic flow); obtaining, responsive to the input being provided to the computer-implemented model: a first label assigned to a token within the tokens by the computer-implemented model, wherein the first label indicates that a word represented by the token pertains to the topic ( Das: Column 12, lines 5-21, column 11, lines 6-13, the module can assign label “ Animals” to a specific grouping of words that includes "dog," "cat," "loyal," "animal," etc., where the topic is animals (first label) ); and a second label assigned collectively to the tokens by the computer-implemented model, wherein the second label indicates that the sequence of words represented by the tokens collectively pertains to the topic (Das: Column 11, lines 31-48, assigning second label to the document. For each document, the module goes through each word, computes: the probability of words in document that are assigned to topic and tries to capture the topic); Das, while teaching the method of claim 1, fails to explicitly teach the claimed, wherein the first label and the second label are concurrently assigned by the computer- implemented model ; and updating a computer-implemented index based upon the first label and the second label such that the word and the sequence of words are identified in the computer-implemented index as pertaining to the topic. However, Roman does teach the claimed, wherein the first label and the second label are concurrently assigned by the computer- implemented model ( Roman: Para.[0057]-[0059], Figs. 1B, 5, a document annotation computer routine is illustrated in fig. 5, which is an implementation of routine D in FIG. 1B. After the document is parsed, correlated, corrected, words and sentences are tagged by the tag words and sentences routine (68) in the formatted document ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Roman’s teaching of methods for producing improved searchable formatted electronic documents , into the system and method for analysis and classification of unstructured computer text for generation of a recommended conversation topic flow , taught by Das, because, this would provide a highly accurate, electronically searchable document and would improve the thoroughness of documents analysis. ( Roman, Para.[0007]-[0014]). Das in view of Roman, while teaching the method of claim 1, fails to explicitly teach the claimed, and updating a computer-implemented index based upon the first label and the second label such that the word and the sequence of words are identified in the computer-implemented index as pertaining to the topic. However, Tolman does teach the claimed, and updating a computer-implemented index based upon the first label and the second label such that the word and the sequence of words are identified in the computer-implemented index as pertaining to the topic ( Tolman: Para.[ 0213], Fig. 12, at 1210, a topic cluster ( computer-implemented index ) is generated based on the key term and the one or more related terms associated with the key term ( first and second label). Para.]0203], Fig. 11, topic cluster manager 1124 can create, modify, and update topic clusters). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tolman’s teaching of recommending topic clusters for unstructured text documents, into the system and method , taught by Das in view of Roman, because, this would improve the organization of electronic text documents (or simply text documents) by intelligently generating recommended topic clusters for a collection of electronic text documents, where the recommended topic clusters are tailored to the collection of electronic text documents. ( Tolman, Para.[0033]). Regarding Claim 2, Das in view of Roman, further in view of Tolman teach the computing system of claim 1.Das further teaches, the acts further comprising: obtaining the sequence of words ( Das: Column 15, lines 40-48, Fig. 2B, computing device 106 receives the bitstream corresponding to a current voice call between a user of a first client computing device 102 and an agent of a second client computing device 108 and converts the bitstream into unstructured computer text ( sequence of words)), and generating the set of tokens based upon the sequence of words ( Das: Column 9, lines 17-23, Fig.1, computing device 106 can further generate tokens from the unstructured text-where each token comprises a word in the overall phrase). Regarding Claim 5, Das in view of Roman, further in view of Tolman teach the computing system of claim 1. Das further teaches, wherein the first label indicates that the word belongs to a predefined category and wherein the second label indicates that the sequence of words includes at least one word that belongs to the predefined category ( Das: Column 10, lines 53-60, the module can be configured to decide on pre-determined number of topics. Column 13, lines 30-35, column 11, lines 11-13, a particular box of text is assigned topic label “retirement income”, where income can be predefined category); Regarding Claim 7, Das in view of Roman, further in view of Tolman teach the computing system of claim 1. Tolman further teaches, the acts further comprising: subsequent to updating the computer-implemented index, receiving a query, wherein the query identifies the topic wherein the computer implemented index is searched based on the query (Tolman: Para.[0203], [0207], Fig. 11, topic cluster manager 1124 create, modify, and update topic clusters ( computer implemented index). Document database 1112 stores topic cluster as tag as metadata and it enables the content management system 1104 or another outside system to query the document database 1112 for text documents based on one or more tags or topic clusters ); and returning at least one of the word or the sequence of words based upon the query (Tolman: Para. [0207], Fig. 11, document database 1112 enables the content management system 1104 or another outside system to generate statistical or other reports based on tags/topic clusters associated with text documents based on the query ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tolman’s teaching of recommending topic clusters for unstructured text documents, into the system and method, taught by Das in view of Roman, because, this would improve the organization of electronic text documents (or simply text documents) by intelligently generating recommended topic clusters for a collection of electronic text documents, where the recommended topic clusters are tailored to the collection of electronic text documents. ( Tolman, Para.[0033]). Claim 18 is computer readable storage medium comprising instructions that, when executed by a processor, cause the processor (Das: Column 18, lines 14-42, Computer- readable storage mediums suitable for embodying computer program instructions, processors suitable for the execution of a computer program), performing the steps in system claim 1 above and as such, claim 18 is similar in scope and content to claim 1 and therefore, claim 18 is rejected under similar rationale as presented against claim 1 above . 07-21-aia AIA Claim s 3, 4, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. ( US 10965812 B1), hereinafter referenced as Das, in view of Roman et al. (US 20140324894 A1 ), hereinafter referenced as Roman, further in view of Tolman et al. (US 20180032606 A1 ), hereinafter referenced as Tolman, further in view of Kanagovi et al. ( US 20230116515 A1), hereinafter referenced as Kanagovi . Regarding Claim 3, Das in view of Roman, further in view of Tolman teach the computing system of claim 2. Das in view of Roman, further in view of Tolman fail to explicitly teach the claimed, the acts further comprising: extracting text from an electronic document; identifying boundaries of a sentence in the text, wherein the sentence is the sequence of words. However, Kanagovi does teach the claimed, the acts further comprising: extracting text from an electronic document (Kanagovi: Para.[0037], Fig. 3, parsing documents from web accessible document source 106 ( electronic documents) ); identifying boundaries of a sentence in the text, wherein the sentence is the sequence of words ( Kanagovi: Para.[0073], Fig. 4, at step 405, identifying boundaries by designated delimiters). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kanagovi’s teaching of determining named entities associated with aspect terms extracted from documents having unstructured text data, into the system and method, taught by Das in view of Roman, further in view of Tolman, because, this would improve the accuracy of the processing of unstructured text data. ( Kanagovi, Para.[0002]-[0004],[0104]). Regarding Claim 4, Das in view of Roman, further in view of Tolman teach the computing system of claim 1. Das in view of Roman, further in view of Tolman fail to explicitly teach the claimed, wherein the computer-implemented model is a binary classifier. However, Kanagovi does teach the claimed, wherein the computer-implemented model is a binary classifier ( Kanagovi: Para.[0132], para.[0145], binary classifier). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kanagovi’s teaching of determining named entities associated with aspect terms extracted from documents having unstructured text data, into the system and method, taught by Das in view of Roman, further in view of Tolman, because, this would improve the accuracy of the processing of unstructured text data. ( Kanagovi, Para.[0002]-[0004],[0104]). Regarding Claim 19, Das in view of Roman, further in view of Tolman teach the computer-readable storage medium of claim 18. Das in view of Roman, further in view of Tolman fail to explicitly teach the claimed, wherein the sequence of words is a paragraph extracted from a webpage. However, Kanagovi does teach the claimed, wherein the sequence of words is a paragraph extracted from a webpage (Kanagovi: Para.[0037], Fig. 3, parsing documents from webpage at 301); Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kanagovi’s teaching of determining named entities associated with aspect terms extracted from documents having unstructured text data, into the system and method, taught by Das in view of Roman, further in view of Tolman, because, this would improve the accuracy of the processing of unstructured text data. ( Kanagovi, Para.[0002]-[0004],[0104]) . 07-21-aia AIA Claim s 6, 8, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. ( US 10965812 B1), hereinafter referenced as Das, in view of in view of Roman et al. (US 20140324894 A1 ), hereinafter referenced as Roman, further in view of Tolman et al. (US 20180032606 A1 ), hereinafter referenced as Tolman, further in view of Xu et al. (US 2023/0176242 A1), hereinafter referenced as Xu . Regarding Claim 6, Das in view of Roman, further in view of Tolman teach the computing system of claim 1. Das in view of Roman, further in view of Tolman fail to explicitly teach the claimed, wherein the first label indicates that the word represents a hydrocarbon indicator and the second label indicates that the sequence of words comprises a hydrocarbon indicator. However, Xu does teach the claimed, wherein the first label indicates that the word represents a hydrocarbon indicator and the second label indicates that the sequence of words comprises a hydrocarbon indicator ( Xu: Para.[0025], para.[0059], Fig. 7 illustrates the flowchart of information generation of geophysical data information indicative of geo features such as hydrocarbon indicator). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Xu’s teaching of computer-implemented method for analyzing geophysical data, into the system and method , taught by Das in view of Roman, further in view of Tolman, because, this would improve the accuracy of detecting, locating, identifying, modelling, and/or quantifying subsurface structures and likelihood of hydrocarbon occurrence. ( Xu, Para.[0003]-[0006]). Regarding Claim 8, Das in view of Roman, further in view of Tolman teach the computing system of claim 1. Das in view of Roman, further in view of Tolman fail to explicitly teach, wherein the computer-implemented model is a deep neural network that comprises bidirectional transformer encoders. However, Xu does teach the claimed, wherein the computer-implemented model is a deep neural network that comprises bidirectional transformer encoders ( Xu: Para.[0042], Fig. 1, para.[0053], Fig. 3A, deep NN may comprises BERT (Bidirectional Encoder Representation from Transformers)). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Xu’s teaching of computer-implemented method for analyzing geophysical data, into the system and method, taught by Das in view of Roman, further in view of Tolman, because, this would improve the accuracy of detecting, locating, identifying, modelling, and/or quantifying subsurface structures and likelihood of hydrocarbon occurrence. ( Xu, Para.[0003]-[0006]). Claim 14 is a method claim performing the steps in system claim 6 above and as such, claim 14 is similar in scope and content to claim 6 and therefore, claim 14 is rejected under similar rationale as presented against claim 6 above. Claim 20 is computer readable storage medium claim performing the steps in system claim 6 above and as such, claim 20 is similar in scope and content to claim 6 and therefore, claim 20 is rejected under similar rationale as presented against claim 1 above . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Das et al. ( US 10965812 B1), hereinafter referenced as Das, in view of Roman et al. (US 20140324894 A1 ), hereinafter referenced as Roman, further in view of Tolman et al. (US 20180032606 A1 ), hereinafter referenced as Tolman, further in view of Wu et al. ( US 20230102892 A1), hereinafter referenced as Wu . Regarding Claim 9, Das in view of Roman, further in view of Tolman teach the computing system of claim 1. Das in view of Roman, further in view of Tolman fail to explicitly teach the claimed, wherein the sequence of words is a paragraph that includes a sentence, the acts further comprising: obtaining, from the computer-implemented model, a third label assigned to a subset of the tokens that represents the sentence in the paragraph, wherein the third label indicates that the sentence represented by the subset of the tokens pertains to the topic. However, Wu does teach the claimed, wherein the sequence of words is a paragraph that includes a sentence, the acts further comprising: obtaining, from the computer-implemented model, a third label assigned to a subset of the tokens that represents the sentence in the paragraph, wherein the third label indicates that the sentence represented by the subset of the tokens pertains to the topic ( Wu: Para.[0031], Fig. 2, for the sentence, “I was a member of the conservative party” labelling function creation module 220 applies label to the sentence as “political” , which represents the topic ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wu’s teaching of methods of annotating text data and more particularly determining text data categories for text data in freeform or unknown formats, into the system and method for analysis and classification of unstructured computer text for generation of a recommended conversation topic flow , taught by Das in view of Roman, further in view of Tolman, because, efficient annotating or labelling of text data from numerous electronic sources would improve the computer system functionality. ( Wu, Para.[0002]) . 07-21-aia AIA Claim s 11-13, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. ( US 10965812 B1), hereinafter referenced as Das, in view of Wu et al. ( US 20230102892 A1), hereinafter referenced as Wu, further in view of Xu et al. (US 2023/0176242 A1), hereinafter referenced as Xu, in view of Roman et al. (US 20140324894 A1 ), hereinafter referenced as Roman . Regarding Claim 11, Das teaches a method performed by a computing system, the method comprising: providing a sequence of tokens as input to a computer-implemented [deep neural] network, wherein the sequence of tokens is representative of a sentence extracted from text of [an electronic] document ( Das: Column 15, lines 40-48, Fig. 2B, column 9, lines 17-23, Fig.1, computing device 106 receives the bitstream corresponding to a current voice call between a user of a first client computing device 102 and an agent of a second client computing device 108 and converts the bitstream into a transcript of unstructured computer text ( tokens). The transcript is consists of multiple sentences), and further wherein the computer- implemented [deep neural] network is configured to perform text classification ( Das: Column 5, lines 11-16, Fig. 1, The client computing device 102 connects to the communications network 104 in order to communicate with the server computing device 106 to provide input and receive output relating to the process of analysis and classification of unstructured computer text), and has been trained to concurrently assign labels to a sequence of tokens and one or more individual tokens of the sequence of tokens, wherein the labels are indicative of the presence of a word or sub word that pertains to a topic for which the computer implemented model has been trained to identify ( Das: Column 10, lines 10-15, 45-50, Fig. 3, server computing device 106 executes a topic modelling algorithm such as LDA based topic modelling on the transcript(s) to determine a weighted distribution of topics. The general principle behind the LDA algorithm is that each document can be described by a distribution of topics ( labels for sequence of tokens/words) and each topic can be described by a distribution of words ( labels for words). Column 7, lines 19-49, column 15, lines 23-39, Fig. 2A, illustrates training phase for the server computing device 106 generate a classification for the topic flow); Obtaining, responsive to the input being provided to the computer-implemented [deep neural ] network: a first label for a token in the sequence of tokens, wherein the first label indicates that a word represented by the token pertains to the topic ( Das: Column 12, lines 5-21, column 11, lines 6-13, the module can assign label “ Animals” to a specific grouping of words that includes "dog," "cat," "loyal," "animal," etc., where the topic is animals (first label) ); and a second label for the sequence of tokens, wherein the second label indicates that the sentence pertains to the topic (Das: Column 11, lines 31-48, assigning second label to the document. For each document, the module goes through each word, computes: the probability of words in document that are assigned to topic and tries to capture the topic); and in a computer-implemented database, mapping the word to the topic based upon at least one of the first label or the second label (Das: Column 3, lines 60-64, mapping the selected plurality of words to one or more topics in a database). Das while teaching the method of claim 11, fails to explicitly teach the claimed, sentence extracted from text of an electronic document; deep neural network; wherein the first label and the second label are concurrently assigned by the computer- implemented model. However, Wu does teach the claimed, sentence extracted from text of an electronic document ( Wu: Para.[0023], a dataset of sentences may be accessed from Wikipedia or another online encyclopedic database to generate the dataset of unlabeled text data). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wu’s teaching of methods of annotating text data and more particularly determining text data categories for text data in freeform or unknown formats, into the system and method for analysis and classification of unstructured computer text for generation of a recommended conversation topic flow , taught by Das, because, efficient annotating or labelling of text data from numerous electronic sources would improve the computer system functionality. ( Wu, Para.[0002]). Das in view of Wu while teaching the method of claim 11, fail to explicitly teach the claimed, deep neural network; wherein the first label and the second label are concurrently assigned by the computer- implemented model. However, Xu does teach the claimed, deep neural network ( Xu: Para.[0042], Fig. 1, deep neural network 120). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Xu’s teaching of computer-implemented method for analyzing geophysical data, into the system and method , taught by Das in view of Wu, because, this would improve the accuracy of detecting, locating, identifying, modelling, and/or quantifying subsurface structures and likelihood of hydrocarbon occurrence. ( Xu, Para.[0003]-[0006]). Das in view of Wu, further in view of Xu while teaching the method of claim 11, fail to explicitly teach the claimed, deep neural network; wherein the first label and the second label are concurrently assigned by the computer- implemented model. However, Roman does teach the claimed, wherein the first label and the second label are concurrently assigned by the computer- implemented model ( Roman: Para.[0057]-[0059], Figs. 1B, 5, a document annotation computer routine is illustrated in fig. 5, which is an implementation of routine D in FIG. 1B. After the document is parsed, correlated, corrected, words and sentences are tagged by the tag words and sentences routine (68) in the formatted document ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Roman’s teaching of methods for producing improved searchable formatted electronic documents , into the system and method , taught by Das in view of Wu, further in view of Xu, because, this would provide a highly accurate, electronically searchable document and would improve the thoroughness of documents analysis. ( Roman, Para.[0007]-[0014]). Regarding Claim 12, Das in view of Wu, further in view of Xu, further in view of Roman teach the method of claim 11. Das further teaches, further comprising mapping the sentence to the topic based upon the second label (Das: Column 3, lines 60-64, mapping the selected plurality of words to one or more topics in a database). Regarding Claim 13, Das in view of Wu, further in view of Xu, further in view of Roman teach the method of claim 11. Das further teaches, wherein the first label indicates that the word represented by the token belongs to a category, and further wherein the second label indicates that the sentence includes the word that belongs to the category ( Das: Column 11, lines 11-13, “income” represents the first label associated with the Income Generation topic. Column 13, lines 30-35, a particular box of text ( sentences) is assigned topic label “retirement income”, which indicates it includes the word for same category); Regarding Claim 15, Das in view of Wu, further in view of Xu, further in view of Roman teach the method of claim 11. Wu further teaches, wherein the sentence belongs to a paragraph extracted from the text of the electronic document (Wu: Para.[0023], a dataset of sentences may be accessed from Wikipedia or another online encyclopedic database to generate the dataset of unlabeled text data), the method further comprising: providing a super sequence of tokens as input to the computer-implemented neural network, wherein the super sequence of tokens includes the sequence of tokens (Wu: Para.[0039], Fig. 4, unlabeled text data with probabilistic labels ( super sequence tokens) have been input into the transformer-based machine learning algorithm module 410); and obtaining a third label for the super sequence of tokens from the computer- implemented neural network, wherein the third label indicates that the paragraph pertains to the topic (Wu: Para.[0052]-[0054], Fig. 6, generating label for sentences, related to certain category). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wu’s teaching of methods of annotating text data and more particularly determining text data categories for text data in freeform or unknown formats, into the system and method, taught by Das in view of Xu, , further in view of Roman, because, efficient annotating or labelling of text data from numerous electronic sources would improve the computer system functionality. ( Wu, Para.[0002]). Regarding Claim 16, Das in view of Wu, further in view of Xu, further in view of Roman teach the method of claim 11. Xu further teaches, wherein the computer-implemented deep neural network is a language transformer model ( Xu: Para.[0042], Fig. 1, para.[0045], Fig. 2A, deep NN 208 may comprise a natural language processing module or transformer) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Xu’s teaching of computer-implemented method for analyzing geophysical data, into the system and method, taught by Das in view of Wu, further in view of Roman because, this would improve the accuracy of detecting, locating, identifying, modelling, and/or quantifying subsurface structures and likelihood of hydrocarbon occurrence. ( Xu, Para.[0003]-[0006]) . 07-21-aia AIA Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Das et al. ( US 10965812 B1), hereinafter referenced as Das, in view of Wu et al. ( US 20230102892 A1), hereinafter referenced as Wu, further in view of Xu et al. (US 2023/0176242 A1), hereinafter referenced as Xu, in view of Roman et al. (US 20140324894 A1 ), hereinafter referenced as Roman , further in view of Tolman et al. (US 20180032606 A1 ), hereinafter referenced as Tolman . Regarding Claim 17, Das in view of Wu, further in view of Xu, further in view of Roman teach the method of claim 11. Das in view of Wu, further in view of Xu, further in view of Roman fail to teach the claimed, receiving a query from a client computing device that is in network communication with the computing system, wherein the database is searched based on the query, wherein the query identifies the topic; identifying the word in the database based upon the query identifying the topic; and returning the word to the client computing device upon identifying the word. However, Tolman does teach the claimed, receiving a query from a client computing device that is in network communication with the computing system, wherein the database is searched based on the query, wherein the query identifies the topic ( Tolman: Para.[0050], Fig. 1, each device connected via a network 110. Para.[0207], Fig. 11, Document database 1112 stores topic cluster as tag as metadata and it enables the content management system 1104 or another outside system to query the document database 1112 for text documents based on one or more tags or topic clusters ); identifying the word in the database based upon the query identifying the topic; and returning the word to the client computing device upon identifying the word (Tolman: Para. [0207], Fig. 11, document database 1112 enables the content management system 1104 or another outside system to generate statistical or other reports based on tags/topic clusters associated with text documents based on the query). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tolman’s teaching of recommending topic clusters for unstructured text documents, into the system and method, taught by Das in view of Wu, further in view of Xu, further in view of Roman because, this would improve the organization of electronic text documents (or simply text documents) by intelligently generating recommended topic clusters for a collection of electronic text documents, where the recommended topic clusters are tailored to the collection of electronic text documents. ( Tolman, Para.[0033]) . Allowable Subject Matter Claim 10 contain subject matter that is allowable over the prior art of record. Claim 10 would be considered allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The closest prior art of Wang et al., (US 20200312432 A1) discloses a computer architecture for labeling documents is disclosed. According to some aspects, a computer accesses a collection of documents corresponding to a medical encounter and a labeling for the collection, wherein the labeling comprises one or more labels representing medical annotations assigned to the medical encounter. The computer computes, using a Hierarchical Attention Network (HAN), for each of a plurality of document-label pairs, a probability that a document of the document-label pair corresponds to a label of the document-label pair based on one or more features of text in the document, wherein each document-label pair comprises a document from the collection of documents and a label from the labeling. The computer provides an output representing the computed probabilities. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADIRA SULTANA whose telephone number is (571)272-4048. The examiner can normally be reached M-F,7:30 am-5:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D. Shah can be reached on (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NADIRA SULTANA/Examiner, Art Unit 2653 /Paras D Shah/Supervisory Patent Examiner, Art Unit 2653 06/12/2026 Application/Control Number: 17/886,440 Page 2 Art Unit: 2653 Application/Control Number: 17/886,440 Page 3 Art Unit: 2653 Application/Control Number: 17/886,440 Page 4 Art Unit: 2653 Application/Control Number: 17/886,440 Page 5 Art Unit: 2653 Application/Control Number: 17/886,440 Page 6 Art Unit: 2653 Application/Control Number: 17/886,440 Page 7 Art Unit: 2653 Application/Control Number: 17/886,440 Page 8 Art Unit: 2653 Application/Control Number: 17/886,440 Page 9 Art Unit: 2653 Application/Control Number: 17/886,440 Page 10 Art Unit: 2653 Application/Control Number: 17/886,440 Page 11 Art Unit: 2653 Application/Control Number: 17/886,440 Page 12 Art Unit: 2653 Application/Control Number: 17/886,440 Page 13 Art Unit: 2653 Application/Control Number: 17/886,440 Page 14 Art Unit: 2653 Application/Control Number: 17/886,440 Page 15 Art Unit: 2653 Application/Control Number: 17/886,440 Page 17 Art Unit: 2653 Application/Control Number: 17/886,440 Page 18 Art Unit: 2653 Application/Control Number: 17/886,440 Page 19 Art Unit: 2653 Application/Control Number: 17/886,440 Page 20 Art Unit: 2653 Application/Control Number: 17/886,440 Page 21 Art Unit: 2653 Application/Control Number: 17/886,440 Page 22 Art Unit: 2653 Application/Control Number: 17/886,440 Page 23 Art Unit: 2653
Prosecution Insights