DETAILED ACTION
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 .
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. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 1.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a method, which is a process.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“generating a label dictionary, the generating comprising performing natural language processing on the raw training data using an automatic engine”; (Mental processes- concept of observation and evaluation of labeling datasets).
“generating tagged data from the raw training data using the label dictionary”; (Mental processes- concept of observation and evaluation of labeling datasets).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application.
“inputting raw training data into the entity resolution model, the raw training data including historical transaction data comprising a plurality of transactions;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“scanning text of each transaction;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“extracting one or more entities from the text;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“storing the label dictionary in a database, the label dictionary including the one or more entities;” involves storing and retrieving data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“training the entity resolution model using a transformer model, the tokenized text in the transformer model specific format, and the tagged data”, “performing vocabulary training on the raw training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
“storing the trained entity resolution model in a database”: involves storing and retrieving data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, 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. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2.
Step 1: A method, as above.
Step 2A Prong 2, Step 2B: The claim recites that “receiving the raw training data from a data source”: involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 3.
Step 1: A method, as above.
Step 2A Prong 2, Step 2B: The claim recites that “receiving the raw training data comprises one or more of the following: retrieving the raw training data from the database and receiving the raw training data from one or more data source computing devices”: involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 4.
Step 1: A method, as above.
Step 2A Prong 2, Step 2B: The claim recites that “performing the natural language processing on the raw training data comprises using Python-based Natural Language Toolkit (NLTK)”: involves Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 5.
Step 1: A method, as above.
Step 2A Prong 2, Step 2B: The claim recites that “extracting the one or more entities from the text comprises performing the following processes on the text of each transaction: word tokenization, word lemmatization, stop word removal, punctuation removal, and accented character and word conversion”: involves Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 6.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “performing the word tokenization process comprises splitting the text into individual words and defining a token for each word”: This limitation merely specifies mental processes- concept of observation and evaluation of labeling datasets.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 7.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “generating tagged data from the raw training data using the label dictionary comprises performing name entity recognition (NER) tagging on each transaction, including labelling each respective transaction with the one or more entities identified in the respective transaction.”: This limitation merely specifies mental processes- concept of observation and evaluation of labeling datasets and filtering the labeling between positive and negative sampling based on relevancy.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 8.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “tokenizing the text of each transaction of the raw training data includes performing subword-based tokenization on the raw training data”: This limitation merely specifies mental processes- concept of observation and evaluation of labeling datasets.
Step 2A Prong 2, Step 2B: The claim recites that “obtaining a set of the potential queries;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 9.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “performing the subword-based tokenization on the processed training data comprises: initializing a word unit inventory with all the characters in the text of each transaction of the processed training data; building a language model on the processed training data using the word unit inventory; and generating a new word unit by combining two units out of the word inventory.”: This limitation merely specifies mental processes- concept of observation and evaluation of labeling datasets.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 10.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “determining whether training criteria for the entity resolution model are met, the training criteria comprising one or more of the following: a target loss accuracy, a depth for recall at a specified percentage, and an F1 measure.”: This limitation merely specifies mental processes- concept of observation and evaluation of labeling datasets.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claims 11-20.
Step 1: The claims recite a server; therefore, they fall into the statutory category.
Step 2A Prong 1: The claims recite the same mental processes as claims 1-10, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Claims 11-20 recite generic computer components, namely “the server comprising: a database;
one or more processors; and a memory storing computer-executable instructions”. As before, the mere recitation that the method is to be performed on a generic computer amounts to a mere instruction to apply the exception on the computer. See MPEP § 2106.05(f). With that exception, the analysis mirrors that of claims 1-10, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, with the one exception noted above, mirrors that of claims 1-10, respectively.
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.
Claim(s) 1-3, 7, 10-13, 17, and 20 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (“A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts”, Proceedings of the 2021 IEEE) in view of Feng et al. (US 20220253725 A1).
Regarding claim 1.
Zhao teaches a computer-implemented method performed by a server for training an entity resolution model for entity determination and standardization of financial transactions (see abstract idea, “we propose a sentiment analysis and key entity detection approach based on BERT, which is applied in online financial text mining and public opinion analysis in social media.”), the method comprising:
inputting raw training datasee pages 1235-1236, “We use two sets of data in different granularity from 2019 CCF BDCI1 sub task as Dataset 1: Negative Financial Information And Subject Determination and 2019 CCKS2 sub task as Dataset 2: Event Subject Extraction For Financial Field. These datasets are all online text data in financial domain”);
generating a label dictionary, the generating comprising performing natural language processing on the raw training data using an automatic engine, the natural language processing including: scanning text of each transaction; extracting one or more entities from the text (see page 1235, “b) Get financial entity list and select key entities: For each piece of financial data, we use NER or rule matching to get entities from the text as entity list (in some datasets, the entity list has been provided). In the coarse-grained task, we detected some key entities related to the financial text from the entity list. The key entities may be one or more. Therefore, we consider this task as a sentence matching task”);
generating tagged data from the raw training data using the label dictionary (see page 1236, “There are two existing strategies for applying pre-trained language representations to downstream tasks: feature-based and fine-tuning [22]. Therefore, we use BERT as a pretrained model or use BERT to generate sentence-level vectors and then connect to the downstream model in different granularity. For two sets of data, we divide them into training set and development set by the method of 10-fold cross validation. Experiment processes are as follows: • Clean the data and remove extraneous symbols, URLs, garbled characters, etc. • Construct a new dataset with each entity in the entity list and text if there is entity list in the original dataset”.);
performing vocabulary training on the raw training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format (see 1233, “We use RoBERTa as a pre-training model for fine-tuning, and different fine-tuning methods are used to implement sentiment analysis and key entity detection.”, also see page 1235, “we use sentiment analysis model based on RoBERTa to select negative information (Fig. 2). RoBERTa was chosen to have an identical model size as BERT and used bidirectional Transformer. The online financial text words are used as the input of the model, and sentiments are output by the models from the last layer.”);
training the entity resolution model using a transformer model, the tokenized text in the transformer model specific format, and the tagged data (see 1233, “We use RoBERTa as a pre-training model for fine-tuning, and different fine-tuning methods are used to implement sentiment analysis and key entity detection.”, also see page 1235, “we use sentiment analysis model based on RoBERTa to select negative information (Fig. 2). RoBERTa was chosen to have an identical model size as BERT and used bidirectional Transformer. The online financial text words are used as the input of the model, and sentiments are output by the models from the last layer.”, see page 1236, “There are two existing strategies for applying pre-trained language representations to downstream tasks: feature-based and fine-tuning [22]. Therefore, we use BERT as a pretrained model or use BERT to generate sentence-level vectors and then connect to the downstream model in different granularity. For two sets of data, we divide them into training set and development set by the method of 10-fold cross validation. Experiment processes are as follows: • Clean the data and remove extraneous symbols, URLs, garbled characters, etc. • Construct a new dataset with each entity in the entity list and text if there is entity list in the original dataset. Train the training data in the model to obtain the model parameters. • Use the training parameters to predict the sentiment and key entity of the development set. • Compare the predicted sentiment and key entity of the development set with its true result and assess the model by accuracy or F1.”.);
Zhao do not teach inputting raw training data into the entity resolution model, the raw training data including historical transaction data comprising a plurality of transactions; and storing the label dictionary in a database, the label dictionary including the one or more entities; and storing the trained entity resolution model in a database.
Feng teaches inputting raw training data into the entity resolution model, the raw training data including historical transaction data comprising a plurality of transactions (see ¶ 51, “As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained and/or input from training data (e.g., historical data), such as data gathered during one or more processes described herein”); and storing the label dictionary in a database, the label dictionary including the one or more entities; and storing the trained entity resolution model in a database (see ¶ 73, “The entity resolution system 420 includes one or more devices capable of receiving, generating, storing, processing, providing, and/or routing information associated with machine learning model-based entity resolution, as described elsewhere herein. The entity resolution system 420 may include a communication device and/or a computing device. For example, the entity resolution system 420 may include a server, such as an application server, a client server, a web server, a database server, a host server, a proxy server, a virtual server (e.g., executing on computing hardware), or a server in a cloud computing system. In some implementations, the entity resolution system 420 includes computing hardware used in a cloud computing environment.”).
Both Zhao and Feng pertain to the problem of neural network entity recognition, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Zhao and Feng to teach the above limitations. The motivation for doing so would be to store data that is not duplicate, different types etc., to standardize transaction entity, “Entity resolution tasks involve disambiguating records that correspond to manifestations of real world entities across different datasets or within the same dataset. Entity resolution tasks may include eliminating duplicate copies of repeated data, clustering or grouping records that correspond to the same entity, identifying records that reference the same entity across different datasets, and/or converting data that represents entities with multiple representations into a standard form, among other examples.” (see Feng ¶ 1 and ¶ 12).
Regarding claim 2.
Zhao and Feng teaches the computer-implemented method in accordance with claim 1,
Feng further teaches further comprising receiving the raw training data from a data source (see ¶ 51, “As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained and/or input from training data (e.g., historical data), such as data gathered during one or more processes described herein”, see ¶ 73, “The entity resolution system 420 includes one or more devices capable of receiving, generating, storing, processing, providing, and/or routing information associated with machine learning model-based entity resolution, as described elsewhere herein. The entity resolution system 420 may include a communication device and/or a computing device. For example, the entity resolution system 420 may include a server, such as an application server, a client server, a web server, a database server, a host server, a proxy server, a virtual server (e.g., executing on computing hardware), or a server in a cloud computing system. In some implementations, the entity resolution system 420 includes computing hardware used in a cloud computing environment.”).
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 2.
Regarding claim 3.
Zhao and Feng teaches the computer-implemented method in accordance with claim 2,
Feng further teaches receiving the raw training data comprises one or more of the following: retrieving the raw training data from the database and receiving the raw training data from one or more data source computing devices (see ¶ 51, “As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained and/or input from training data (e.g., historical data), such as data gathered during one or more processes described herein”, see ¶ 73, “The entity resolution system 420 includes one or more devices capable of receiving, generating, storing, processing, providing, and/or routing information associated with machine learning model-based entity resolution, as described elsewhere herein. The entity resolution system 420 may include a communication device and/or a computing device. For example, the entity resolution system 420 may include a server, such as an application server, a client server, a web server, a database server, a host server, a proxy server, a virtual server (e.g., executing on computing hardware), or a server in a cloud computing system. In some implementations, the entity resolution system 420 includes computing hardware used in a cloud computing environment.”).
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 3.
Regarding claim 7.
Zhao and Feng teaches the computer-implemented method in accordance with claim 1,
Zhao further teaches generating tagged data from the raw training data using the label dictionary comprises performing name entity recognition (NER) tagging on each transaction, including labelling each respective transaction with the one or more entities identified in the respective transaction (see page 1235, “b) Get financial entity list and select key entities: For each piece of financial data, we use NER or rule matching to get entities from the text as entity list (in some datasets, the entity list has been provided). In the coarse-grained task, we detected some key entities related to the financial text from the entity list. The key entities may be one or more. Therefore, we consider this task as a sentence matching task”, see page 1236, “There are two existing strategies for applying pre-trained language representations to downstream tasks: feature-based and fine-tuning [22]. Therefore, we use BERT as a pretrained model or use BERT to generate sentence-level vectors and then connect to the downstream model in different granularity. For two sets of data, we divide them into training set and development set by the method of 10-fold cross validation. Experiment processes are as follows: • Clean the data and remove extraneous symbols, URLs, garbled characters, etc. • Construct a new dataset with each entity in the entity list and text if there is entity list in the original dataset”.).
Regarding claim 10.
Zhao and Feng teaches the computer-implemented method in accordance with claim 1,
Zhao further teaches further comprising determining whether training criteria for the entity resolution model are met, the training criteria comprising one or more of the following: a target loss accuracy, a depth for recall at a specified percentage, and an F1 measure (see page 1236, “There are two existing strategies for applying pre-trained language representations to downstream tasks: feature-based and fine-tuning [22]. Therefore, we use BERT as a pretrained model or use BERT to generate sentence-level vectors and then connect to the downstream model in different granularity. For two sets of data, we divide them into training set and development set by the method of 10-fold cross validation. Experiment processes are as follows: • Clean the data and remove extraneous symbols, URLs, garbled characters, etc. • Construct a new dataset with each entity in the entity list and text if there is entity list in the original dataset. Train the training data in the model to obtain the model parameters. • Use the training parameters to predict the sentiment and key entity of the development set. • Compare the predicted sentiment and key entity of the development set with its true result and assess the model by accuracy or F1.”.).
Claim 11 recites a server comprising a database; one or more processors; and a memory storing computer-executable instructions to perform the method recited in claim 1. Therefore the rejection of claim 1 above applies equally here. Feng also teaches the addition elements of claim 11 not recited in claim 1 comprising server comprising a database; one or more processors; and a memory storing computer-executable instructions (see ¶ 73, “The entity resolution system 420 includes one or more devices capable of receiving, generating, storing, processing, providing, and/or routing information associated with machine learning model-based entity resolution, as described elsewhere herein. The entity resolution system 420 may include a communication device and/or a computing device. For example, the entity resolution system 420 may include a server, such as an application server, a client server, a web server, a database server, a host server, a proxy server, a virtual server (e.g., executing on computing hardware), or a server in a cloud computing system. In some implementations, the entity resolution system 420 includes computing hardware used in a cloud computing environment.”).
Claims 12-13, 17, and 20 recites a server comprising a database; one or more processors; and a memory storing computer-executable instructions to perform the method recited in claims 2-3, 7, 10. Therefore the rejection of claims 2-3, 7, 10 above applies equally here.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (“A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts”, Proceedings of the 2021 IEEE) in view of Feng et al. (US 20220253725 A1) in further in view of Lobur et al. (“Using NLTK for educational and scientific purposes”, CADSM’2011, 23-25 February, 2011).
Regarding claim 4.
Zhao and Feng teaches the computer-implemented method in accordance with claim 1,
Zhao and Feng do not teach the limitations of claim 4.
Lobur teaches performing the natural language processing on the raw training data comprises using Python-based Natural Language Toolkit (NLTK) (see page 427, “During this course they learn to use NLTK and acquire the basics of programming in Python, using Natural Language Processing with Python by Steven Bird et al [1] as a guide. Among other things, students learn to: • work with different variable types; • access text corpora and lexical resources; • process raw text (normalize, tokenize, etc.); • discover part-of-speech tags; • use regular expressions; • use tagging, stemming and chunking;”).
Zhao, Feng and Lobur pertain to the problem of Natural language processing, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Zhao, Feng and Lobur to teach the above limitations. The motivation for doing so would be, “Python and the Natural Language Toolkit (NLTK) allow any programmer, even a beginner, to get acquainted with NLP tasks easily without spending too much time on studying or gathering resources. The aim of this paper is to provide valuable proof and examples, which show how necessary the NLTK is for the course of Computational Linguistics at the university and for researchers in the field of natural language processing.” (see Lobur introduction).
Claim 14 recites a server comprising a database; one or more processors; and a memory storing computer-executable instructions to perform the method recited in claim 4. Therefore the rejection of claim 14 above applies equally here.
Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (“A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts”, Proceedings of the 2021 IEEE) in view of Feng et al. (US 20220253725 A1) in further in view of Scott et al. (US 20210232772 A1).
Regarding claim 5.
Zhao and Feng teaches the computer-implemented method in accordance with claim 1,
Zhao and Feng do not teach the limitations of claim 5.
Scott teaches extracting the one or more entities from the text comprises performing the following processes on the text of each transaction: word tokenization, word lemmatization, stop word removal, punctuation removal, and accented character and word conversion (see ¶ 16, “FIG. 2 shows an exemplary breakdown of the NLP functions. At 21, the system tags words by tokenizing the text, tagging sections of text based on type (e.g., noun, verb, etc.), and lemmatizes nouns. At 23, the system removes various text, including removal of extraneous punctuation, removal of basic stop words (e.g., the, a, and), removal of a list of stop words (e.g., words that are known to be stop words based on the context), and removal of a user created list of stop words (e.g., user defined words that may be expected).”).
Zhao, Feng and Scott pertain to the problem of Natural language processing, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Zhao, Feng and Scott to teach the above limitations. The motivation for doing so would be, “system that uses natural language processing (NLP) to read data from a file and analyze the data based on user defined parameters. When presented with a data file of potentially unknown or unfamiliar format, users have to manually read hundreds of lines of data to deduce information from a text entry description. For large and complicated files, this can take weeks of effort and time. This can also be prone to bias in trend only looking for the certain types of information. What is needed is an automated process to remove manual processing as well as give analytical results to assist a user in evaluating the data.” (see Scott ¶ 4).
Regarding claim 6.
Zhao, Feng and Scott teach the computer-implemented method in accordance with claim 5,
Scott further teaches performing the word tokenization process comprises splitting the text into individual words and defining a token for each word (see ¶ 16, “FIG. 2 shows an exemplary breakdown of the NLP functions. At 21, the system tags words by tokenizing the text, tagging sections of text based on type (e.g., noun, verb, etc.), and lemmatizes nouns. At 23, the system removes various text, including removal of extraneous punctuation, removal of basic stop words (e.g., the, a, and), removal of a list of stop words (e.g., words that are known to be stop words based on the context), and removal of a user created list of stop words (e.g., user defined words that may be expected).”).
The motivation utilized in the combination of claim 4, super, applies equally as well to claim 6.
Claims 15-16 recites a server comprising a database; one or more processors; and a memory storing computer-executable instructions to perform the method recited in claims 5-6. Therefore the rejection of claims 5-6 above applies equally here.
Claim(s) 8-9 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (“A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts”, Proceedings of the 2021 IEEE) in view of Feng et al. (US 20220253725 A1) in further in view of Sennrich et al. (“Neural Machine Translation of Rare Words with Subword Units”, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1715–1725, 2016).
Regarding claim 8.
Zhao and Feng teaches the computer-implemented method in accordance with claim 1,
Zhao and Feng do not teach the limitations of claim 8.
Sennrich teaches tokenizing the text of each transaction of the raw training data includes performing subword-based tokenization on the raw training data (see page 1716, “3 Subword Translation The main motivation behind this paper is that the translation of some words is transparent in that they are translatable by a competent translator even if they are novel to him or her, based on a translation of known subword units such as morphemes or phonemes. Word categories whose translation is potentially transparent include… In an analysis of 100 rare tokens (not among the 50000 most frequent types) in our German training data1, the majority of tokens are potentially translatable from English through smaller units.”).
Zhao, Feng and Sennrich pertain to the problem of Natural language processing, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Zhao, Feng and Sennrich to teach the above limitations. The motivation for doing so would be, “we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks by up to 1.1 and 1.3 BLEU, respectively.” (see Sennrich Abstract).
Regarding claim 9.
Zhao, Feng and Sennrich teaches the computer-implemented method in accordance with claim 8,
Sennrich further teaches performing the subword-based tokenization on the processed training data comprises: initializing a word unit inventory with all the characters in the text of each transaction of the processed training data; building a language model on the processed training data using the word unit inventory; and generating a new word unit by combining two units out of the word inventory (see page 1717, “Firstly, we initialize the symbol vocabulary with the character vocabulary, and represent each word as a sequence of characters, plus a special end-of word symbol ‘·’, which allows us to restore the original tokenization after translation. We iteratively count all symbol pairs and replace each occurrence of the most frequent pair (‘A’, ‘B’) with a new symbol ‘AB’. Each merge operation produces a new symbol which represents a character n-gram. Frequent character n-grams (or whole words) are eventually merged into a single symbol, thus BPE requires no shortlist. The final symbol vocabulary size is equal to the size of the initial vocabulary, plus the number of merge operations– the latter is the only hyperparameter of the algorithm.”, also see 1718, “Figure 1 shows a toy example of learned BPE operations. At test time, we first split words into sequences of characters, then apply the learned operations to merge the characters into larger, known symbols. This is applicable to any word, and allows for open-vocabulary networks with fixed symbol vocabularies.3 In our example, the OOV ‘lower’ would be segmented into ‘low er·’.”).
The motivation utilized in the combination of claim 8, super, applies equally as well to claim 9.
Claims 18-19 recites a server comprising a database; one or more processors; and a memory storing computer-executable instructions to perform the method recited in claims 8-9. Therefore the rejection of claims 8-9 above applies equally here.
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US 20240303466 teaching methods and systems are presented for improving the accuracy performance and utilization rates of a cascade machine learning model system. The cascade machine learning model system includes multiple machine learning models configured to process transactions according to a cascade operation scheme.
US 20230351194 teaching a system for identifying connections between individuals based on relationships found in data. The system includes a database containing data records and fields and identifying individuals involved in each record.
US 20210357375 teaching various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.
US 20210342847 teaching an artificial intelligence system configured to detect anomalies in transaction data sets. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform modeling operations which include receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique, accessing at least one micro-model trained using at least one second data set separate from the first data set, determining risk scores from the first data set using the at least one micro-model, enriching the first data set with the risk scores, and determining the first machine learning model for the enriched first data set using the machine learning technique.
US 20210326888 teaching a method of reducing financial fraud by operating artificial intelligence machines organized into parallel sets of predictive models with each set specially trained with supervised and unsupervised training data filtered for a particular financial channel.
US 20200202245 teaching decision engines are deployed in a variety of fields, from medical diagnostics to financial applications such as lending. Typically, solutions involve rule engines or artificial intelligence (AI) to assist in making a decision based on transactional data.
Conclusion
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/IMAD KASSIM/Primary Examiner, Art Unit 2129