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 claims are either directed to a system or a method, which is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified system Claim 8 as the claim that represents the claimed invention for analysis and is similar to Claim 1 and Claim 15. Claim 8 recites the limitations of (additional elements emphasized in bold and are considered to be parsed from the remaining abstract idea):
“A server for training a classification model, the server comprising: a database; one or more processors; and a memory storing computer-executable instructions, that when executed by the one or more processors, cause the one or more processors to: receive raw training data from a data source, the raw training data including historical transaction data comprising a plurality of transactions; input the raw training data into the classification model; generate processed training data by performing a data preparation operation on the raw training data, including removing numerical characters, repeating special characters, and accent words; perform vocabulary training on the processed training data, including tokenizing text of each transaction of the processed training data and converting the tokenized text into a transformer model specific format; obtain a transformer model; train the classification model using the transformer model and the tokenized text in the transformer model specific format; and store the trained classification model in the database.”
Which is a process that, under its broadest which is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) as a Mental process (concept performed in the human mind) and Certain Methods of Organizing Human Activity (fundamental economic practice -- mitigate risk) training a classification model by using a transformer model (see, e.g., ¶56).
If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation as a certain method of a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Similarly if a claim limitation under its BRI, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. (Claims can recite a mental process even if they are claimed as being performed on a computer Gottschalk v. Benson, 409 U.S. 63; "Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).)
Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h). In claim 8, the server, database, processors, memory storing computer-executable instructions and transformer model are using generic computer components. And claim 15 additionally includes a non-transitory CRM.
Claim 1 does not utilize any generic computer components in its current form, however in order to advance compact prosecution, will be assumed to also apply the same circuitry as applied in Claim 1 (Applicant should fix in the next action). Each step should positively recited to include “the server” performing each step.
The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore claim 8 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Mere instructions to implement an abstract idea on or with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claim 8 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
The dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements (Claims 2, 9 & 16 – computing devices; Claims 5, 12 & 19 – BERTWordPieceTokenizer; Claims 6, 13 & 20 – DistilBert transformer model, which are all generic computer components that further implement the abstract idea) that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination:
The dependent claims recite further steps that can be performed in the human mind.
Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
Allowable Subject Matter
Claims 1-20 would be allowable if re-written to overcome the 35 USC 101 rejections detailed above.
The closest prior art of record is:
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.
The following is a statement of reasons for the indication of allowable subject matter. In combination with the other limitations nothing in the prior art of record teaches, suggests, or discloses:
Re 1-7: in claim 1, “generating processed training data by performing a data preparation operation on the raw training data, including removing numerical characters, repeating special characters, and accent words; performing vocabulary training on the processed training data, including tokenizing text of each transaction of the processed training data and converting the tokenized text into a transformer model specific format; obtaining a transformer model; training the classification model using the transformer model and the tokenized text in the transformer model specific format; and storing the trained classification model in a database.”
The prior art of record fails to teach or render obvious this limitation.
Re 8-14: in claim 8, “generate processed training data by performing a data preparation operation on the raw training data, including removing numerical characters, repeating special characters, and accent words; perform vocabulary training on the processed training data, including tokenizing text of each transaction of the processed training data and converting the tokenized text into a transformer model specific format; obtain a transformer model; train the classification model using the transformer model and the tokenized text in the transformer model specific format; and store the trained classification model in the database.”
The prior art of record fails to teach or render obvious this limitation.
Re 15-20: in claim 15, “generate processed training data by performing a data preparation operation on the raw training data, including removing numerical characters, repeating special characters, and accent words; perform vocabulary training on the processed training data, including tokenizing text of each transaction of the processed training data and converting the tokenized text into a transformer model specific format; obtain a transformer model; train the classification model using the transformer model and the tokenized text in the transformer model specific format; and store the trained classification model in a database.”
The prior art of record fails to teach or render obvious this limitation.
Conclusion
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GERALD J. SUFLETA II
Primary Examiner
Art Unit 2875
/GERALD J SUFLETA II/Primary Examiner, Art Unit 2875