DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
2. Applicant’s 11/25/2025 election without traverse of Group I, claims 1, 2, 4, 5, 7, 8, 9, 11, 13, 14, 16, 17, 19, and 20 in response to the restriction requirement of 11/21/2025, is acknowledged and the election is made Final. Pending claims 1, 2, 4, 5, 7, 8, 9, 11, 13, 14, 16, 17, 19, and 20 are rejected for the reasons set forth below.
Information Disclosure Statement
3. The Information Disclosure Statement (IDS) filed on 11/04/2023 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1, 2, 4, 5, 7, 8, 9, 11, 13, 14, 16, 17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
6. Analysis:
Step 1: Statutory Category?: (is the claim(s) directed to a process, machine, manufacture or composition of matter?) - YES: In the instant case, claims 2, 7,16, and 20 are directed to a method (i.e., process), claims 1, 4, 5, 8, 9, 11, 13, 14, 17, and 19 are directed to a system (i.e., machine).
Regarding independent claim 1:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 1 recites the at least following limitations of “… automatically detecting anomalies in financial reports ….” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities and/or business relations for identifying discrepancies in financial reports). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 1 further to the abstract idea includes additional elements of “a software solution” and “natural language processing”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as 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 -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a software solution” and “natural language processing” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 2:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 2 recites the at least following limitations of “… automatically detecting anomalies in financial reports … the parsed financial data … is converted into a structured format for easier analysis” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities and/or business relations for identifying discrepancies in financial reports). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 2 further to the abstract idea includes additional elements of “natural language processing”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as 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 -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “natural language processing” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Dependent claims 4, 5, 7, 8, 9, 11, 13, 14, 16, 17, 19, and 20 have been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent-ineligible under 35 U.S.C. 101.
Dependent claim 4: simply provide further definition to “machine learning models” recited in independent claim 1. Simply stating that wherein machine learning models are employed to enhance detection accuracy based on historical data amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., machine learning models).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more).
Dependent claim 5: simply refines the abstract idea because it recites limitations (e.g., which offers users a summarized report of detected inconsistencies), that fall under the category of organizing human activity as described above in independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 7: simply provide further definition to “financial data” recited in independent claim 2. Simply stating that wherein financial data is classified and segregated according to predefined categories does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more).
Dependent claim 8: simply refines the abstract idea because it recites limitations (e.g., which includes an adaptive learning mechanism that refines anomaly detection processes over time), that fall under the category of organizing human activity as described above in independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 9: simply provide further definition to “the anomaly detection” recited in independent claim 1. Simply stating that where the anomaly detection is facilitated by a combination of both rule-based and probabilistic approaches amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., machine learning models).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more).
Dependent claim 11: simply refines the abstract idea because it recites limitations (e.g., with a built-in alert system that notifies users upon detection of critical inconsistencies in financial data), that fall under the category of organizing human activity as described above in independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 13: simply refines the abstract idea because it recites limitations (e.g., that employs normalization and standardization techniques for the input financial data), that fall under the category of organizing human activity as described above in independent claim 2. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 14: simply refines the abstract idea because it recites limitations (e.g., which integrates with external financial databases or third-party interfaces for additional data validation), that fall under the category of organizing human activity as described above in independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 16: simply provide further definition to “the structured format” recited in independent claim 2. Simply stating that where the structured format assists in comparative financial analysis over different periods amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., machine learning models).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more).
Dependent claim 17: simply refines the abstract idea because it recites limitations (e.g., that maintains a repository of historical discrepancies to facilitate the machine learning model's training), that fall under the category of organizing human activity as described above in dependent claim 4. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 19: simply refines the abstract idea because it recites limitations (e.g., which can be deployed across various platforms including cloud, on-premises, and hybrid environments), that fall under the category of organizing human activity as described above in dependent claim 4. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 20: simply provide further definition to “additional metadata” recited in independent claim 2. Simply stating that where additional metadata is generated to provide context to the structured financial data amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., machine learning models).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more).
Claim Rejections - 35 USC § 102
7. 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.
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
9. Claims 1, 2, 4, 5, 7, 8, 9, 11, 13, 14, 16, 17, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stoddard et al. (U.S. Pub. No. 2019/0180385), hereinafter, “Stoddard”.
Claim 1 –
Stoddard disclose:
a software solution capable of automatically detecting anomalies in financial reports using natural language processing (Stoddard, [0020], [0050], “the analysis may be semi-automated such that the smart bot identifies financial issues or errors based on the received data, and a bank representative provides personalized advice to the customer's representatives via email, text, or telephone conversations … storage units 38 are used to store program instructions for execution by processors 34. Storage units 38 may be used by software or applications running on computing device 18 (e.g., customer training unit 40) to temporarily store information during program execution”, see also Figures 1-5).
Claim 2 –
Stoddard disclose:
a method where the parsed financial data from claim 1 is converted into a structured format for easier analysis (Stoddard, [0008], [0020], “The method further comprises receiving, by the computing device and at the entry included in the user interface, an indication of a financial value that is input by a customer representative associated with the customer of the financial entity … the analysis may be semi-automated such that the smart bot identifies financial issues or errors based on the received data, and a bank representative provides personalized advice to the customer's representatives via email, text, or telephone conversations”, see also Figures 1-5).
Claim 4 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
wherein machine learning models are employed to enhance detection accuracy based on historical data (Stoddard, [0064], “Error detection unit 48 may identify a financial issue. Financial issues may refer to instances where customer information, information in external financial database 20, information in financial institution network 13, other information, or combinations thereof indicate that a financial value that is input by a customer representative associated with the customer of the financial entity is incorrect, potentially incorrect, or is otherwise inconsistent with the customer information, information in external financial database 20, information in financial institution network 13, other information”, see also Figure 2).
Claim 5 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
which offers users a summarized report of detected inconsistencies (Stoddard, [0068], “covenants may be calculated monthly and financials may be reported monthly. In this instance, error detection unit 48 may pre-empt the covenant calculation to identify inconstancies between the covenant calculation and the financials reported”, see also Figure 2).
Claim 7 –
Stoddard disclose the method of claim 2, as shown above.
Stoddard further disclose:
wherein financial data is classified and segregated according to predefined categories (Stoddard, [0069], “Error detection unit 48 may identify a financial issue related to a proposed financial transaction. For example, in response to receiving a financial value that is input, at an entry of a user interface corresponding to a proposed purchase, by a customer representative indicating that the proposed purchase trip (e.g., breach) a covenant of the customer, error detection unit 48 may identify that a financial issue related to what triggers a covenant would occurred if the proposed purchase is performed”, see also Figure 2).
Claim 8 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
which includes an adaptive learning mechanism that refines anomaly detection processes over time (Stoddard, [0064], “Error detection unit 48 may identify a financial issue. Financial issues may refer to instances where customer information, information in external financial database 20, information in financial institution network 13, other information, or combinations thereof indicate that a financial value that is input by a customer representative associated with the customer of the financial entity is incorrect, potentially incorrect, or is otherwise inconsistent with the customer information, information in external financial database 20, information in financial institution network 13, other information”, see also Figure 2).
Claim 9 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
where the anomaly detection is facilitated by a combination of both rule-based and probabilistic approaches (Stoddard, [0064], “Error detection unit 48 may identify a financial issue. Financial issues may refer to instances where customer information, information in external financial database 20, information in financial institution network 13, other information, or combinations thereof indicate that a financial value that is input by a customer representative associated with the customer of the financial entity is incorrect, potentially incorrect, or is otherwise inconsistent with the customer information, information in external financial database 20, information in financial institution network 13, other information”, see also Figure 2).
Claim 11 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
with a built-in alert system that notifies users upon detection of critical inconsistencies in financial data (Stoddard, [0068], “covenants may be calculated monthly and financials may be reported monthly. In this instance, error detection unit 48 may pre-empt the covenant calculation to identify inconstancies between the covenant calculation and the financials reported”, see also Figure 2).
Claim 13 –
Stoddard disclose the system of claim 2, as shown above.
Stoddard further disclose:
that employs normalization and standardization techniques for the input financial data (Stoddard, [0073], “financial institution representative notification unit 52 may generate an e-mail to a representative of financial institution network 13 indicating that the input value of $2 million dollars per month for accounts receivable is outside of the expected range of less than $1 million for the customer of the financial institution”, see also Figure 2).
Claim 14 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
which integrates with external financial databases or third-party interfaces for additional data validation (Stoddard, [0073], “financial institution representative notification unit 52 may generate an e-mail to a representative of financial institution network 13 indicating that the input value of $2 million dollars per month for accounts receivable is outside of the expected range of less than $1 million for the customer of the financial institution”, see also Figure 2).
Claim 16 –
Stoddard disclose the system of claim 2, as shown above.
Stoddard further disclose:
where the structured format assists in comparative financial analysis over different periods (Stoddard, [0064], “Error detection unit 48 may identify a financial issue. Financial issues may refer to instances where customer information, information in external financial database 20, information in financial institution network 13, other information, or combinations thereof indicate that a financial value that is input by a customer representative associated with the customer of the financial entity is incorrect, potentially incorrect, or is otherwise inconsistent with the customer information, information in external financial database 20, information in financial institution network 13, other information”, see also Figure 2).
Claim 17 –
Stoddard disclose the system of claim 4, as shown above.
Stoddard further disclose:
that maintains a repository of historical discrepancies to facilitate the machine learning model's training (Stoddard, [0064], “Error detection unit 48 may identify a financial issue. Financial issues may refer to instances where customer information, information in external financial database 20, information in financial institution network 13, other information, or combinations thereof indicate that a financial value that is input by a customer representative associated with the customer of the financial entity is incorrect, potentially incorrect, or is otherwise inconsistent with the customer information, information in external financial database 20, information in financial institution network 13, other information”, see also Figure 2).
Claim 19 –
Stoddard disclose the system of claim 1, as shown above.
Stoddard further disclose:
which can be deployed across various platforms including cloud, on-premises, and hybrid environment (Stoddard, [0084], “if implemented in software, the functions may be stored on or transmitted over a computer-readable medium as one or more instructions or code, and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media”, see also Figures 1-5).
Claim 20 –
Stoddard disclose the system of claim 2, as shown above.
Stoddard further disclose:
where additional metadata is generated to provide context to the structured financial data (Stoddard, [0064], “Error detection unit 48 may identify a financial issue. Financial issues may refer to instances where customer information, information in external financial database 20, information in financial institution network 13, other information, or combinations thereof indicate that a financial value that is input by a customer representative associated with the customer of the financial entity is incorrect, potentially incorrect, or is otherwise inconsistent with the customer information, information in external financial database 20, information in financial institution network 13, other information”, see also Figure 2).
Relevant Prior Art
10. The prior art made of record and not relied upon are considered pertinent to applicant's disclosure:
Alletto et al. (U.S. Pub. No. 2013/0061179) teach identification and escalation of risk-related data.
Gupta (U.S. Pub. No. 2005/0197931) teaches system, apparatus and method for standardized financial reporting.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Liz Nguyen whose telephone number is (571) 272-5414. The examiner can normally be reached on Monday to Friday 8:00 A.M to 5:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart, can be reached on (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Center system (visit: https://patentcenter.uspto.gov). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (USA or CANADA) or (571) 272-1000.
/LIZ P NGUYEN/
Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696