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 .
Applicant filed a response dated October 23, 2025 in which claims 21, 28, and 35 have been amended. Therefore, claims 21-40 are currently pending in the application.
Priority
Application 18/168,458 was filed on 02/13/2023 and is a CON of 16/749,573 01/22/2020 PAT 11,580,549.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. § 112(a) or § 112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
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 21-40 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. (MPEP 2106). The claims are directed to a method, system, and apparatus which is one of the statutory categories of invention (Step 1: YES). The recitation of the claimed invention is analyzed as follows, in which the abstract elements are boldfaced.
Claim 28 recites the limitations of:
A device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to:
format recording data associated with a recording of an authorized user associated with an account, wherein formatting the recording data comprises removing a portion of the recording data;
convert, based on receiving an indication that a transaction involving the account is to be processed, the recording data from video data to text data,
wherein the recording data includes a recording of the authorized user describing the transaction;
analyze, the text data to determine one or more transaction characteristics that describe the transaction;
identify one or more missing transaction characteristics that are not identified in the recording based on comparing the one or more transaction characteristics with a list of one or more required transaction characteristics,
wherein the one or more required transaction characteristics includes characteristics used by a risk score model to determine particular risk scores associated with particular transactions;
cause a user device associated with providing the recording data to display a message providing instructions to submit a supplemental recording that includes the one or more missing transaction characteristics;
update the one or more transaction characteristics based on obtaining the supplemental recording that includes the one or more missing transaction characteristics;
analyze, using a set of feature identification techniques, historical transaction data to identify one or more features that impact a likelihood of a transaction being a legitimate transaction, wherein the set of feature identification techniques includes: text mining and latent semantic analysis (LSA), a trend variable analysis technique, an interest diversity analysis technique, a technique using a neural network, a composite indicators analysis technique, a clustering technique, or a regression technique;
train, based on the identified one or more features, the risk score model, wherein training the risk score model comprises training a neural network that has an input layer, one or more intermediate layers, and an output layer;
determine a risk score associated with the transaction based on an output of the risk score model,
wherein the one or more processors, to determine the risk score, are configured to: perform a feedforward technique to provide particular transaction characteristic values, of the one or more transaction characteristics, and merchant review data values as input into the risk score model,
output, using the risk score model, the risk score based on the particular transaction characteristic values and the merchant review data values,
determine an error value based on comparing the risk score to one or more known risk score values, and update, based on the error value, a cost function associated with the risk score model;
cause, based on the risk score meeting or exceeding a threshold, the recording data to be stored in association with transaction data that identifies a list of transactions that are associated with the account; and
update, in response to causing the recording data to be stored, a search feature of the device, wherein updating the search feature enables the recording to be located using a keyword search.
The claim as a whole recites a method that, under its broadest reasonable interpretation, covers collecting, analyzing, and transmitting data for the mitigation of risk and completion of a transaction. This is a fundamental economic practice of a financial transaction; a commercial interaction, such as for business relations; and managing personal behavior or relationships or interactions between people, which are certain methods of organizing human activity.
Finally, claims 21, 28, and 35 also recite “set of feature identification techniques includes: text mining and latent semantic analysis (LSA), a trend variable analysis technique, an interest diversity analysis technique, a technique using a neural network, a composite indicators analysis technique, or a clustering technique, a regression technique;” and claim 25, 32, and 38 recite “use machine learning to determine whether an individual associated with transaction is the authorized user”. This is a mathematical calculation.
Thus, the claims recite an abstract idea. (Step 2A, prong 1: YES).
Moreover, the judicial exception is not integrated into a practical application. Other than reciting a “device”, “one or more memories”, “one or more processors coupled to the one or more memories”, “user device”, “neural network”, “machine learning”, “non-transitory computer-readable medium”, to perform the steps of “formatting”, “converting”, “analyzing”, “identifying”, “causing”, “updating”, “determining”, “training”, and “outputting”, nothing in the claim elements preclude the steps from practically being a certain method for organizing human activity or mathematical calculation. The claim as a whole does not integrate the judicial exception into a practical application. The claim merely describes how to generally “apply” the concept of collecting, analyzing, and transmitting data for the mitigation of risk and completion of a transaction in a computer environment. The additional computer elements recited in the claim limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception utilizing generic computer components.
For example, the Specification at [0074] discloses “environment 200 may include a user device210, an application server device 220, a transaction management platform 230 supported by a cloud computing environment 240, a data storage device 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections. [0075] User device 210 includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with a recording. For example, user device 210 may include a communication and/or computing device, such as a mobile phone (e.g., a smart phone, a radiotelephone, and/or the like), a laptop computer, a tablet computer, a handheld computer, a gaming device, a wearable communication device (e.g., a smart wristwatch, a pair of smart eyeglasses, smart clothing, and/or the like), or a similar type of device.”
Additionally, the limitation of “format recording data associated with a recording of an authorized user associated with an account, wherein formatting the recording data comprises removing a portion of the recording data”, amounts to mere data gathering and data manipulation means of the user’s option to select specific data, and the transaction management platform is merely generally linking the judicial exception to a particular technological environment. The specification says no more than the transaction management platform may remove a portion of the recording, condense a portion of the recording, and/or the like. (See Spec. [0043]).
Furthermore, the Specification at [0047] discloses “The one or more machine learning techniques may include a regression technique, a clustering technique, a technique using a neural network, and/or the like.”
Thus, the specification supports that general purpose computers or computer components are utilized to implement the steps of the abstract idea.
Merely implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim as a whole, in viewing the additional elements both individually and in combination, does not integrate the judicial exception into a practical application. Accordingly, these additional elements do 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 2A prong two: No)
The claim does not include additional elements, when considered both individually and as an ordered combination, 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 using a “device”, “one or more memories”, “one or more processors coupled to the one or more memories”, “user device”, “neural network”, “machine learning”, “non-transitory computer-readable medium”, to perform the steps of “formatting”, “converting”, “analyzing”, “identifying”, “causing”, “updating”, “determining”, “training”, and “outputting”, amounts to no more than mere instructions to apply the exception using generic computer component. The claim merely describes how to generally “apply” the concept of collecting, analyzing, and transmitting data for the mitigation of risk and completion of a transaction in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Such additional elements are determined to not contain an inventive concept according to MPEP 2106.05(f). It should be noted that (1) the “recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not provide significantly more because this type of recitation is equivalent to the words “apply it”, and (2) “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, commercial interaction, or managing personal behavior or relationships or interactions between people or mathematical calculation) does not integrate a judicial exception into a practical application or provide significantly more”.
Claims 21 and 35 are substantially similar to claim 28, thus, they are rejected on similar grounds.
Claim 35 recites the additional elements of “A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:”.
Furthermore, while claims 25, 32, and 38 recite the additional elements of “use machine learning to determine whether an individual associated with transaction is the authorized user”, in view of the specification, as explained above, the broadest reasonable interpretation of these elements require a mathematical calculation. Therefore, the limitations fall into the mathematical concepts groupings of abstract ideas.
For similar reasons as explained above with regard to claim 28, under Step 2A, prong two, these additional elements are merely applying generic computer components to implement the abstract idea. Under Step 2B, when viewing the additional elements individually and in combination, the additional elements do not amount to an inventive concept amounting to significantly more than the judicial exception itself as the claimed computer-related technologies are mere tools for implementing the abstract idea as explained with regard to claim 28.
Dependent claims 22-27, 29-34, and 36-40 merely limit the abstract idea and do not recite any additional elements beyond the cited abstract idea, thus, they do not amount to significantly more. The dependent claims are abstract for the reasons presented above because there are no additional elements 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. Thus, the dependent claims are directed to an abstract idea. (Step 2B: No)
Therefore, claims 21-40 are not patent-eligible.
Response to Arguments
Applicant’s arguments filed on October 23, 2025 have been fully considered but are not persuasive for the following reasons:
With respect to Applicant’s arguments as to the § 101 rejections for now pending claims 21-40, Examiner notes the following:
Applicant argues that the claims are not directed to an abstract idea.
Examiner disagrees and notes that the claim as a whole recites a method that, under its broadest reasonable interpretation, covers collecting, analyzing, and transmitting data for the mitigation of risk and completion of a transaction. This is a fundamental economic practice of a financial transaction; a commercial interaction, such as for business relations; and managing personal behavior or relationships or interactions between people, which are certain methods of organizing human activity.
Finally, claims 21, 28, and 35 also recite “set of feature identification techniques includes: text mining and latent semantic analysis (LSA), a trend variable analysis technique, an interest diversity analysis technique, a technique using a neural network, a composite indicators analysis technique, or a clustering technique, a regression technique;” and claim 25, 32, and 38 recite “use machine learning to determine whether an individual associated with transaction is the authorized user”. This is a mathematical calculation.
Thus, the claims recite an abstract idea.
Applicant argues that the claims are integrated into a practical application. In particular, Applicant points to the Specification at paragraphs 43, 54, and 72 and argues that “claim 21 as a whole integrates into a practical application because it is directed to improvements in the technical field of transaction tracking and fraud detection by enabling efficient and secure verification, analysis, and storage of transaction data using advanced machine learning techniques including training and using a multi-layer neural network.”
Examiner notes that the stated problems of inefficient transaction verification and potential transaction fraud are not technical problems, and the claimed solution is not a technical solution. In the claim, the solution of a more efficient transaction verification and the determination of a risk score is part of the abstract idea, as it is merely involves data manipulation and analysis and the process could be completed manually or mentally or by pen and paper.
Furthermore, Examiner notes that the additional elements of the computer system - a “device”, “one or more memories”, “one or more processors coupled to the one or more memories”, “user device”, “neural network”, “machine learning”, “non-transitory computer-readable medium”, to perform the steps of “formatting”, “converting”, “analyzing”, “identifying”, “causing”, “updating”, “determining”, “training”, and “outputting”, in all steps is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claims at issue covers collecting, analyzing, and transmitting data for the mitigation of risk and completion of a transaction in a computer environment. The claims invoke the “device”, “one or more memories”, “one or more processors coupled to the one or more memories”, “user device”, “neural network”, “machine learning”, “non-transitory computer-readable medium”, to perform the steps of “formatting”, “converting”, “analyzing”, “identifying”, “causing”, “updating”, “determining”, “training”, and “outputting” merely as tools to execute the 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 mental process or mathematical calculation) does not integrate a judicial exception into a practical application. (MPEP 2106.05 (f))
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is available for review on Form PTO-892 Notice of References Cited.
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 MERRITT J HASBROUCK whose telephone number is (571)272-3109. The examiner can normally be reached M-F 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Tran can be reached on 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERRITT J HASBROUCK/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695