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 .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 12/31/2025, Applicant responded on 2/10/2026. Amended claim 1, 12, 18.
Claims 1-20 are pending in this application have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/10/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 12, 18 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are US Patent Publication to US20220215142A1 to Gutierrez et al., (hereinafter referred to as “Gutierrez”) in view of US Patent Publication to US20210406896A1 to Chaturvedi., (hereinafter referred to as “Chaturvedi”)
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 12, 18,
A method for selectively training a multi-headed neural network comprising a plurality of sets of prediction layers each corresponding to a different type of event, comprising:
storing, by a processor, a plurality of modifiers each corresponding to a different type of event, wherein each set of prediction layers of the plurality of sets of prediction layers has a stored identification corresponding to a respective type of event of the different types of events;
receiving, by the processor, a first profile characteristic configuration and an identification of a distribution modifier corresponding to a first type of event of the plurality of modifiers for each of a plurality of probability distributions and a first start time, each probability distribution corresponding to a different profile characteristic regarding transactions performed by an entity;
creating, by the processor, a training set comprising labeled training data identifying times in which the first type of event occurred and changes in transaction patterns between times before and after the first type of event occurred, based on a sampling of each of the plurality of probability distributions adjusted based on the first profile characteristic configuration, the distribution modifier for the probability distribution, and the first start time;
retrieving, by the processors, a first set of prediction layers of the multi-headed neural network selected from the plurality of sets of prediction layers based on the first set of prediction layers corresponding to an identification that matches the distribution modifier; and
training, by the processor using the training set, the first set of prediction layers of the multi-headed neural network to generate account prediction values for the first type of event by inserting the training set into the first set of prediction layers.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1, 12, 18.
The prior art rejection is hereby withdrawn.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1, 12, 18 recites “selectively training a multi-headed neural network comprising a plurality of sets of prediction layers”. However, Applicant’s Specification does not expressly or inherently require “multi-headed”
In order to satisfy the written description requirement, each claim limitation must be expressly or inherently supported by the disclosure. MPEP 2163 (emphasis added). "The 'written description' requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor's obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent's term. See MPEP 2163. For claims directed toward computer-implemented functions, like the presently claimed invention, "[i]f the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made." MPEP 2161.01.
Applicant’s specification discloses,
[0031] The transaction data engine 104 may comprise one or more processors that are configured to implement a multi-model architecture to generate transaction data (e.g., synthetic transaction data) for one or more transactions.
[0039] Alternatively, or in addition, in some embodiments the probability distribution adjuster 120 may generate multiple different sets of adjusted probability distributions at the same time or at nearly the same time, with each set of adjusted probability distributions comprising a probability distribution for each of the profile characteristics of the transaction data to be generated and stored in the transaction data database 128.
[0042] The machine learning model 122 may generate an account prediction value by inserting or propagating the transaction data (e.g., the input for model 122) into a set of prediction layers (e.g., such as a neural network with a single or multiple layers (e.g., fully connected layers)) corresponding to a specific event (e.g., a marriage, a divorce, an attrition, etc.). For example, if the machine learning model 122 only includes one set of prediction layers, the machine learning model 122 may retrieve the set of prediction layers from memory 116 and insert the transaction data into the retrieved set of prediction layers.
[0059] At operation 210, the data processing system determines whether to generate transaction data using multiple profile characteristic configurations. The data processing system may determine whether to generate transaction data using multiple profile characteristic configurations by identifying the period of time for which transaction data is to be generated. Alternatively, or in addition, the data processing system may determine whether to generate transaction data using multiple profile characteristic configurations by randomly sampling a probability distribution for the number of possible profile characteristic configurations for which transaction data is to be generated for a specified period of time.
[0061] Responsive to determining to generate transaction data using multiple profile characteristic configurations, at operation 212, the data processing system retrieves one or more additional profile characteristic configurations for each of the probability distributions and one or more additional start times (e.g., one or more specified dates) associated with the one or more additional profile characteristic configurations. In retrieving the additional profile characteristic configurations for each of the probability distributions, the data processing system may retrieve additional values for the particular probability distribution associated with that profile characteristic, such as a median value, a mean value, a standard deviation, minimum and maximum values, and the like, which together define a probability distribution of possible values, for example, as a normal (Gaussian) distribution of the values for the associated probability distribution (e.g., as shown in FIGS. 5A and 5B). Alternatively, or in addition, the data processing system may retrieve a custom function (e.g., range of values and associated probabilities) for one or more of the additional profile characteristic configurations.
However, the paragraph and figures does not expressly or inherently require “multi-headed neural network”, as required by claim 1, 12, 18.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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