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 Final Office Action.
In response to Examiner's communication of 5/8/2026, Applicant responded on 5/12/2026.
Claims 1-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 4 are not sufficient to overcome the claim objection set forth in the previous action. The claim objection is hereby withdrawn.
Applicant's amendments to claims 1, 2, 4, 6, 7 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 2, 4, 6, 7 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 112(a)
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Applicant submits that the term "multi-headed neural network" is a well-known term of art in the field of machine learning. One of ordinary skill in the art would understand a "multi- headed neural network" to describe a neural network architecture with multiple output branches (or "heads"), each specialized for different tasks or predictions. The specification provides ample support for this architecture paragraph [0041] of the as-filed specification describes "a dual-stage machine learning model that includes one or more sets of prediction layers" where "[t]he one or more sets of prediction layers can be trained to generate one or more account prediction values" each corresponding to different events. As-Filed Specification, paragraph [0041]. Paragraph [0042] further describes "a neural network with a single or multiple layers (e.g., fully connected layers)" with multiple "sets of prediction layers" each "corresponding to a specific event (e.g., a marriage, a divorce, an attrition, etc.)." As-Filed Specification, paragraph [0042]. Importantly, paragraph [0042] explains that "the machine learning model 122 can predict values for different types of events based on the sets of prediction layers that the machine learning model 122 uses to make the prediction." Id. The specification also describes that "[i]f the machine learning model 122 identifies the set of prediction layers based on an identification (e.g., an identification of an event) in a user input or a request, the machine learning model 122 may identify the set of prediction layers from memory based on the set of prediction layers corresponding to an identification in memory that matches the identification in the user input or the request." Id. Additionally, paragraph [0070] describes training "different sets of prediction layers" where "[e]ach machine learning model or set of prediction layers can correspond to a different account prediction value." As-Filed Specification, paragraph [0070]. Applicant submits that this architecture a neural network with multiple sets of prediction layers (heads), each corresponding to a different event type, where a specific head is selected based on an event identifier is precisely what one of ordinary skill in the art would recognize as a "multi-headed neural network." The specification describes the structural and functional characteristics of a multi-headed architecture: shared input processing with multiple output heads, each dedicated to different prediction tasks, with selective retrieval and training of specific heads based on event identification..…the claims themselves define what "multi-headed" means in this context by reciting "a multi-headed neural network comprising a plurality of sets of prediction layers each corresponding to a different type of event." This claim language, read in light of the specification's detailed description of the architecture, would convey to one of ordinary skill in the art that the inventors had possession of the claimed multi-headed neural network…” The Examiner respectfully disagrees.
As noted by Applicant, "multi-headed neural network" is a well-known term of art in the field of machine learning, One of ordinary skill in the art would understand a "multi- headed neural network" to describe a neural network architecture with multiple output branches (or "heads").
In Applicant’s specification, [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. If the machine learning model 122 identifies the set of prediction layers based on an identification (e.g., an identification of an event) in a user input or a request, the machine learning model 122 may identify the set of prediction layers from memory based on the set of prediction layers corresponding to an identification in memory that matches the identification in the user input or the request (e.g., by using the identification of the set of prediction layers from the user input or request in a look-up technique in memory). The machine learning model 122 may retrieve the identified set of prediction layers from memory and insert the transaction data into the retrieved set of prediction layers. The machine learning model 122 may execute the set of prediction layers to generate the account prediction value for the transaction data provided as input to the machine learning model 122. Accordingly, the machine learning model 122 can predict values for different types of events based on the sets of prediction layers that the machine learning model 122 uses to make the prediction.
Therefore, here, Applicant’s Specification does not expressly or inherently require “multi-headed” neural network. The specification as filed, support “single headed” multi-layer perception neural network, inputting “transaction data” into multiple prediction layers with different event probability distributions to generate a “single headed” output of “account prediction value”. Thus, Applicant’s Specification does not expressly or inherently require “multi-headed” neural network.
Examiner invites Applicant to schedule an interview with the Examiner to expedite the prosecution of the present application at Applicant’s convenience.
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. If the machine learning model 122 identifies the set of prediction layers based on an identification (e.g., an identification of an event) in a user input or a request, the machine learning model 122 may identify the set of prediction layers from memory based on the set of prediction layers corresponding to an identification in memory that matches the identification in the user input or the request (e.g., by using the identification of the set of prediction layers from the user input or request in a look-up technique in memory). The machine learning model 122 may retrieve the identified set of prediction layers from memory and insert the transaction data into the retrieved set of prediction layers. The machine learning model 122 may execute the set of prediction layers to generate the account prediction value for the transaction data provided as input to the machine learning model 122. Accordingly, the machine learning model 122 can predict values for different types of events based on the sets of prediction layers that the machine learning model 122 uses to make the prediction.
[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. 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 PO HAN MAX LEE whose telephone number is (571)272-3821. The examiner can normally be reached on Mon-Thurs 8:00 am - 7:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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