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 .
Response to Amendment
2. The Amendment filed on September 05, 2025 has been entered. Claims 1, 4, 6, 8, 10, 12, and 15 have been amended. No claims have been added or cancelled. Thus, claims 1-20 are pending and rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 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.
In sum, claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and do not include an inventive concept that is something “significantly more” than the judicial exception under the January 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 8-14), a machine (claims 1-7) and a manufacture (claims 15-20), where the machine and manufacture are substantially directed to the subject matter of the process. (See, e.g., MPEP §2106.03). Therefore, we proceed to step 2A, Prong 1.
Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability. Here, the claims recite the abstract idea of processing a transaction after classifying a transaction using vectors by:
receiving,…,transaction data associated with a transaction conducted between a first entity and a second entity,…, associated with the second entity, wherein the transaction data is in a first modality;
retrieving multimedia data associated with the transaction based on the transaction data, wherein the multimedia data is in a second modality different from the first modality;
retrieving, from a,…,associated with the second entity, data associated with the,…,through which the transaction is conducted, wherein the data represents,…, and content presented on the…;
converting, using a first transformer, the transaction data into a first vector;
converting, using a second transformer, the multimedia data into a second vector;
converting, using a third transformer, the data associated with the,…,into a third vector;
generating a first combined vector based on the first vector, the second vector, and the third vector;
determining one or more hyperparameters for a machine learning model based on the second modality of the multimedia data retrieved from the first server, wherein the machine learning model was trained using a plurality of training vectors, wherein the plurality of training vectors comprises different training vectors that correspond to different modalities, wherein different hyperparameters were used to configure the machine learning model when the different training vectors corresponding to the different modalities were provided to the machine learning model for training the machine learning model, wherein the machine learning model is configured to accept the first combined vector as an input for classifying the transaction, and wherein the one or more hyperparameters specify different weights for different portions of the first combined vector representing different portions of the multimedia data;
configuring the machine learning model based on the one or more hyperparameters;
classifying the transaction using the machine learning model, wherein the classifying comprises providing the first combined vector to the machine learning model, and obtaining an output, based on the first combined vector, from the machine learning model; and
processing the transaction based on the classifying of the transaction.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: the category of certain methods of organizing human activity, which includes fundamental economic practices or principles and commercial or legal interactions (e.g., processing a transaction after classifying a transaction using vectors).
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). Therefore, the claim is directed to an abstract idea. Independent claims 1 and 15 are nearly identical to independent claim 8 so the same analysis applies to those two claims as well. Claim 1 includes additional elements such as a “memory” and “processor” which are being used to implement the abstract idea noted in claim 8.
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as: a “device,” “interface,” “server,” and “system,” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming. (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. (See, e.g., MPEP §2106.05 I.A.); (see also, paragraph [0031] of the specification).
Dependent claims 2–7, 9–14, and 16–20 have all been considered and do not integrate the abstract idea into a practical application. Dependent claims 2 and 17 both recite nearly identical limitations that further define the abstract idea noted in claim 8 in that they describe how the hyperparameters are determined based on analysis of the non-textual data. This is simply using a generic machine learning model using input data and then outputting the data to make a decision. Dependent claims 3 and 18 both recite nearly identical limitations that further define the abstract idea noted in claim 8 in that they describe determining a composite output based on multiple outputs and basing the classification on this. Dependent claims 4 and 19 both recite nearly identical limitations that further define the abstract idea noted in claim 8 in that they describe classifying each of the images and determining at least one image being an outlier and excluding it in conversion into a secondary vector. This is analyzing an image to determine if it is similar or different from the group to then exclude it from further data analysis. Dependent claim 5 recites limitations that further define the abstract idea noted in claim 8 in that it describes using weights to non-textual data and text data to determine a first output. Dependent claim 6 recites limitations that further define the abstract idea noted in claim 8 in that it describes what the text data is (“description of an item”). Dependent claims 7 and 20 both recite nearly identical limitations that further define the abstract idea noted in claim 8 in that they describe denying the transaction if it is classified to be a certain (“particular”) classification. Dependent claim 9 recites limitations that further define the abstract idea noted in claim 8 in that it describes obtaining a first putout from the machine learning model based on the combined vector in which the classifying is based on this first output. This is a generic use of a machine learning model to obtain an output of data which is then classified. Dependent claim 10 recites limitations that further define the abstract idea noted in claim 8 in that it describes receiving specific multimedia data that is taken from transaction data and then converting it to a third vector. Dependent claim 11 recites limitations that further define the abstract idea noted in claim 8 in that it describes scanning data for products on a merchant website and this data is then classified. Dependent claim 12 recites limitations that further define the abstract idea noted in claim 8 in that it describes retrieving multimedia data from a generic database (“server”) from a query. Dependent claim 13 recites limitations that further define the abstract idea noted in claim 8 in that it describes what the transaction data is (“text data”) and what the multimedia data is (“image data”). Dependent claim 14 recites limitations that further define the abstract idea noted in claim 8 in that it describes what the transformers are (“language” and “image” based transformers). Dependent claim 16 recites limitations that further define the abstract idea noted in claim 8 in that it describes that the device is associated with either the merchant or the user.
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field (e.g., the field of computer coding technology is not being improved); the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., simply claiming the use of a computer and/or computer system to implement the abstract idea).
Prior Art Not Relied Upon
5. The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. (See MPEP §707.05). The Examiner considers the following reference pertinent for disclosing various features relevant to the invention, but not all the features of the invention, for at least the following reasons:
1. Srivastava et al. (U.S. Pub. No. 2020/0311467) teaches receiving an image to be classified that has embedded text and then a machine learning model is used to generate a text vector from that text data. Another machine learning model generates an image vector from image data. Although Srivastava describes generating text and image vectors similar to the current invention, Srivastava does not disclose the following limitations as part of the current invention:
“generating a first combined vector based on the first vector and the second vector;
classifying, by the computer system, the transaction using a machine learning model
based on the combined vector; and processing, by the computer system, the transaction based on the classifying of the transaction.”
These limitations discuss the features relating combining the two vectors and then processing a transaction based on a classification of the transaction based on the combined vector data. This is not found in the prior art, including Srivastava.
Response to Arguments
6. Applicant’s arguments filed on September 05, 2025 have been fully considered.
Applicant’s arguments concerning the 35 U.S.C. §101 rejection of the claims, including supposed deficiencies in the rejection, are not persuasive. Applicant first argues that “[a]pplicant respectfully submits that at least the above highlighted additional elements recited in the claims integrate the abstract idea into a practical application of providing a machine learning model framework that improves the flexibility and accuracy performance of machine learning models. (See Applicant’s Arguments, pp. 10-11). Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). There are no interactive elements featured in the claims of this application. Merely using additional data inputs to be provided to a machine learning model does not integrate the abstract idea into a practical application.
Applicant also argues that “[s]imilar to Claim 1 in Example 42, the additional elements recited in amended claim I herein enables a system to dynamically retrieve additional relevant information (independent of the types of the additional information) and provide the additional information as input to a machine learning model to perform a task. In particular, the additional information (while relevant to improving the accuracy performance of the task) would not be available to the machine learning model without using the claimed machine learning model framework. Thus, the elements as a whole provides a specific improvement over prior machine learning model systems.” (See Applicant’s Arguments, p. 12). However, retrieval of additional relevant information and providing it to a machine learning model is not a technological improvement. There is no specialized hardware or software being used in this invention. This is merely sending more data (although a different type of data) to a machine learning model. It is just data gathering in a different format that is being used to carry out a transaction. The merely addition of an electronic user interface as well as several hyperparameters used to configure the machine learning model is not a technological improvement.
Therefore, the rejection under 35 U.S.C. §101 is maintained.
Conclusion
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 Amit Patel whose telephone number is (313) 446-4902. The Examiner can normally be reached Mon - Thu 8 AM - 6 PM EST. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Matthew Gart, can be reached at (571) 272-3955. The Examiner’s fax number is (571) 273-6087. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Amit Patel/
Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696