DETAILED ACTION
Claims 1-20 are pending in this 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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-10, 12-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US PGPUB No. 2024/0320471) [hereinafter “Chen”] in view of Qiao et al. (US PGPUB No. 2018/0300171) [hereinafter “Qiao”].
As per claim 1, Chen teaches a system for authorizing an action, the system comprising: a memory configured to store instructions and data related to authorizing the action; and a processor operably coupled to the memory and configured to: receive a plurality of authorization messages, wherein each of the plurality of authorization messages is a request for authorizing one or more different actions (Abstract, authenticating user based on request for digital transaction execution); extract predetermined data from each of the plurality of authorization messages, wherein the predetermined data includes at least identification and geolocation information associated with the one or more different actions ([0015] and [0025], user request are associated with user data including geo-location and other ID data); retrieve a convolutional neural network algorithm stored in the memory, wherein the convolutional neural network algorithm is previously trained using historic authorization messages and wherein the historic authorization messages are associated with an external device receiving permission to perform one or more different actions ([0042]-[0044], training convolution model using past data submissions received for service requests); group, by the convolutional neural network algorithm, the plurality of authorization messages based on determined common features from the extracted predetermined data, to produce a plurality of message groupings ([0120], classifying by convolution model, messages based on various data into groups such as forgery or valid); determine from the plurality of message groupings, by a long-short-term- memory algorithm (LSTM) ([0066], using a long short-term memory model to generated UA values/patterns, to train the neural network), a plurality of patterns that indicate that a particular authorization message meets a predetermined criteria, wherein the predetermined criteria indicates that a particular action associated with the particular authorization message should not be authorized ([0066], LSTM model used to determine whether a request includes a forgery see [0058]); determine from the plurality of message groupings, a plurality of patterns that indicate that a particular authorization message meets a predetermined criteria ([0120], classifying an image as a forgery), wherein the predetermined criteria indicates that a particular action associated with the particular authorization message should not be authorized ([0120], transactions associated with forgery images will not be authorized to be executed); compare the one or more new authorization messages to the plurality of patterns ([0120], using past profiles and embeddings to predict whether new transaction requests are forgeries); receive one or more first new authorization message and a second new authorization messages from the external device, wherein the first new authorization message comprises a first action and the second new authorization message comprises a second action and wherein the first action is to access the external device and the second action is to access a secure document ([0002], receiving requests for services that require authentication including banks and payment processing – bank services are interpreted to include device access requests and access to secure documents, i.e. statements, tax forms, etc.); transmit a first notification to the external device to deny the first action associated with each of the one or more first new authorization message when the one or more first new authorization messages corresponds to at least one of the plurality of patterns, wherein the first notification indicates that the first action is a fraudulent action (Abstract and [0059], a forgery prediction is an indication of a fraudulent action which is interpreted as a notification to deny the action if it is a positive determination); compare the second new authorization message to the plurality of patterns ([0039], comparing service request with various patterns including text, images, and behavior patterns see [0049]); and transmit a second notification to the external device to allow access to the secure document when the second new authorization message does not correspond to at least one of the plurality of patterns (Abstract and [0059], a forgery prediction is an indication of a fraudulent action which is interpreted as a notification to allow the action if it is a negative determination)
While Chen teaches the task of comparing of service requests to past user patterns based on one or more predetermined criteria, see Chen at [0049], Chen does not explicitly teach executing a multi dependency data processing scheduling (MDDPS) algorithm that determines an order that such tasks are performed. Qiao teaches executing a multi dependency data processing scheduling (MDDPS) algorithm that determines an order that such tasks are performed ([0022], scheduling dependency sequences in micro-tasks in a machine learning model).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen with the teachings of Qiao, executing a multi dependency data processing scheduling (MDDPS) algorithm that determines an order that such tasks are performed, to take advantage of new methods in training data sets in field of anomaly detection.
transmit a first notification to the external device to deny the first action associated with each of the one or more first new authorization message when the one or more first new authorization messages corresponds to at least one of the plurality of patterns, wherein the first notification indicates that the first action is a fraudulent action (Abstract and [0059], a forgery prediction is an indication of a fraudulent action which is interpreted as a notification to deny the action if it is a positive determination); compare the second new authorization message to the plurality of patterns ([0039], comparing service request with various patterns including text, images, and behavior patterns see [0049]); and transmit a second notification to the external device to allow access to the secure document when the second new authorization message does not correspond to at least one of the plurality of patterns (Abstract and [0059], a forgery prediction is an indication of a fraudulent action which is interpreted as a notification to allow the action if it is a negative determination).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen with the teachings of PRIOR, transmit a first notification to the external device to deny the first action associated with each of the one or more first new authorization message when the one or more first new authorization messages corresponds to at least one of the plurality of patterns, wherein the first notification indicates that the first action is a fraudulent action; compare the second new authorization message to the plurality of patterns; and transmit a second notification to the external device to allow access to the secure document when the second new authorization message does not correspond to at least one of the plurality of patterns, to take advantage of new methods in training data sets in field of anomaly detection.
As per claim 2, the combination of Chen and Qiao teaches the system of claim 1, wherein applying the MDDPS algorithm determines an order that each of the first new authorization message and the second new authorization are compared (Chen; [0049], ML model tasks like comparing authentication requests) combine with (Qiao; [0022], scheduling dependency sequences in micro-tasks in a machine learning model), to ensure values are properly determined at runtime based on interdependency.
As per claim 3, the combination of Chen and Qiao teaches the system of claim 1, wherein each of the plurality of authorization messages are sent from the external device and each of the plurality of authorization messages are related to receiving permission for the external device to perform an action (Chen; Abstract, authenticating user for processing a payment).
As per claim 5, the combination of Chen and Qiao teaches the system of claim 1, wherein the plurality of authorization messages is grouped by the neural network at least based on where the external device is located (Chen; [0057], classifying a user device based on IP address).
As per claim 6, the combination of Chen and Qiao teaches the system of claim 1, wherein the plurality of authorization messages is grouped by the neural network at least based on a numerical amount associated with each of the plurality of authorization messages (Chen; [0062], setting a threshold for the number of requests).
As per claim 7, the combination of Chen and Qiao teaches the system of claim 1, wherein the processor is further configured to update the neural network and LSTM algorithm using the one or more new authorization messages (Chen; [0085], continuously updating parameters with new updated versions using input values from requests – LSTM model is also updated with UA values based on user activities).
As per claim 8, the substance of the claimed invention is identical to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 9, the substance of the claimed invention is identical to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 10, the substance of the claimed invention is identical to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 12, the substance of the claimed invention is identical to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 13, the substance of the claimed invention is identical to that of claim 6. Accordingly, this claim is rejected under the same rationale.
As per claim 14, the substance of the claimed invention is identical to that of claim 7. Accordingly, this claim is rejected under the same rationale.
As per claim 15, the substance of the claimed invention is identical to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 16, the substance of the claimed invention is identical to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 17, the substance of the claimed invention is identical to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 19, the substance of the claimed invention is identical to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 20, the substance of the claimed invention is identical to that of claim 6. Accordingly, this claim is rejected under the same rationale.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and Qiao in further view of Yao et al. (CN-109308494-A) [hereinafter “Yao”].
As per claim 4, the combination of Chen and Qiao teaches the system of claim 1, wherein the LSTM algorithm ([0081], various neural networks can be used as a training model including an LSTM network) determines a probability that a particular pattern is associated with an unauthorized action (Chen; [0065]-[0066], using user activity values in a neural network like a LSTM model to predict if it is a forgery see Abstract).
The combination of Chen and Qiao does not explicitly teach the predetermined criteria is that the probability that the particular pattern is associated with an unauthorized action is greater than a threshold percentage (Examiner Note: this appears to be inherently how a LSTM model operates but a reference is provided to clearly demonstrate this). Yao teaches the predetermined criteria is that the probability that the particular pattern is associated with an unauthorized action is greater than a threshold percentage (Page 3 para. 10-11, using probability threshold to classify “messages/requests” as attacks).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen and Qiao with the teachings of Yao, the predetermined criteria is that the probability that the particular pattern is associated with an unauthorized action is greater than a threshold percentage, to take advantage of new methods in training data sets in field of anomaly detection.
As per claim 11, the substance of the claimed invention is identical to that of claim 4. Accordingly, this claim is rejected under the same rationale.
As per claim 18, the substance of the claimed invention is identical to that of claim 4. Accordingly, this claim is rejected under the same rationale.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered. In light of the new amendments, a new prior art reference has been introduced and cited to, Chen and Qiao.
To expedite prosecution, Examiner is open to conducting an interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Brady et al. (US PGPUB No. 2019/0391796), Park et al. (US PGPUB No. 2019/0251380), Gu et al. (US PGPUB No. 2022/0188598), Nascimento et al. (US PGPUB No. 2023/0120865), Liu et al. ("Recognition of Access Control Role Based on Convolutional Neural Network," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, 2018, pp. 2069-2074, doi: 10.1109/CompComm.2018.8780610), Abroyan ("Convolutional and recurrent neural networks for real-time data classification," 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Luton, UK, 2017, pp. 42-45, doi: 10.1109/INTECH.2017.8102422) and Wu et al. ("Spiking-Timing-Dependent Plasticity Convolutional Spiking Neural Network for Efficient Radar-Based Gesture Recognition," 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Chengdu, China, 2023, pp. 617-620, doi: 10.1109/ICICML60161.2023.10424838) all disclose various aspects of the claimed invention including training within groupings of authorization requests based on request attributes including location.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 February 14, 2026