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
The instant application having Application No. 18/202,997 is presented for examination by
the examiner. Claims 1, 19 and 20 are amended. Claims 2 and 4-7 have been cancelled. Claims 1, 3 and 8-20 have been examined.
Response to Arguments
The previous rejection under 35 U.S.C. §101 has been removed with the amendment claim limitations.
Applicant’s arguments with respect to claim(s) 1, 19 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.-Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.С. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 3 and 18 are rejected under 35 U.S.C. §112(d) or pre-AIA 35 U.S.C. §112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claims 3 and 18 each depend on claim 2. However, claim 2 has been previously canceled and is no longer pending in the application. As a result, claims 3 and 18 fail to reference a preceding pending claim as required by 35 U.S.C. §112(d). Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form by referencing a currently pending claim such as claim 1, rewrite the claim(s) in independent form incorporating all necessary limitations, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claims 1, 8-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guim (US 20210328934 A1), in view of Ford (US 20200257823 A1) and Lebda (US 6,611,816 B2).
Regarding Claim 1
Guim teaches:
A continuously active edge data filter computer program product, the computer program product comprising executable instructions stored on non-transitory memory of a computer system, the executable instructions when executed by a processor on the computer system:
intercept incoming data before the incoming data is downloaded by a network configured to analyze and store mortgage applications (Guim ¶96–100 and ¶130–134 : teach intercepting incoming data packets at the network edge using traffic-interceptor circuitry 620 before forwarding to downstream network services, identifying associations via packet headers and stream parameters, treating packets/messages as discrete data portions, sending intercepted packets into the ML relevance-model.), wherein the incoming data includes two or more quanta of data (Guim ¶96: teaches that inbound streams may include multiple discrete data pieces by disclosing associations of “two or more data elements or data portions,” where those portions can be packets/messages in formats such as “binary data format, comma delimited data, tab delimited data, SQL structures, an executable, etc.,” which shows incoming data comprising two or more quanta.);
store the incoming data in a safe zone (Guim ¶96–100, 136, and 183 teach intercepting inbound packets at the network edge and staging them into a trusted execution environment or sandbox region before ML relevance model execution and prior to downstream network handoff, providing direct support for storing intercepted incoming data in an isolated, protected safe-zone buffer before network download or ingestion.);
analyze, in the safe zone, the incoming data and metadata of the incoming data to determine when one or more quanta of the incoming data is relevant to the network (Guim ¶84–85 and 136 teach executing ML/AI relevance classification on incoming packets and packet parameters inside a sandbox/TEE prior to downstream network handoff, supporting analysis of incoming data portions and associated stream parameters (metadata-equivalent attributes) to determine which of multiple ingress data quanta are relevant before the network ingests or downloads them.);
when one or more quanta of data is relevant to the network, release, from the safe zone, the one or more quanta of data that is relevant to the network (Guim ¶96–100, ¶136, ¶160, 182–183: teaches prior to ML relevance model execution, extracting stream content as metadata-equivalent attributes, invoking AI/ML relevance models on individual or grouped ingress data quanta, and thereafter gated, selective forwarding only of packets that exceed or satisfy intercept thresholds and stream/model association tests, out of the sandbox and into downstream network ingestion paths.);
evaluate the released one or more quanta of data by the network;
wherein the analysis applies one or more artificial intelligence/machine learning ("AI/ML") algorithms to determine whether the incoming data is relevant to the network by evaluating, on a dynamic numerical scale, with a larger value more likely to be relevant, one or more of:
an origin source of the incoming data;
a geographic origin of the incoming data:
a type of the incoming data;
an originating time of the incoming data;
a personal origin of the incoming data; contents of the incoming data; and
the network (Guim ¶¶37, 76, 84–88, 96–100, 105, 107, 136, and 182–183: teach executing AI/ML relevance models on intercepted data inside a trusted execution environment (TEE) or sandbox (¶76), generating dynamically adjusted priority values based on model outputs wherein higher relevance corresponds to an increased priority value consistent with a larger value being more likely to be relevant (¶¶85, 87–88), and evaluating one or more of: an origin source of the incoming data corresponding to the source service or appliance identifier including IP address, port number, and MAC address (¶¶37, 105); a type of the incoming data corresponding to the data type extracted from packet payloads or stream-definition parameters (¶¶37, 107); contents of the incoming data corresponding to payload content, header analysis, and bit stream comparison (¶¶37, 85–86); and the network corresponding to the target service or appliance and stream/model association parameters (¶¶37, 96–100)); and
the one or more AI/ML algorithms automatically adjust relevancy determinations and the numerical scale based on:
iteration (Guim ¶118: teaches that the data relevance model generator circuitry 640 re-trains the data relevance models 678 based on training data implemented by one or more intercepted data packets 672 or portions thereof, including data stream parameters, headers, and data payloads; Guim ¶122: further teaches that the data relevance model generator circuitry 640 invokes supervised training by iterating over combinations of select parameters for the data relevance models 678 to reduce model error, supporting an iterative process that automatically adjusts the relevance models and corresponding priority values as additional incoming data is intercepted);
feedback from the computer system (Guim ¶128: teaches that output of the deployed data relevance models 678 may be captured and provided as feedback, and by analyzing the feedback an accuracy of the deployed models can be determined; if the feedback indicates that the accuracy of the deployed model is less than a threshold or other criterion, training of an updated model is triggered using the feedback and an updated training data set to generate an updated deployed model, thereby automatically adjusting relevancy determinations based on system-generated feedback); and
feedback from an administrator (Guim ¶124: teaches that the data relevance model generator circuitry 640 may perform re-training in response to override(s) by a user of the edge resource, supporting automatic adjustment of relevancy determinations and the corresponding priority value scale based on administrator-supplied feedback).
Guim does not teach the following limitation: “when one or more quanta of data is not relevant to the network, delete the one or more irrelevant quanta of data.” Specifically, Guim teaches a prioritization forwarding system wherein data determined to be of lower relevance is transmitted at a reduced priority rather than deleted from the system, However, in an analogous art, Ford discloses deleting data determined to be irrelevant from an incoming event stream. Specifically, Ford teaches:
when one or more quanta of data is not relevant to the network, delete the one or more irrelevant quanta of data (Ford ¶70: teaches that event enrichment may include deleting certain data associated with certain incoming events in the event stream, where certain data associated with various incoming events may be determined to be irrelevant to analyzing the probability distributions of certain interrelated event features; Ford ¶73: further discloses that data cleansing operations may include identifying incomplete, incorrect, inaccurate, or irrelevant data elements and then replacing, modifying, or deleting certain data elements that fail to meet certain data use parameters).
Given the teaching of Ford, a person having ordinary skill in the art before the effective filing date of the claimed invention would have recognized the desirability of modifying the teachings of Guim to affirmatively delete incoming data quanta determined to be irrelevant, rather than merely deprioritizing or not forwarding such data. Ford teaches that when incoming event data is determined to be irrelevant, it is deleted as part of standard data enrichment and cleansing operations applied to incoming event streams (¶¶70, 73). Incorporating such deletion of irrelevant data into Guim’s edge filtering system would reduce unnecessary storage overhead within the safe zone, improve processing efficiency, and ensure that irrelevant data does not persist after a relevance determination is made. It would have been obvious to a person of ordinary skill in the art to implement deletion of irrelevant incoming data quanta as taught by Ford in combination with the edge data filtering and relevance determination system of Guim as this represents a predictable application of known data management and event stream processing techniques to achieve predictable results.
Guim and Ford do not teach the following limitation “wherein data is relevant to the network when it is related to mortgage applications”
However, In an analogous art, Lebda discloses a mortgage application system/method that includes:
and wherein data is relevant to the network when it is related to mortgage applications (Lebda Column 4, Line 53 – Column 5, Line 6: discloses that the system process and routes borrower-submitted credit application data, including mortgage application, for evaluation by lending institutes based on mortgage-specific criteria.).
Given the teaching of Lebda, a person having ordinary skill in the art before the effective filing date of the claimed invention would have recognized the desirability of modifying the teachings of Guim and Ford to implement a system that evaluates released data by the network and defines relevance as data related to a mortgage application. Lebda teaches evaluating credit application data again lender-specific criteria and filtering mortgage application data for processing based on mortgage specific rules. This inherently defines relevance in terms of mortgage content and shows network evaluation of released data. Implementing such logic would have been obvious design choice for automating eligibility decision in finical data processing system (Lebda Column 4, Line 53 – Column 5, Line 6).
Regarding Claim 8
Guim teaches:
The edge data filter computer program product of claim 1 wherein the analysis compares the incoming data to data already on the network (Guim ¶145 teaches executing a comparison-based data-relevance model at an edge node that compares bit streams extracted from incoming data packets to known/stored bit streams accessible to networked edge resources, providing support for analysis that compares incoming data quanta to data already resident in a network-accessible store.).
Regarding Claim 9
Guim teaches:
The edge data filter computer program product of claim 1 wherein the analysis applies one or more filtering rules to determine whether the incoming data is relevant to the network, said one or more filtering rules being supplied by an administrator (Guim ¶28, 40, and 96–100: teach applying administrator-configurable edge filtering and intercept-threshold rules at a traffic-interceptor stage implemented in non-transitory memory of a computer system, where those policy thresholds and stream definition parameters gate relevance and selective forwarding decisions prior to network ingestion, consistent with filtering rules supplied by an administrator that determine relevance of incoming data quanta at the edge).
Regarding Claim 10
Guim teaches:
The edge data filter computer program product of claim 1 wherein the instructions are trained with a training set of data (Guim ¶142: teaches creating relevance ML models 678 by training them on real intercepted data packets 672 or packet portions stored in non-transitory edge memory, and loading those trained relevance models for execution via acceleration circuitry 660 prior to downstream network handoff, which supports edge-deployed instructions/models trained with a training set of data.).
Regarding Claim 11
Guim teaches:
The edge data filter computer program product of claim 10 wherein the analysis iterates when intercepting additional incoming data (Guim ¶118: teaches that the data relevance model generator circuitry 640 re-trains the relevance models 678 based on newly intercepted incoming data packets 672 or portions thereof, supporting an iterative analysis process that updates or re-applies relevance evaluation as additional incoming data is intercepted.).
Regarding Claim 12
Guim teaches:
The edge data filter computer program product of claim 1 wherein the analysis analyzes metadata of the incoming data to determine when one or more quanta of the incoming data is relevant to two or more networks (Guim ¶21, 49–54: teach edge cloud infrastructure capable of analyzing stream parameters and data source attributes (metadata) to determine appropriate routing or relevance across multiple network types and service domains. These determinations occur in a multi-tenant, multi-network environment, where data is routed to the correct service or network layer based on metadata-derived insights such as origin, device type, or application class, thereby teaching analysis of metadata to determine when data is relevant to two or more networks.).
Regarding Claim 13
Guim teaches:
The edge data filter computer program product of claim 12 wherein when one or more quanta of data is relevant to one or more of the two or more networks, the instructions release the one or more relevant quanta of data to the one or more of the two or more networks (Guim ¶21, 50–54, 96, 118–119: collectively teach that after metadata-based relevance determination, the system selectively releases or routes the relevant data quanta into downstream ingestion paths or appropriate network domains. The forwarding occurs only when the data meets intercept thresholds or model relevance requirements, and it may be distributed across multiple networks or services based on application type, service orchestration, and dynamic tenant/network needs. This teaches releasing one or more quanta to one or more of the relevant networks.).
Regarding Claim 14
Guim teaches:
The edge data filter computer program product of claim 13 wherein the instructions route relevant one or more quanta of data to each network of the one or more networks (Guim ¶21, 50–54, 96, and 136: teach that in a multi-access, multi-tenant edge architecture, relevant data quanta are routed to each appropriate network or service based on stream association and service orchestration. Routing logic is dynamically determined and supports delivery to multiple networks simultaneously if needed, satisfying the requirement to route relevant data to each of the identified networks.).
Regarding Claim 15
Guim teaches:
The edge data filter computer program product of claim 1 wherein the instructions are configured to store all of the incoming data in a database (Guim ¶37 and 139: together teach storing incoming data packets, stream parameters, and models in a persistent datastore implemented by memory or mass storage, with support for structured formats including SQL structures and binary data.).
Regarding Claim 16
Guim teaches:
The edge data filter computer program product of claim 1 wherein the network is an internal network (Guim ¶53: teaches that the edge cloud 110 is a type of internal network, composed of edge nodes and infrastructure that sit between endpoints and core/provider networks.).
Regarding Claim 17
Guim teaches:
The edge data filter computer program product of claim 16 wherein the internal network is configured to perform an evaluation of data received by the internal network (Guim ¶159: teaches that the internal network performs evaluations on received data packets (e.g., checking stream associations and priority), and makes transmission decisions based on those evaluations.).
Regarding Claim 20
Claim 20 is directed to a method corresponding to the computer-implemented system in claim 1. Claim 20 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Claims 3 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guim (US 20210328934 A1), Ford (US 20200257823 A1) and Lebda (US 6,611,816 B2) as applied to claim 1 above, and in further view of Yekhanin (US 9,244,761 B2).
Regarding Claim 3
Guim, Ford and Lebda do not teach the following limitation “wherein the safe zone is outside of the network”
However, in an analogous art, Yekhanin discloses a storing data in an isolated safe zone. Yekhanin (Column 1, Lines 39-41 and Column 5, Lines 15-18): highlights that these storage zones include geographically distinct areas, such as data centers or regions, separate from the primary network infrastructure.
Given the teaching of Yekhanin, a person having ordinary skill in the art before the effective filing date of the claimed invention would have recognized the desirability of modifying the teachings of Guim, Ford and Lebda by storing data in safe zones outside the primary network infrastructure to ensure durability and fault tolerance. Yekhanin discloses that storage zones can include geographically distinct areas, such as data centers or regions, which are separate from the primary network infrastructure. This separation provides additional protection against network failures and large scale outages. It would have been obvious to implement a method where the safe zone is located outside the network, aligning with Yekhanin’s teaching of using geographically distinct storage zones to enhance data security and reliability (Yekhanin Column 1, Lines 39-41 and Column 5, Lines 15-18).
Regarding Claim 18
Guim, Ford and Lebda do not teach the following limitation “wherein the safe zone is part of the network but is isolated from a remaining portion of the network”
However, in an analogous art, Yekhanin discloses a method for storing data in an isolated safe zone. Yekhanin (Column 16, Lines 3-18) describes zones within a network, such as data centers or geographic regions, that are part of the overall network but function independently or in isolation. These zones, including subzones or fault domains are isolated from the remaining network while still being a part of it.
Given the teaching of Yekhanin, a person having ordinary skill in the art before the effective filing date of the claimed invention would have recognized the desirability of modifying the teachings of Guim, Ford and Lebda by implementing a safe zone that is part of the network but isolated from its remaining portions to enhance reliability and fault tolerance. Yekhanin discloses zones within a network, such as data centers or geographic regions, that are part of the overall network but function independently or in isolation. These zones, including subzones or fault domains, are configured to store and manage data securely, providing fault tolerance and durability. The isolation ensures that disruptions in one portion of the network do not impact the functionality or security of other zones. It would have been obvious to implement a safe zone as part of the network but isolated from its remaining portions, aligning with Yekhanin’s teaching of leveraging isolated zones within a network to enhance fault tolerance, data security, and network reliability (Yekhanin Column 16, Lines 3-18).
Claims 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guim (US 20210328934 A1), in view of WADHWA (US20220020026A1) and Ford (US 20200257823 A1).
Regarding Claim 19
Guim teaches:
An apparatus for an edge data filter, the apparatus comprising: a central server, the central server including: a server communication link; a server processor; and a server non-transitory memory configured to store at least: a server operating system; and an edge data filter; and one or more network nodes, each network node comprising: a node communication link; a node processor; and a node non-transitory memory configured to store at least a node operating system (Guim ¶96–100 and ¶130–134 : teach intercepting incoming data packets at the network edge using traffic-interceptor circuitry 620 before forwarding to downstream network services, identifying associations via packet headers and stream parameters, treating packets/messages as discrete data portions, sending intercepted packets into the ML relevance-model.),(Guim ¶96–100 and ¶130–134 : teach intercepting incoming data packets at the network edge using traffic-interceptor circuitry 620 before forwarding to downstream network services, identifying associations via packet headers and stream parameters, treating packets/messages as discrete data portions, sending intercepted packets into the ML relevance-model.); stores the incoming data in a safe zone (Guim ¶96–100, 136, and 183 teach intercepting inbound packets at the network edge and staging them into a trusted execution environment or sandbox region before ML relevance model execution and prior to downstream network handoff, providing direct support for storing intercepted incoming data in an isolated, protected safe-zone buffer before network download or ingestion.); analyzes in the safe zone, by applying one or more artificial intelligence/machine learning ("AI/ML") algorithms, the incoming data and metadata of the incoming data to determine when one or more quanta of the incoming data is relevant to one or more network nodes by evaluating, on manually adjusted numerical scale, with a larger value more likely to be relevant, one or more of: an origin source of the incoming data; type of the incoming data; contents of the incoming data; and the network (Guim ¶¶37, 76, 84–88, 96–100, 105, 107, 136, and 182–183: teach executing AI/ML relevance models on intercepted data inside a trusted execution environment (TEE) or sandbox (¶76), generating dynamically adjusted priority values based on model outputs wherein higher relevance corresponds to an increased priority value consistent with a larger value being more likely to be relevant (¶¶85, 87–88), and evaluating one or more of: an origin source of the incoming data corresponding to the source service or appliance identifier including IP address, port number, and MAC address (¶¶37, 105); a type of the incoming data corresponding to the data type extracted from packet payloads or stream-definition parameters (¶¶37, 107); contents of the incoming data corresponding to payload content, header analysis, and bit stream comparison (¶¶37, 85–86); and the network corresponding to the target service or appliance and stream/model association parameters (¶¶37, 96–100).); and when one or more quanta of data is relevant to one or more network nodes, releases, from the safe zone, the one or more quanta of data that is relevant to the one or more network nodes (Guim ¶96–100, ¶136, ¶160, 182–183: teaches prior to ML relevance model execution, extracting stream content as metadata-equivalent attributes, invoking AI/ML relevance models on individual or grouped ingress data quanta, and thereafter gated, selective forwarding only of packets that exceed or satisfy intercept thresholds and stream/model association tests, out of the sandbox and into downstream network ingestion paths.).
Guim does not teach the following limitation “wherein each of the one or more network nodes is configured to store and analyze data for know your customer" ("KYC") anti-money laundering requirements; wherein data is relevant to the network when it is related to KYC requirements”. However, in an analogous art, WADHWA discloses an KYC system/method that includes: WADHWA (¶2-4, 26-31 AND 42) discloses that the system stores and analyzes financial data including KYC information using ML models to detect potential money laundering transactions.
Given the teaching of WADHWA, a person having ordinary skill in the art before the effective filing date of the claimed invention would have recognized the desirability of modifying the teachings of Guim by implementing a system where each network node stores and analyzes data for KYC compliance and define relevance as data related to KYC requirements. WADHWA teaches storing KYC data and using ML to analyze user data to detect money laundering thereby defining relevance in terms of KYC and demonstrating node-based analysis of compliance data (WADHWA ¶2-4, 26-31 AND 42).
Guim and WADHWA do not teach the following limitation “and when one or more quanta of data is not relevant to one or more network nodes, delete the one or more irrelevant quanta of data.” However, in an analogous art, Ford discloses deleting data determined to be irrelevant from an incoming event stream. Specifically, Ford teaches:
and when one or more quanta of data is not relevant to one or more network nodes, delete the one or more irrelevant quanta of data (Ford ¶70: teaches that event enrichment may include deleting certain data associated with certain incoming events in the event stream, where certain data associated with various incoming events may be determined to be irrelevant to analyzing the probability distributions of certain interrelated event features; Ford ¶73: further discloses that data cleansing operations may include identifying incomplete, incorrect, inaccurate, or irrelevant data elements and then replacing, modifying, or deleting certain data elements that fail to meet certain data use parameters.).
Given the teaching of Ford, a person having ordinary skill in the art before the effective filing date of the claimed invention would have recognized the desirability of modifying the teachings of Guim and WADHWA to affirmatively delete incoming data quanta determined to be irrelevant, rather than merely deprioritizing or not forwarding such data. Ford teaches that when incoming event data is determined to be irrelevant, it is deleted as part of standard data enrichment and cleansing operations applied to incoming event streams (¶¶70, 73). Incorporating such deletion of irrelevant data into Guim’s edge filtering system would reduce unnecessary storage overhead within the safe zone, improve processing efficiency, and ensure that irrelevant data does not persist after a relevance determination is made. It would have been obvious to a person of ordinary skill in the art to implement deletion of irrelevant incoming data quanta as taught by Ford in combination with the edge data filtering and relevance determination system of Guim and WADHWA as this represents a predictable application of known data management and event stream processing techniques to achieve predictable results.
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 SAAD A ABDULLAH whose telephone number is (571) 272-1531. The examiner can normally be reached on Monday - Friday, 8:30am - 5:00pm, EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571) 272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAAD AHMAD ABDULLAH/Examiner, Art Unit 2431
/MICHAEL R VAUGHAN/Primary Examiner, Art Unit 2431
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