DETAILED ACTION
This Office Action is in response to the communication filed on 10/29/2025.
Claims 1-5 were withdrawn due to a restriction requirement and claims 6-8 were elected.
Claims 6-8 are pending.
Claims 6 and 8 have been amended.
Claims 6-8 are rejected.
The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
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 .
Election/Restrictions
Applicant’s elected without traverse was indicated in a telephonic interview with applicant’s representative Michael Roby on 8/14/2025.
Claim Objections
Claim objections are withdrawn due to Applicant’s amendments.
Claim Rejections - 35 USC § 112
Claim rejections under 112 are withdrawn due to Applicant’s amendments
Double Patenting
The nonstatutory double patenting rejection remans and will be withdrawn upon filing of a terminal disclaimer.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of co-pending Application No. 17/107,299 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other. The difference between 324’ and 299’ is that 299’ outputs the result set. It would have been obvious to output results if you calculate them. Displaying the results yields the expected result of providing the result to a user.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant application 18/040,536
Co-pending application 18/005,924
Claim 6: A method to prevent capturing of an Al module in an Al system, comprising:
receiving input data from at least one user through an input interface;
transmitting the input data through a blocker module to the Al module;
computing a first output data by the Al module executing a first model based on the input data;
pre-processing the input data by a submodule to obtain at least one subset of the input data;
processing the input data and said at least one subset of the input data by the submodule to identify an attack vector from the input data; and
sending identification information of the attack vector to an information gain module.
Claim 6: A method to prevent capturing of an Al module in an Al system, said method comprising the following steps
receiving input data from at least one user through an input interface
computing a first output data by the Al module executing a first model based on the input data
pre-processing input data by a submodule to obtain at least one subset of the input data
processing input data and said at least one subset of the input data by a submodule to identify an attack vector from the input data
the identification information of the attack vector is sent to the information gain module
Claim 7: The method to prevent the capturing of the Al module in the Al system as claimed in claim 6, wherein preprocessing the input data comprises
transposing the input data to obtain the at least one subset of the input data.
Claim 7: The method to prevent capturing of an Al module in an Al system as claimed in claim 6, where preprocessing the input data comprises
modifying the fidelity of the input data to obtain at least one subset of the input data
Claim 8: The method to prevent the capturing of the Al module in the Al system as claimed in claim 6, wherein processing the input data and said at least one subset of the input data further comprises:
executing at least two models with the input data and said at least one subset, one of said at least two models is the first model;
comparing the outputs received on execution of said at least two models; and
determining the input data as the attack vector based on the comparison.
Claim 8: The method to prevent the capturing of the Al module in the Al system as claimed in claim 6, wherein the processing the input data and the said at least one subset of the input data further comprises
executing at least two models with the input data and the said at least one subset, one of the at least two models is the first model;
comparing the outputs received on execution of the said at least two models; and
determining the input data as the attack vector based on the comparison.
Co-pending Application 18/005,924 does not explicitly teach transmitting the input data through a blocker module to the Al module and transposing the input data.
However, in the same field of endeavor Lee teaches transmitting the input data… to the Al module; (Lee [Fig. 6]; [0136] teaches data set may be provided as input to a cognitive computing operation engine that processes the labeled data set to perform a cognitive operation)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the invention of Co-pending application 18/005,924 with the transmitting the input data through a blocker module to the Al module in Lee because it would allow for performing a cognitive operation using AI model.
However, in the same field of endeavor Parandehgheibi teaches transposing input data in order to perform processing (Parandehgheibi [0109] teaches pre-processing by transposing (encoding/decoding/compression/parallelism) the input data).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the invention of Co-pending application 18/005,924 with the transposing Parandehgheibi because it would allow for raw data may be processed or normalized to a suitable form to populate a vector or other appropriate data structures.
Response to Arguments
Applicant argues that the proposed combinations do not arrive at the limitations of the respective claims. Examiner disagrees. Applicant's arguments filed 10/29/2025 have been fully considered but they are not persuasive.
Applicant argues (Remarks page 8) that the proposed combination does not teach “… obtain… subset of input data” Applicant argues that Parandehgheibi transforms raw data rather than obtain a subset of the raw data.
Examiner disagrees because Parandehgheibi teaches that “raw data may be processed… into a suitable form… for representing a flow” (transformation) and further teaches that the raw data is processed/grouped/clustered into corresponding different “flows” (subsets). The “flows” are subsets of raw data.
Applicant argues (Remarks page 8) that the proposed combination does not teach “… identify an attack vector” and “send (information identifying the attack vector) to an information gain module (AI system)”
Examiner disagrees because Jimenez teaches “… identify an attack vector” “In step 334, the method 300 comprises receiving from the manager configuration information for the Attack Vector data object, the configuration information updating a value for a Resource or Resources in the Attack Vector data object. The updated Resource values may for example include values for the Attack method Resource” and “ In step 336, the method 300 comprises identifying, on the basis of the updated value or values in the Attack Vector data Object, the message from the entity as an attack”, and
Examiner disagrees because Jimenez teaches sending attack vector information to update firewall policies and Lee teaches using machine learning (AI system) and Lee teaches information gain module (AI system). (for further context on how the machine learning in Lee teaches information gain, see thresholds and scoring in Lee [0129 - 0135] and instant application [0033])
Therefore, and in response to arguments presented on page 9 of Remarks labeled “additionally”, Lee in view of Jimenez and Parandehgheibi teaches: “identifying an attack vector (see D(B)(I)) by processing input data and a subset of the input data (see D(A)(i))”.
Applicant argues (Remarks page 9) that claims 7-8 are allowable based on dependency. Examiner disagrees because the independent claims are not allowable.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (U.S. 20190095629), in view of Jimenez (U.S. 20210367926) and in further view of Parandehgheibi (U.S. 20160359740).
Regarding claim 6,
Lee discloses: A method to prevent capturing of an Al module in an Al system, comprising:
receiving input data from at least one user through an input interface; (Lee [Fig. 6-610]; [0122]; [0136] As shown in FIG. 6, the operation starts by receiving an input data set)
transmitting the input data… to the Al module; (Lee [Fig. 6]; [0136] teaches data set may be provided as input to a cognitive computing operation engine that processes the labeled data set to perform a cognitive operation)
computing a first output data by the Al module executing a first model based on the input data; (Lee [Fig. 6-610, 6-620]; [0136] The input data set is processed by a trained model to generate an initial set of output values)
…an information gain module. (Lee [0081] Enable decision making at the point of impact (contextual guidance))
Lee does not explicitly disclose: transmitting the input data through a blocker module… ;
pre-processing the input data by a submodule to obtain at least one subset of the input data;
processing the input data and the at least one subset of the input data by the submodule to identify an attack vector from the input data; and
sending identification information of the attack vector…
However, in the same field of endeavor Jimenez discloses:
transmitting the input data through a blocker module… (Jimenez [Fig. 5]; [0093-0096] the firewall to block the attack and may communicate those policy changes to the constrained device by updating values in resources of an Attack Vector data object configured on the constrained device. The policy changes may be communicated to the firewall via a PCP request from the constrained device to the firewall)
processing the input data… by the submodule to identify an attack vector from the input data; and (Jimenez [Fig. 3b 332-346]; [0080-0083] teaches processing input data to identify an attack vector)
sending identification information of the attack vector… (Jimenez [Fig. 3b 338-344]; [0080-0083] sending attack vector information downstream)
Lee and Jimenez are analogous art because they are from the same field of endeavor of network security.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Lee and Jimenez before him or her, to modify the method of Lee to include the blocker of identified attack vectors of Jimenez because it will allow for attacks, which conventional firewall security rules are not designed to prevent, to be prevented.
The motivation for doing so would be [“ attacks would have limited impact on non-constrained devices, and consequently conventional firewall security rules are not designed to prevent such attacks, leaving IoT deployments potentially vulnerable to attack, even when deployed behind a firewall.”] (Paragraph 0004 by Jimenez)].
Therefore, it would have been obvious to combine Lee and Jimenez to obtain the invention as specified in the instant claim.
Lee in view of Jimenez does not explicitly disclose:
pre-processing the input data by a submodule to obtain at least one subset of the input data;
processing the input data and the at least one subset of the input data by the submodule to identify an attack vector from the input data
However, in the same field of endeavor Parandehgheibi teaches
pre-processing the input data by a submodule to obtain at least one subset of the input data; (Parandehgheibi [0068] The next stage of the data pipeline 300 is pre-processing 304 in which the raw data may be processed or normalized to a suitable form to populate a vector or other appropriate data structure)
processing the input data and the at least one subset of the input data by the submodule to identify an attack vector from the input data (Parandehgheibi [0068] The next stage of the data pipeline 300 is pre-processing 304 in which the raw data may be processed or normalized to a suitable form to populate a vector or other appropriate data structure)
Lee, Jimenez and Parandehgheibi are analogous art because they are from the same field of endeavor attack prevention.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Lee, Jimenez and Parandehgheibi before him or her, to modify the method of Lee and Jimenez to include the pre-processing of Parandehgheibi because it will raw data to be converted into a suitable format for further processing.
The motivation for doing so would be “pre-processing in which the raw data may be processed or normalized to a suitable form to populate a vector or other appropriate data structure for representing a flow.” (Paragraph 0068 by Parandehgheibi)].
Therefore, it would have been obvious to combine Lee, Jimenez and Parandehgheibi to obtain the invention as specified in the instant claim.
Regarding claim 7,
Lee, Jimenez and Parandehgheibi discloses: The method to prevent the capturing of the Al module in the Al system as claimed in claim 6, wherein preprocessing the input data comprises
transposing the input data to obtain the at least one subset of the input data. (Parandehgheibi [0109] teaches pre-processing by transposing (encoding/decoding/compression/parallelism) the input data)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Jimenez and Parandehgheibi for similar reasons as cited in claim 6.
Regarding claim 8,
Lee and Jimenez in view of Parandehgheibi discloses: The method to prevent the capturing of the Al module in the Al system as claimed in claim 6, wherein processing the input data and the at least one subset of the input data further comprises:
executing at least two models with the input data and the at least one subset of the input data, one of the at least two models is the first model; (Parandehgheibi [Fig. 3]; [0068]; [Fig. 4]; [0080-0081]; [Fig. 5]; [0094] teach training and using a plurality of models based on inputted data and/or subsets of that data)
comparing the outputs received on execution of the at least two models; and (Parandehgheibi [0021]; [0071]; [0096-0097]; [0110]; teach comparing data to other data in order to label the data as a potential attack on the network)
determining the input data as the attack vector based on the comparison. (Parandehgheibi [0021]; [0071]; [0096-0097]; [0110]; teach comparing data to other data in order to label the data as a potential attack on the network)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Jimenez and Parandehgheibi for similar reasons as cited in claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Lee 12/12/2011 (US 20120159622 ) teaches A method for generating an adaptive security model includes: generating an initial security model with respect to data input via an Internet during a learning process; and continuously updating the initial security model by applying characteristics of the input data during an online process. Said generating an initial security model includes: matching the input data with a unit having a weight vector with distance closest to the input data using a first unsupervised algorithm; generating a map composed of weight vectors of units; and performing a second unsupervised algorithm using the weight vectors forming the map as input values to partition an attack cluster.
Lou 7/17/2019 (US 11689549) teaches Balancing the observed signals used to train network intrusion detection models allows for a more accurate allocation of computing resources to defend the network from malicious parties. The models are trained against live data defined within a rolling window and historic data to detect user-defined features in the data. Automated attacks ensure that various kinds of attacks are always present in the rolling training window. The set of models are constantly trained to determine which model to place into production, to alert analysts of intrusions, and/or to automatically deploy countermeasures. The models are continually updated as the features are redefined and as the data in the rolling window changes, and the content of the rolling window is balanced to provide sufficient data of each observed type by which to train the models. When balancing the dataset, low-population signals are overlaid onto high-population signals to balance their relative numbers.
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS A CARNES whose telephone number is (571)272-4378. The examiner can normally be reached Monday-Friday.
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, Shewaye Gelagay can be reached at (571) 272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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THOMAS A. CARNES
Examiner
Art Unit 2436
/THOMAS A CARNES/ Examiner, Art Unit 2436 /SHEWAYE GELAGAY/Supervisory Patent Examiner, Art Unit 2436