DETAILED ACTION
This Office Action is in response to the communication filed on 12/23/2025.
Claims 1-20 are pending.
Claims 1, 9 and 16 have been amended.
Claims 1-20 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 .
Claim Objections
The claim objections have been withdrawn.
Response to Arguments
Applicant’s arguments with respect to claims 1-20, 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 regarding “after intercepting the first user device communication prior to reaching the user device, homomorphically encrypt the first user device communication as first encrypted user device communication;”.
Applicant's remaining arguments filed 12/23/2025 have been fully considered but they are not persuasive. Regarding the amended limitation which the new reference was not applied to, the arguments are not persuasive because the new limitations are rejected under 112 (see below) and do not further limit the claims.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “collocated” in claim 1 is a relative term which renders the claim indefinite. The term “collocated” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. “collocated” plainly means “located close to”, because distance thresholds for collocation is not defined in the specification one of ordinary skill would not be able to determine the physical distance threshold between processor and memory for them to be considered collocated, rendering the claim indefinite.
The limitation
“monitor a first user device communication transmitted by the user device to a first caller of interest;
intercept the first user device communication prior to reaching the user device;
after intercepting the first user device communication prior to reaching the user device, homomorphically encrypt the first user device communication as first encrypted user device communication;” renders the claim indefinite.
The phrase “intercept the first user device communication prior to reaching the user device” is ambiguous because it is not clear how the communication is being intercepted before it reaches the user device because the communication originates from the user device. Examiner is interpreting this limitation to read “intercept the first user device communication prior to reaching the first caller of interest”. For similar reasons examiner is interpreting “after intercepting the first user device communication prior to reaching the user device” to read “after intercepting the first user device communication prior to reaching the first caller of interest”. The specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shah (U.S. 20240031475), in view of Dalli (U.S. 20220198254) and in further view of Jasleen (U.S. 20220247762).
Regarding claims 1, 9 and 16,
Shah discloses: An apparatus, comprising: (Shah [0017] apparatus)
a memory, configured to store: (Shah [0003] processor coupled to a memory)
comprising one or more sensitive word predictions, each sensitive word prediction being a word that is expected to be found in one or more user device communications; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches conversational model trained to recognize problem words and phrases with natural language processing to classify the call as a fraud risk. The training data is words and/or phrase from existing data used to make predictions about spam risk calls to the customer. The detection component 210 builds the conversation model from the previous spam risk calls (e.g., “training data set”))
comprising one or more predefined phrases representative of expected conversations between a user device and at least one caller of interest; and (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The conversation model can determine correlations between words and/or phrases and abnormal biometrics of the customer to determine spam risk calls and/or distress of the customer. For example, the phrase “What is your social security number?”)
an asset roster that lists one or more assets associated with the apparatus; and (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044; [Fig. 140] teaches a list of assets, for example the assets (components) displayed in figure 2 and the associated details regarding those assets in corresponding paragraphs, including call sources on the blocklist, which are associated with the apparatus)
a processor communicatively coupled to the memory, collocated with the memory within the apparatus, and configured to: (Shah [0003, 0050] a system that comprises a processor coupled to a memory)
monitor a first user device communication transmitted by the user device to a first caller of interest; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The call manager 140 can monitor calls to the customer device 130)
intercept the first user device communication prior to reaching the user device; (Shah [0002-0005, 0027-0037, 0040-0042] teaches a call manager which includes a monitoring component which captures biometrics of the customers using those captured (intercepted) biometrics to determine spam risk calls. Further teaches security component can pause or hold the spam risk call, mut the spam caller and disconnect the spam caller i.e., intercepting the communication)
obtain… a first plurality of sensitive word predictions for the first user device communication (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches training the model using “problem words or phrases” this teaches obtaining (in order to train) sensitive words (problem words))
determine whether the first… user device communication comprises the first plurality of sensitive word predictions; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] the call manager can detect the phrase “I need your bank account number.” The call manager , via the conversation model, can classify the call as a spam risk call based on the detected phrase)
in response to determining that the first… user device communication comprises the first plurality of sensitive word predictions, (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches classification of the caller as a spam risk based on detecting a specific phrase; the conversation model can classify the call as a spam risk call upon detecting a problem word or phrase)
determine whether the asset roster comprises a first asset identifier associated with the first caller of interest; (Shah [Fig. 1-110]; [0003-0007], [0018-0022], [0025-0028] detect a spam call risk via determining the call source. The call manager can determine the call source and compare the call source to a list of blocklist sources of known spam callers or spam call originators. The call manager can classify the call as a spam risk call upon determining a match of the call source and a listing of the blocklist sources)
…a first plurality of predefined phrases for the first user device communication (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches conversational model trained to recognize problem words and phrases with natural language processing to classify the call as a fraud risk. The training data is words and/or phrase from existing data used to make predictions about spam risk calls to the customer. The detection component 210 builds the conversation model from the previous spam risk calls (e.g., “training data set”))
identify a first plurality of… words from the first… user device communication; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches classification of the caller as a spam risk based on detecting a specific words; the conversation model can classify the call as a spam risk call upon detecting a problem word or phrase)
compare the first plurality of predefined phrases to the first plurality of… words; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaching using conversational models, which were trained using training data (past words/phrases) which consists of past words/phrases, to compare current time words/phrases to past words/phrases (predefined phrases))
in response to determining that the first plurality of predefined phrases match the first plurality of… words, determine that the first caller of interest attempts to obtain sensitive information associated with the user device based at least in part the first plurality of predefined phrases identified in the first… user device communication; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The conversation model can analyze conversations in real time or near real time for problem words or phrases. The conversation model can classify the call as a spam risk call upon detecting a problem word or phrase. For example, the call manager can detect the phrase “I need your bank account number.” The call manager 140, via the conversation model, can classify the call as a spam risk call based on the detected phrase)
in response to determining that the first caller of interest attempts to obtain the sensitive information, generate a first alert to the user device indicating that the first caller of interest is a first attacker; and (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The security component can contact the user via a user device. The security component can generate and send a notification to the user device, implement a call to the user device, and/or the like)
block the first user device communication from reaching the first attacker. (Shah [0003-0005, 0034-0039] teaches disconnecting calls determined to be fraudulent and a blocklist which those fraudulent actors can be added to which will prevent communications from teaching those fraudulent actors (attackers).
Similar claim 9 additionally discloses: A method, comprising: (Shah [0017] Method)
Similar claim 16 additionally discloses: A non-transitory computer readable medium storing instructions that when executed by a processor cause the processor to: (Shah [0017, 0053-0054]n storage media excludes modulated data signals)
Shah does not explicitly disclose:
a classification and regression tree (CART)
a plurality of directed acyclic graphs
after intercepting the first user device communication prior to reaching the user device, homomorphically encrypt the first user device communication as first encrypted user device communication;
in response to determining that the asset roster is missing the first asset identifier obtain, from the plurality of directed acyclic graphs;
cyphertext; encrypted
However, in the same field of endeavor Dalli discloses:
a classification and regression tree (CART); a plurality of directed acyclic graphs (Dalli [Abstract]; [0002-0004]; [0155 and 0210]; teaches explainable transducer transformer (XTT), Finite state transducers (FST) and Natural Language Processing (NLP) and image classification; [0243-0254] teaches that an XTT can be a Resource Description Framework (RDF) tree, RDF graph, Levi graph, or other suitable form of graph structure; [0020, 0177, 0145, 0254-0257, 0294] teaches structure such as (i.) hierarchical tree or network, (ii.) causal diagrams, (iii.) directed and undirected graphs; [Claim 23] specifically recites directed acyclic graphs)
cyphertext; encrypted (Dalli [0249-0250] teaches homomorphically encrypting text)
Shah and Dalli are analogous art because they are from the same field of endeavor of determining if a communication is adversarial based on text contents.
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 Shah and Dalli before him or her, to modify the method of Shah to include the graphs and homomorphic encryption of Dalli to increase efficiency and protect data.
The motivation for doing so would be [“ allows for crossover between different knowledge learnt for the different tasks to occur efficiently”] (Paragraph [0254, 0267] by Dalli) and [“In privacy preserving solution (iv.), homomorphic encryption, or homomorphic computing may be used to allow computation on encrypted data without either decrypting the data and also” (Paragraph [0250] by Dalli)] .
Therefore, it would have been obvious to combine Shah and Dalli to obtain the invention as specified in the instant claim.
Shah in view of Dalli does not explicitly disclose: in response to determining that the asset roster is missing the first asset identifier, obtain, from the plurality of directed acyclic graphs,
However, in the same field of endeavor Jasleen discloses: in response to determining that the asset roster is missing the first asset identifier, obtain, from the plurality of directed acyclic graphs, (Jasleen [Fig. 3 and 4]; [0012]; [0059-0073] discloses determining if a device is a new device (asset roster is missing the associated device identifier) and then the method performs additional verification steps which can include verification of voice signature or determining if a trusted peripheral device is in range of the IHS)
Shah in view of Dalli and Jasleen are analogous art because they are from the same field of endeavor threat detection and use identification.
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 Shah in view of Dalli and Jasleen before him or her, to modify the method of Shah in view of Dalli to include the device identification/verification of Jasleen because it will so that new devices can be added to a user’s profile.
The motivation for doing so is to provide a method such that the user can quickly and conveniently login to a protected network (Paragraph [0060-0071] by Jasleen)].
Therefore, it would have been obvious to combine Shah, Dalli and Jasleen to obtain the invention as specified in the instant claim.
While Dalli discloses homomorphic encryption which examiner Shah in view of Dalli and Jasleen do not explicitly disclose: after intercepting the first user device communication prior to reaching the user device, homomorphically encrypt the first user device communication as first encrypted user device communication;
However, in the same field of endeavor Badrinarayanan discloses: after intercepting the first user device communication prior to reaching the user device, homomorphically encrypt the first user device communication as first encrypted user device communication; (Badrinarayanan [0016, 0053, 0080, 0144-0169, 0170-0178, 0494-0501, 0554-0469] teaches homomorphically encrypting communications before the communications reach subsequent devices)
Shah in view of Dalli and Jasleen and Badrinarayanan are analogous art because they are from the same field of endeavor secure communications.
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 Shah in view of Dalli and Jasleen and Badrinarayanan before him or her, to modify the method of Shah in view of Dalli and Jasleen to include the homomorphically encrypting communication before transmitting to a subsequent device of Badrinarayanan because it will provide for eliminate the possibility for unencrypted data to be inertsecpted.
The motivation for doing so would be [“ The input devices can use a distributed threshold fully homomorphic encryption (dTFHE) scheme to encrypt their respective input shares. Such a scheme allows the participating devices to calculate a function of the respective input shares while those shares are in encrypted form, preserving input privacy.”] (Paragraph 0277 by Badrinarayanan)].
Therefore, it would have been obvious to combine Shah in view of Dalli and Jasleen and Badrinarayanan to obtain the invention as specified in the instant claim.
Regarding claims 2, 10 and 17,
Shah in view of Dalli, Jasleen and Badrinarayanan disclose: The apparatus of claim 1, wherein the processor is further configured to: in response to determining that the asset roster comprises the first asset identifier, identify the first caller of interest as a first asset associated with the apparatus. (Shah [Fig. 1-110]; [0003-0007], [0018-0022], [0025-0028] detect a spam call risk via determining the call source. The call manager can determine the call source and compare the call source to a list of blocklist sources of known spam callers or spam call originators. The call manager can classify the call as a spam risk call upon determining a match of the call source and a listing of the blocklist sources)
Regarding claims 3 and 11,
Shah in view of Dalli, Jasleen and Badrinarayanan disclose: The apparatus of claim 2, wherein the processor is further configured to: (Shah [0003] processor coupled to a memory)
monitor a second user device communication at the user device; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The call manager 140 can monitor calls to the customer device 130)
obtain a second plurality of sensitive word predictions from the; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches training the model using “problem words or phrases” this teaches obtaining (in order to train) sensitive words (problem words))
determine whether the… user device communication comprises the second plurality of sensitive word predictions; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] the call manager can detect the phrase “I need your bank account number.” The call manager , via the conversation model, can classify the call as a spam risk call based on the detected phrase)
in response to determining that the… user device communication comprises the second plurality of sensitive word predictions, identify a second caller of interest in communication with the user device; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches classification of the caller as a spam risk based on detecting a specific phrase; the conversation model can classify the call as a spam risk call upon detecting a problem word or phrase)
determine whether the asset roster comprises a second asset identifier associated with the second caller of interest; (Shah [Fig. 1-110]; [0003-0007], [0018-0022], [0025-0028] detect a spam call risk via determining the call source. The call manager can determine the call source and compare the call source to a list of blocklist sources of known spam callers or spam call originators. The call manager can classify the call as a spam risk call upon determining a match of the call source and a listing of the blocklist sources)
in response to determining that the asset roster comprises the second asset identifier, identify the second caller of interest as a second asset associated with the apparatus; (Shah [Fig. 1-110]; [0003-0007], [0018-0022], [0025-0028] detect a spam call risk via determining the call source. The call manager can determine the call source and compare the call source to a list of blocklist sources of known spam callers or spam call originators. The call manager can classify the call as a spam risk call upon determining a match of the call source and a listing of the blocklist sources)
obtain a second plurality of predefined phrases…; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches conversational model trained to recognize problem words and phrases with natural language processing to classify the call as a fraud risk. The training data is words and/or phrase from existing data used to make predictions about spam risk calls to the customer. The detection component 210 builds the conversation model from the previous spam risk calls (e.g., “training data set”))
identify a second plurality of… words from the second… user device communication; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaches classification of the caller as a spam risk based on detecting a specific words; the conversation model can classify the call as a spam risk call upon detecting a problem word or phrase)
compare the second plurality of predefined phrases to the second plurality of… words; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] teaching using conversational models, which were trained using training data (past words/phrases) which consists of past words/phrases, to compare current time words/phrases to past words/phrases (predefined phrases))
in response to determining that the second plurality of predefined phrases match the second plurality of… words, determine that the second caller of interest attempts to obtain the sensitive information associated with the user device based at least in part the second plurality of predefined phrases identified in the second… user device communication; and (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The conversation model can analyze conversations in real time or near real time for problem words or phrases. The conversation model can classify the call as a spam risk call upon detecting a problem word or phrase. For example, the call manager can detect the phrase “I need your bank account number.” The call manager 140, via the conversation model, can classify the call as a spam risk call based on the detected phrase)
in response to determining that the second caller of interest attempts to obtain the sensitive information, generate a second alert to the user device indicating that the second caller of interest is a second attacker. (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The security component can contact the user via a user device. The security component can generate and send a notification to the user device, implement a call to the user device, and/or the like)
Shah does not explicitly disclose:
a classification and regression tree (CART)
a plurality of directed acyclic graphs
homomorphically encrypt the second user device communication as second encrypted user device communication;
in response to determining that the asset roster is missing the second asset identifier;
cyphertext; encrypted
However, in the same field of endeavor Dalli discloses:
a classification and regression tree (CART); a plurality of directed acyclic graphs (Dalli [Abstract]; [0002-0004]; [0155 and 0210]; teaches explainable transducer transformer (XTT), Finite state transducers (FST) and Natural Language Processing (NLP) and image classification; [0243-0254] teaches that an XTT can be a Resource Description Framework (RDF) tree, RDF graph, Levi graph, or other suitable form of graph structure; [0020, 0177, 0145, 0254-0257, 0294] teaches structure such as (i.) hierarchical tree or network, (ii.) causal diagrams, (iii.) directed and undirected graphs; [Claim 23] specifically recites directed acyclic graphs)
homomorphically encrypt the second user device communication as second encrypted user device communication; cyphertext; encrypted (Dalli [0249-0250] teaches homomorphically encrypting text)
Shah and Dalli are analogous art because they are from the same field of endeavor of determining if a communication is adversarial based on text contents.
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 Shah and Dalli before him or her, to modify the method of Shah to include the graphs and homomorphic encryption of Dalli to increase efficiency and protect data.
The motivation for doing so would be [“ allows for crossover between different knowledge learnt for the different tasks to occur efficiently”] (Paragraph [0254, 0267] by Dalli) and [“In privacy preserving solution (iv.), homomorphic encryption, or homomorphic computing may be used to allow computation on encrypted data without either decrypting the data and also” (Paragraph [0250] by Dalli)] .
Therefore, it would have been obvious to combine Shah and Dalli to obtain the invention as specified in the instant claim.
Shah in view of Dalli does not explicitly disclose: in response to determining that the asset roster is missing the second asset identifier
However, in the same field of endeavor Jasleen discloses: in response to determining that the asset roster is missing the second asset identifier (Jasleen [Fig. 3 and 4]; [0012]; [0059-0073] discloses determining if a device is a new device (asset roster is missing the associated device identifier) and then the method performs additional verification steps which can include verification of voice signature or determining if a trusted peripheral device is in range of the IHS)
Shah in view of Dalli and Jasleen are analogous art because they are from the same field of endeavor threat detection and use identification.
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 Shah in view of Dalli and Jasleen before him or her, to modify the method of Shah in view of Dalli to include the device identification/verification of Jasleen because it will so that new devices can be added to a user’s profile.
The motivation for doing so is to provide a method such that the user can quickly and conveniently login to a protected network (Paragraph [0060-0071] by Jasleen)].
Therefore, it would have been obvious to combine Shah, Dalli and Jasleen to obtain the invention as specified in the instant claim.
Examiner note: the steps for claims 3 and 11 are almost identical to the steps for claims 1 and 9, they are just being performed a second time. Shah teaches performing the process for more than once device, which teaches performing the process twice or more. The difference between the independent claims and claims 3 and 11 is that in claim 1 the asset ID was not identified while in line 24 of claim 3 the asset ID was identified which Shah reads on, see blocklist above.
Regarding claims 4, 12 and 18,
Shah in view of Dalli, Jasleen and Badrinarayanan disclose: The apparatus of claim 1, wherein the processor is further configured to: determine whether the asset roster comprises a first voice sample that matches the first caller; and (Shah [0003-0005, 0018-0021, 0025-0035] teaches analyzing the conversation using a voice characteristic of the callee, comparing the voice characteristic of the callee to a voice profile characteristic of the callee and determining that the callee is a spam callee based on the result of the comparison)
in response to determining that the asset roster comprises the first voice sample, identify the first caller as a first asset associated with the apparatus. (Shah [0003-0005, 0018-0021, 0025-0035] teaches analyzing the conversation using a voice characteristic of the callee, comparing the voice characteristic of the callee to a voice profile characteristic of the callee and determining that the callee is a spam callee based on the result of the comparison)
Saha does not explicitly disclose in conjunction with determining that the asset roster is missing the first asset identifier,
However, in the same field of endeavor Jasleen discloses: in conjunction with determining that the asset roster is missing the first asset identifier, (Jasleen [Fig. 3 and 4]; [0012]; [0059-0073] discloses determining if a device is a new device (asset roster is missing the associated device identifier) and then the method performs additional verification steps which can include verification of voice signature or determining if a trusted peripheral device is in range of the IHS)
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 Jasleen for similar reasons as cited in claim 1.
Regarding claims 5, 13 and 19,
Shah in view of Dalli, Jasleen and Badrinarayanan disclose: The apparatus of claim 1, wherein: the processor is further configured to in conjunction with generating the first alert to the user device indicating that the first caller is the first attacker, add the first caller to a denylist. (Shah [0003-0005, 0018-0019, 0025-0027] teaches that the call manager can classify a call as a spam risk and a blocklist; [0019, 0027-0028] teaches that the machine learning technique to analyze the previous spam risk calls across different customers to determine a conversation model based on correlations in the previous spam risk calls; (Shah [0003-0007], [0018-0023], [0025-0036], [0042-0044] The security component can contact the user via a user device. The security component can generate and send a notification to the user device, implement a call to the user device, and/or the like) Therefore Shah teaches or at least suggests adding a caller to a denylist, additionally/alternatively (Dalli [0119-0122, 0147-0151, 0252-0253, 0267]; teaches dynamically training the predictive model using feed-forward and backward pass and dynamic updates)
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 Dalli for similar reasons as cited in claim 1.
Regarding claims 6 and 14,
Shah in view of Dalli, Jasleen and Badrinarayanan disclose: The apparatus of claim 1, wherein:
Shah does not explicitly disclose: the CART is a predictive model repository that is configured to be dynamically updated in accordance with one or more security policies
However, in the same field of endeavor Dalli discloses: the CART is a predictive model repository that is configured to be dynamically updated in accordance with one or more security policies. (Dalli [0119-0122, 0147-0151, 0252-0253, 0267]; teaches dynamically training the predictive model using feed-forward and backward pass and dynamic updates)
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 Dalli for similar reasons as cited in claim 1.
Regarding claims 7 and 15,
Shah in view of Dalli and Jasleen discloses: The apparatus of claim 1, wherein: comprises one or more branching sequences of the one or more predefined phrases that represent a plurality of expected communication requests from the first caller and a plurality of communication responses to the user device. (Shah [0003-0007], [0018-0023], [0022-0036], [0042-0044] The conversation model can determine correlations between words and/or phrases and abnormal biometrics of the customer to determine spam risk calls and/or distress of the customer. For example, the phrase “What is your social security number?”; the call manager can detect the phrase “I need your bank account number.” The call manager 140, via the conversation model, can classify the call as a spam risk call based on the detected phrase)
Shah does not explicitly disclose: each directed acyclic graph
However, in the same field of endeavor Dalli discloses: each directed acyclic graph (Dalli [Abstract]; [0002-0004]; [0155 and 0210]; [0277-0284]; teaches explainable transducer transformer (XTT), Finite state transducers (FST) and Natural Language Processing (NLP) and image classification; [0243-0254] teaches that an XTT can be a Resource Description Framework (RDF) tree, RDF graph, Levi graph, or other suitable form of graph structure; [0020, 0177, 0145, 0254-0257, 0294] teaches structure such as (i.) hierarchical tree or network, (ii.) causal diagrams, (iii.) directed and undirected graphs; [Claim 23] specifically recites directed acyclic graphs)
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 Dalli for similar reasons as cited in claim 1.
Regarding claims 8,
Shah in view of Dalli and Jasleen discloses: The apparatus of claim 1, wherein: the first alert is a visual notification that is displayed at the user device. (Shah [0034] The security component can contact the user via a user device . The security component can generate and send a notification to the user device… For example, the security component can send a push notification to the user device… audio or video interrupt prompt)
Regarding claim 20
Shah in view of Dalli, Jasleen and Badrinarayanan disclose: The non-transitory computer readable medium of claim 16, wherein the instructions further cause the processor to:
the CART is a predictive model repository that is configured to be dynamically updated in accordance with one or more security policies; and (Dalli [0119-0122, 0147-0151, 0252-0253, 0267]; teaches dynamically training the predictive model using feed-forward and backward pass and dynamic updates)
comprises one or more branching sequences of the one or more predefined phrases that represent a plurality of expected communication requests from a first caller and a plurality of communication responses to the user device. (Shah [0003-0007], [0018-0023], [0022-0036], [0042-0044] The conversation model can determine correlations between words and/or phrases and abnormal biometrics of the customer to determine spam risk calls and/or distress of the customer. For example, the phrase “What is your social security number?”; the call manager can detect the phrase “I need your bank account number.” The call manager 140, via the conversation model, can classify the call as a spam risk call based on the detected phrase)
Shah does not explicitly disclose: each directed acyclic graph
However, in the same field of endeavor Dalli discloses: each directed acyclic graph (Dalli [Abstract]; [0002-0004]; [0155 and 0210]; [0277-0284]; teaches explainable transducer transformer (XTT), Finite state transducers (FST) and Natural Language Processing (NLP) and image classification; [0243-0254] teaches that an XTT can be a Resource Description Framework (RDF) tree, RDF graph, Levi graph, or other suitable form of graph structure; [0020, 0177, 0145, 0254-0257, 0294] teaches structure such as (i.) hierarchical tree or network, (ii.) causal diagrams, (iii.) directed and undirected graphs; [Claim 23] specifically recites directed acyclic graphs)
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 Dalli for similar reasons as cited in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Gentry 6/18/2019 (US 20200403781) teaches using homomorphic encryption at the bit level.
Trim 1/25/2022 (US 20230239400) teaches detecting and resolving fraudulent calls. Receiving voice call data corresponding to an incoming telephone call placed to a user device, wherein the voice call data comprises caller voice data. Converting the caller voice data to caller text data comprising one or more text phrases. Determining that the one or more text phrases satisfies a first condition, and responsive to determining that the one or more text phrases satisfies the first condition, transmitting a user alert to the user device.
Chen 6/12/2020 (US 20210392173) teaches the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.
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 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.
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THOMAS A. CARNES
Examiner
Art Unit 2436
/THOMAS A CARNES/ Examiner, Art Unit 2436
/MOEEN KHAN/ Primary Examiner, Art Unit 2436