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
Office Action is in response to the RCE filed on 4/16/2026. Claims 6 and 15 have been cancelled. Claim 22 was added as New. Claims 1-5, 7-14 and 16-22 are pending. This Office Action is Non-Final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/16/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 10 and 16 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 § 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 (i.e., changing from AIA to pre-AIA ) 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 9-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Divakaran et al. (US 2024/0323208) in view of Wong et al. (US 2022/0214677) and Yu et al. (US 2023/0237467).
As per claim 1, Divakaran teaches a system for automatically diverting data transmissions from compromised processing components, the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device (Divakaran, Paragraph 0070 “As shown, the computer system 20 includes a central processing unit (CPU) 21, a system memory 22, and a system bus 23 connecting the various system components, including the memory associated with the central processing unit 21.”),
causes the processing device to perform the steps of: identify a processing component associated with a data transmission (Divakaran, Paragraph 0061 recites “At 504, device monitoring component 106 identifies, from the first plurality of packets, a subset of packets corresponding to a first device of network 101. For example, the first device may be device 102a. For example, device monitoring component 106 may include packets transmitted and/or received by device 102a during a given period of time (e.g., 10 minutes) in the subset of packets.”);
determine whether to transmit the data transmission based on the determination the anomaly is present in the processing component or the determination the data transmission matches at least one pre-identified compromised data transmission pattern (Divakaran, Paragraphs 0065-0066 recites “At 518, device monitoring component 106 calculates a risk score associated with device 102a based on the deviation, the first probability, and the second probability. For example, the risk score may be a function of these three values. In some aspects, the risk score may be a normal average, a weighted average (where each value is weighted differently), or a median of the three values. For example, the values are 50%, 75%, and 85%, as described, the average (i.e., the risk score) is 70. At 520, device monitoring component 106 determines if the risk score is greater than a preset threshold risk score (e.g., 65). In response to determining that the risk score is not greater than the threshold risk score, method 500 returns to 502, where device monitoring component 106 intercepts a new set of packets. In response to determining that the risk score is greater than the threshold risk score, method 500 advances to 522, where device monitoring component 106 determines, using an attack classification model, an attack type of the first device, and executes a remediation action based on the attack type to resolve anomalous behavior in device 102a.”);
and generate an alert interface component based on the determination whether the anomaly is present in the processing component or the determination whether the data transmission matches at least one pre-identified compromised data transmission pattern (Divakaran, Paragraph 0067 recites “In some aspects, the remediation action comprises one or more of: factory resetting device 102a, rebooting device 102a, transmitting an alert about the anomalous behavior of device 102a to a network administrator of network 101, blocking of the traffic of a device, re-routing of the traffic of a device to a security middlebox, and removing device 102a from network 101.”).
But fails to teach determine, by an artificial intelligence (AI) engine, whether an anomaly is present in the processing component based on identifying central processing unit utilization being overburdened.
However, in an analogous art Wong teaches determine, by an artificial intelligence (AI) engine, whether an anomaly is present in the processing component based on identifying central processing unit utilization being overburdened (Wong, Paragraph 0020 recites “In particular embodiments, the microcontroller 213 may determine that an anomalous event has occurred on the electronic device 100 by processing the one or more real-time sensor data with a machine-learning model running on the microcontroller 213. In particular embodiments, the microcontroller 213 may process the one or more real-time sensor data with the machine-learning model 215 at a regular interval. In particular embodiments, the machine-learning model 215 may take a snapshot of the one or more real-time sensor data as input. In particular embodiments, the machine-learning model 215 may be a recursive neural network that considers a trend of the one or more real-time sensor data to produce an output. In particular embodiments, the machine-learning model 215 may be a binary classifier that may determine whether an anomalous event has occurred on the electronic device 100. In particular embodiments, the machine-learning model 215 may be a multiclass classifier that may determine what type of anomalous event has occurred. As an example and not by way of limitation, continuing with a prior example, a TinyML-based classifier 215 may be installed on the sensor hub 213. TinyML may be used for embedded machine-learning applications. TinyML may be optimized for resource constrained environments. For example, a TinyML-based machine-learning model may run on a microcontroller with a few kilobytes memory and the processing power in a few megahertz. The sensor hub 213 may process the sensor data from the one or more sensors 211 with the classifier 215 at a predefined interval. The classifier 215 may be a Long Short-Term Memory (LSTM) model that may take a sequence of the sensor data to determine whether an anomalous event has occurred on the mobile phone 100. In particular embodiments, the classifier 215 may produce a binary output indicating whether an anomalous event has occurred on the mobile phone 100 or not. In particular embodiments, the classifier 215 may produce one of a plural values. Each of the plural values may indicate a type of anomalous event including, but not limited to, no anomalous event, an anomalous event associated with overloaded processors, an anomalous event associated with high memory utilization, an anomalous event associated with lost network connectivity, an anomalous event associated with high network utilization, an anomalous event associated with high device temperature, or an anomalous event associated with any suitable condition for an anomalous event. Although this disclosure describes determining that an anomalous event has occurred on an electronic device in a particular manner, this disclosure contemplates determining that an anomalous event has occurred on an electronic device in any suitable manner.” And Paragraph 0042 recites “FIG. 8 illustrates a diagram 800 of an example artificial intelligence (AI) architecture 802 that may be utilized to perform determining whether an anomalous event has occurred on an electronic device, in accordance with the presently disclosed embodiments.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Wong’s Detecting anomalous events using a microcontroller with Divakaran’s systems and methods for detecting anomalous behavior in internet-of-things (IOT) devices because it offers the advantage of using AI to help determine anomalous events.
But fails to teach determine, by a machine learning model, whether the data transmission matches at least one pre-identified compromised data transmission pattern by generating a confidence score indicating a likelihood that the data transmission matches the at least one pre-identified compromised data transmission pattern and comparing the confidence score to a confidence threshold; apply, in an instance where the confidence score meets or exceeds the confidence threshold, the data transmission and the determination that the data transmission matches at least one pre-identified compromised data transmission pattern to a smart contract; and
trigger, by the smart contract, an automatic block of the data transmission.
However, in an analogous art Yu teaches determine, by a machine learning model, whether the data transmission matches at least one pre-identified compromised data transmission pattern by generating a confidence score indicating a likelihood that the data transmission matches the at least one pre-identified compromised data transmission pattern and comparing the confidence score to a confidence threshold (Yu, Paragraph 0014 recites “As one example of how a machine learning model might be implemented, a system (e.g., one or more computing devices) may train, using training data, a machine learning model to output a risk score associated with a cold restore based on recent transaction activity. Such a cold restore may comprise a transfer of one or more cryptocurrencies from a cold state to a hot state. In the cold state, the one or more cryptocurrencies stored by a first digital wallet may be inaccessible via a public network. In contrast, in the hot state, the one or more cryptocurrencies may be stored in a second digital wallet accessible to one or more users via the public network. The training data may comprise information about historical cold restore activity, such as indications of whether one or more cold restore requests were granted or denied and/or transaction histories associated with time periods when the one or more cold restore requests were granted or denied. A request for a new cold restore of one or more first cryptocurrencies stored by a third digital wallet may be received. One or more computing devices of the system may provide, as input to the trained machine learning model, a first transaction history associated with the request. For example, that transaction history might indicate whether an attack on a blockchain has occurred. One or more computing devices of the system may receive, as output from the trained machine learning model, a first risk score and then output, based on comparing the first risk score to a threshold, an indication of whether the request should be granted. In such an example, one or more computing devices of the system may additionally and/or alternatively provide the machine learning model a variety of different types of input for the purposes of receiving a risk score.” And Paragraph 0052 recites “In step 304, the computing device may determine a risk score. A risk score may be any objective and/or subjective evaluation of the risk of transferring the one or more cryptocurrencies from a cold state to a hot state. For example, the computing device may determine, based on the asset history, a risk score associated with the transfer of the one or more cryptocurrencies from the cold state to the hot state. As such, the risk score may be an indication as to whether the request for the cold restore should be granted in view of current circumstances involving the digital wallet, the one or more cryptocurrencies, or the like. The risk score may be represented as a Boolean value (e.g., “True” for risky, “False” for not risky), a percentage value (e.g., “40% risky”), a subjective evaluation of the risk (e.g., “somewhat risky”), or the like.”);
apply, in an instance where the confidence score meets or exceeds the confidence threshold, the data transmission and the determination that the data transmission matches at least one pre-identified compromised data transmission pattern to a smart contract (Yu, Paragraph 0069 recites “In step 305, the computing device may compare the risk score determined in step 304 to a threshold. The threshold may be based on a time of day. For example, cold restores during daytime hours in the United States may be slightly more trustworthy, such that the threshold during these hours may be relatively more permissive. The threshold may be based on activity associated with one or more VASPs. For example, if a VASP is detecting potential network attacks on its servers, then the threshold may be configured to be less permissive, as the attacks may suggest possible malicious activity. The threshold may be based on a geographical location associated with the request received in step 301. For example, requests originating from the United States may be associated with a more permissive threshold than requests originating from countries associated with frequent malicious activity.”);
and trigger, by the smart contract, an automatic block of the data transmission (Yu, Paragraph 0070 recites “In step 306, the computing device may determine whether to grant the request received in step 301. Determining whether to grant the request received in step 301 may be based on comparing the risk score to the threshold, as is performed in step 305. For example, if the risk score satisfies the threshold, the computing device may grant the request, whereas if the risk score does not satisfy the threshold, the computing device may deny the request. If the computing device decides to grant the request, the flowchart 300 may proceed to step 307a. Otherwise, if the computing device decides to deny the request, the flowchart 300 may proceed to step 307b.”)
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Yu’s Risk Analysis System For Cold Restore Requests For Digital Wallets with Divakaran’s systems and methods for detecting anomalous behavior in internet-of-things (IOT) devices because it offers the advantage of ensuring safe transaction to smart contracts.
As per claim 2, Divakaran in view of Wong and Yu teaches the system of claim 1, Divakaran further teaches wherein determining whether an anomaly is present in the processing component is further based on identifying data transmission data comprises at least one of a current data transmission data or historical data transmission data (Divakaran, Paragraph 0056 recites “Device monitoring component 106 may then generate a respective deterministic profile based on the extracted deterministic features of the respective device. In some aspects, the respective deterministic profile is a hash table comprising hash values representing the extracted deterministic features. For example, each deterministic feature identified above may be inputted into a hashing algorithm and the output may be stored in the deterministic profile. Subsequently, when the respective device is evaluated at a later time (e.g., during method 500), the values in the deterministic profile may be compared against the deterministic features extracted at that time. In addition to generating a deterministic profile, device monitoring component 106 may generate, for each respective device of the plurality of devices 102, a device-specific training dataset (included in training datasets 118) comprising a plurality of feature vectors labelled by anomalous or non-anomalous classes using the plurality of packets. For example, a given training dataset may include device-specific information about device 102a. The feature vectors of that training dataset may include one or more features such as a size of each packet associated with the device, time intervals between transmitted/received packets, direction of packets, semantic information of IP addresses and port numbers, sizes of connections (e.g., using 5-tuple information of source/destination IP addresses, source/destination ports, protocol, etc. The feature vector may further be labelled as anomalous/non-anomalous, although this is not necessary for the proposed unsupervised approach.”).
As per claim 3, Divakaran in view of Wong and Yu teaches the system of claim 1, Yu further teaches wherein the data transmission comprises a plurality of data transmissions, and wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: segment, by the machine learning model, the plurality of data transmissions and generate the data transmission based on the segmented plurality of data transmissions (Yu, Paragraph 0056 recites “For example, the training data may comprise a history of a plurality of different cryptocurrencies and a history of cold restores associated with those cryptocurrencies, and the cold restores may (but need not) be tagged with indications of whether the cold restores were associated with malicious activity (e.g., theft). The computing device may provide, as input to the trained machine learning model, the asset history determined in step 303. The computing device may then receive, as output from the trained machine learning model, the risk score. In this manner, the machine learning model may leverage its knowledge about risk over time pertaining to cryptocurrencies to identify a risk, at a particular time, associated with a cold restore.”);
and automatically block, based on the determination that the data transmission matches at least one pre-identified compromised data transmission, the generated data transmission (Yu, Paragraph 0070 recites “In step 306, the computing device may determine whether to grant the request received in step 301. Determining whether to grant the request received in step 301 may be based on comparing the risk score to the threshold, as is performed in step 305. For example, if the risk score satisfies the threshold, the computing device may grant the request, whereas if the risk score does not satisfy the threshold, the computing device may deny the request. If the computing device decides to grant the request, the flowchart 300 may proceed to step 307a. Otherwise, if the computing device decides to deny the request, the flowchart 300 may proceed to step 307b.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Yu’s Risk Analysis System For Cold Restore Requests For Digital Wallets with Divakaran’s systems and methods for detecting anomalous behavior in internet-of-things (IOT) devices because it offers the advantage of ensuring safe transaction to smart contracts.
As per claim 4, Divakaran in view of Wong and Yu teaches the system of claim 1, Divakaran further teaches wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: trigger an automatic transmission of the alert interface component to a user device, wherein the user device is associated with at least one of a user account associated with the data transmission or associated with at least one entity account associated with the processing component (Divakaran, Paragraph 0067 recites “In some aspects, the remediation action comprises one or more of: factory resetting device 102a, rebooting device 102a, transmitting an alert about the anomalous behavior of device 102a to a network administrator of network 101, blocking of the traffic of a device, re-routing of the traffic of a device to a security middlebox, and removing device 102a from network 101.” A network administrator could be a user of the device.).
As per claim 5, Divakaran in view of Wong and Yu teaches the system of claim 4, Divakaran further teaches wherein the alert interface component comprises an alert indicating the data transmission was transmitted or automatically blocked (Divakaran, Paragraph 0067 recites “In some aspects, the remediation action comprises one or more of: factory resetting device 102a, rebooting device 102a, transmitting an alert about the anomalous behavior of device 102a to a network administrator of network 101, blocking of the traffic of a device, re-routing of the traffic of a device to a security middlebox, and removing device 102a from network 101.”).
As per claim 9, Divakaran in view of Wong and Yu teaches the system of claim 4, Divakaran further teaches wherein the determination of whether to transmit the data transmission further comprises an analysis, by an application engine, of a rule database and a comparison of the data transmission to the rule database (Divakaran, Paragraph 0042 recites “In some aspects, the network characteristics used for profiling IoT devices may be expanded over time. Similarly, the attributes used to define an attack type can be augmented using information available from various sources (e.g., a threat intelligence database). The profiles created and revised may also be shared with a central server in a privacy-preserving manner to improve accuracy of anomaly detection.”).
Regarding claims 10 and 16, claims 10 and 16 are directed to a computer program product and a method associated with the system of claim 1. Claims 10 and 16 are of similar scope to claim 1, and are therefore rejected under similar rationale.
Regarding claims 11 and 17, claims 11 and 17 are directed to a computer program product and a method associated with the system of claim 2. Claims 11 and 17 are of similar scope to claim 2, and are therefore rejected under similar rationale.
Regarding claims 12 and 18, claims 12 and 18 are directed to a computer program product and a method associated with the system of claim 3. Claims 12 and 18 are of similar scope to claim 3, and are therefore rejected under similar rationale.
Regarding claims 13 and 19, claims 13 and 19 are directed to a computer program product and a method associated with the system of claim 4. Claims 13 and 19 are of similar scope to claim 4, and are therefore rejected under similar rationale.
Regarding claims 14 and 20, claims 14 and 20 are directed to a computer program product and a method associated with the system of claim 5. Claims 14 and 20 are of similar scope to claim 5, and are therefore rejected under similar rationale.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Divakaran et al. (US 2024/0323208), Wong et al. (US 2022/0214677) and Yu et al. (US 2023/0237467) and in further view of Kanso et al. (US 2022/0131888).
As per claim 7, Divakaran in view of Wong and Yu teaches the system of claim 1, but fails to teach wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: identify a plurality of processing components and associated current processing component resource capacity for each processing component; and transmit each data transmission to the processing component of the plurality of processing components based on the associated current processing component resource capacity.
However, in an analogous art Kanso teaches wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: identify a plurality of processing components and associated current processing component resource capacity for each processing component; and transmit each data transmission to the processing component of the plurality of processing components based on the associated current processing component resource capacity (Kanso, Paragraph 0035 recites “In another example, as described in detail below, vulnerability risk assessment system 102 can further facilitate (e.g., via processor 106): checking specification and privileges associated with the primary-infected pods and the secondary-infected pods, and generating a list of suspect machines, primary-infected machines, and secondary-infected machines; determining total resource capacity that respective infected containers associated with the primary-infected pods and the secondary-infected pods have ability to consume, and generating a total-capacity-at-risk measure; determining permissions associated with the respective infected containers; generating a contextual risk score and an absolute risk score associated with the primary-infected pods and the secondary-infected pods; assessing bounded capacity of at least one of: processor, memory, or disk; and/or generating a contextual risk score and an absolute risk score associated with the primary-infected pods and the secondary-infected pods, and generating a second contextual risk score and a second absolute risk score associated with the primary-infected pods and the secondary-infected pods based on one or more changes.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Kanso’s context based risk assessment of a computing resource vulnerability with Divakaran’s systems and methods for detecting anomalous behavior in internet-of-things (IOT) devices because it offers the advantage of having another metric to help determine if a resource is compromised or potentially malicious.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Divakaran et al. (US 2024/0323208), Wong et al. (US 2022/0214677) and Yu et al. (US 2023/0237467) and in further view of Cui et al. (US 12,028,362).
As per claim 8, Divakaran in view of Wong and Yu teaches the system of claim 1, but fails to teach wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: transmit the data transmission to a recipient account based on the determination to transmit the data transmission in an instance where the anomaly is not present in the processing component and in an instance where the data transmission does not match any of the at least one pre-identified compromised data transmission pattern.
However, in an analogous art Cui teaches wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: transmit the data transmission to a recipient account based on the determination to transmit the data transmission in an instance where the anomaly is not present in the processing component and in an instance where the data transmission does not match any of the at least one pre-identified compromised data transmission pattern (Cui, Col. 9 Lines 33-40 recites “In some embodiments, a decoder 116 can output a normalcy score as the highest probability value in the probability distribution generated by the normalizing function, and the normalcy score can be used by a request processor 112 or threat detection service 106 to determine whether to allow or disallow execution of requests, to generate alerts of non-anomalous events, and the like.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Cui’s Detecting Anomalous Storage Service Events Using Autoencoders with Divakaran’s systems and methods for detecting anomalous behavior in internet-of-things (IOT) devices because it offers the advantage of informing parties that there are no anomalous issues.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Divakaran et al. (US 2024/0323208), Wong et al. (US 2022/0214677) and Yu et al. (US 2023/0237467) and in further view of Chivu et al. (US 12,238,119).
As per claim 21, Divakaran in view of Wong and Yu teaches the system of claim 1, but fails to teach wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: assign, based on determining the anomaly is present in the processing component or determining the data transmission matches at least one pre-identified compromised data transmission pattern, numeric values to data associated with the data transmission to generate numeric training data;
apply the numeric training data to the AI engine; and generate, by the AI engine, symbolic AI data to represent the data of the AI engine.
However, in an analogous art Chivu teaches wherein the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: assign, based on determining the anomaly is present in the processing component or determining the data transmission matches at least one pre-identified compromised data transmission pattern, numeric values to data associated with the data transmission to generate numeric training data;
apply the numeric training data to the AI engine; and generate, by the AI engine, symbolic AI data to represent the data of the AI engine (Chivu, Col. 3 Line 61 – Col. 4 Line 15 recites “Upon receiving the event dataset 112, the computer system 130 can use AI models 132 to determine an event cluster baseline 114 for the event dataset 112. A first AI model of the AI models 132 can be a variational auto-encoder trained to determine an anomaly score for each event in the event dataset 112. When available, threat intelligence data 134 of known threat event data and associated anomaly scores may be used during training so that the variational auto-encoder learns how to determine anomaly scores for the event dataset 112. During training, the anomaly scores may be used to filter portions of a training event dataset that are to be used for cluster training. For the training, the computer system 130 may compare each anomaly score to a threshold, and events having anomaly scores greater than the threshold may be included in a final training dataset of clustering. During inference, the event dataset 112 may, but need not, be filtered. The events of the event dataset 112 can be input into a second AI model of the AI models 132. The second AI model can be a self-organizing map trained to cluster events. The second AI model can output the event cluster baseline 114. The event cluster baseline 114 can be a set of clusters that group the events.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Chivu’s Determining Threats From Anomalous Events Based On Artificial Intelligence Models with Divakaran’s systems and methods for detecting anomalous behavior in internet-of-things (IOT) devices because it offers the advantage of having up to date training data to help prevent further anomalies.
Allowable Subject Matter
Claim 22 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RODERICK TOLENTINO whose telephone number is (571)272-2661. The examiner can normally be reached Mon- Fri 8am-4pm.
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, Luu Pham can be reached at 571-270-5002. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
RODERICK . TOLENTINO
Examiner
Art Unit 2439
/RODERICK TOLENTINO/Primary Examiner, Art Unit 2439
Prosecution Insights