DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The present application, filed on July 05, 2024, is accepted.
Claims 1 – 17 are being considered on the merits.
Drawings
The drawings, filed on July 05, 2024, are accepted.
Specification
The specification, filed on July 05, 2024, is accepted.
Double Patenting
No rejection warranted at application’s initial filling time of filling for a patent.
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.
Claims 1 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220350898 A1 to LV et al., (hereinafter, “LV”) in view of US 20230342649 A1 to Flament et al., (hereinafter, “Flament”)
Regarding claim 1, LV teaches a method performed by an apparatus including a first entity in a quantum key distribution network (QKDN) supporting autonomic management and control (AMC), the method comprising: collecting first data from a second entity; [LV, para. 11 discloses the first trusted node includes a first receiving unit configured to receive a first target data set sent by a first participant. The first target data set is obtained via encrypting, by the first participant, a data set provided by the first participant based on a first preset encryption mode] determining whether a first machine learning (ML) model available for analyzing the first data exists; generating a control action, wherein i) if the first ML model exists, the control action is generated by analyzing the first data using the first ML model, [LV, para. 12 discloses the first trusted node further includes an intermediate training result determination unit configured to decrypt the first target data set, determine first training data, and perform model training for a preset machine learning model based on the first training data to obtain a first intermediate training result.] and requesting the second entity to apply the control action, wherein the second data is processable data after receiving the second ML model, [LV, para. 14 discloses The first trusted node further includes a federated learning unit configured to perform federated learning for the preset machine learning model based on at least the first intermediate training result and the decrypted second intermediate training result, to update model parameters of the preset machine learning model and obtain a learning completed target model.] and wherein the second ML model is generated by training using the first data [LV, para. 14 discloses the first trusted node further includes a federated learning unit configured to perform federated learning for the preset machine learning model based on at least the first intermediate training result and the decrypted second intermediate training result, to update model parameters of the preset machine learning model and obtain a learning completed target model.], but LV does not teach if the first ML model does not exist, the control action is generated by analyzing second data collected from the second entity using a second ML model received from a third entity;
However, Flament does teach if the first ML model does not exist, the control action is generated by analyzing second data collected from the second entity using a second ML model received from a third entity; [Flament, para. 108 discloses After determining the difference between an initial and a final polarization of the one or more photons, the polarization correction facility may proceed to act 604. In act 604, the polarization correction facility may determine, using a machine learning model and/or a lookup table, a feedback parameter for a polarization modulator based on the difference between the initial polarization and the measured polarization of the one or more photons. The machine learning model may be, for example, a Q-learning algorithm, an Actor-Critic algorithm, or any other suitable reinforcement learning model. The machine learning model may have been trained to predict an appropriate feedback parameter or parameters configured to return the measured polarization to the initial polarization or approximately the initial polarization by changing one or more settings of the polarization modulator.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Flament’s system with LV’s system, with a motivation for the machine learning model may have been trained by, for example, a policy configured to provide feedback to the machine learning model based on an accuracy of the machine learning model's predictions. [Flament, para. 108]
As per claim 2, modified LV teaches the method of claim 1, wherein the first data and/or the second data include one or more quantum channel performance related parameters. [LV, para. 38 discloses the user profile data 120 for each user (i.e., each customer/subscriber) can also include other relevant information associated with the subscriber's account, including information regarding their rate plan, service level agreement (SLA) parameters, device history (e.g., regarding past devices used by the subscriber, duration of use, etc.). In some embodiments, the user profile data 120 can also include information identifying past and current device accessories associated with each user and/or their CD or CDs (e.g., auxiliary IoT devices, headphones, devices cases, screen protectors, etc.).]
Regarding claim 3, modified LV teaches the method of claim 2, but LV does not teach wherein the first data and/or the second data include at least one of i) a quantum bit-error ratio (QBER) of a quantum channel, ii) a single photon detector (SPD) output counter, and iii) a code formation rate.
However, Flament does teach wherein the first data and/or the second data include at least one of i) a quantum bit-error ratio (QBER) of a quantum channel, ii) a single photon detector (SPD) output counter, and iii) a code formation rate. [Flament, para. 61 discloses The probe photons, for example, may be encoded with a known initial polarization, and may be produced periodically (e.g., to be interweaved with quantum data photons) or in response to a triggering event (e.g., in response to a detected change in temperature, in response to the difference between the known initial polarization and a final polarization exceeding a threshold value, in response to a reduction or increase of the useful quantum operation rate (e.g., a change in the quantum bit error rate (QBER)). In some embodiments, the triggering event may be a signal generated by a GPS-disciplined clock and/or a fiber-based network synchronization protocol (e.g., a white rabbit protocol). The method may further include measuring a polarization of the one or more probe photons after traversing the optical fiber (e.g., by using a polarimeter), and determining a difference between an initial polarization of the one or more probe photons and the measured polarization of the one or more probe photons. The method may include determining, using a machine learning model and/or a lookup table, a feedback parameter based on the difference between the initial polarization and the measured polarization, and changing a parameter of a polarization modulator coupled to the optical fiber to correct for the difference between the initial polarization and the measured polarization.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Flament’s system with LV’s system, with a motivation for the machine learning model may have been trained by, for example, a policy configured to provide feedback to the machine learning model based on an accuracy of the machine learning model's predictions. [Flament, para. 108]
As per claim 4, modified LV teaches the method of claim 2, wherein the control action includes an action related to improving performance of a quantum channel. [LV, para. 45 discloses the first preset encryption mode is an encryption mode agreed by the first trusted node and the first parameter, and accordingly the first trusted node decrypts the first target data set based on an agreed decryption mode to obtain the first training data. In other words, the first training data is obtained via decryption and is unencrypted data, so that the first trusted node is capable of performing model training for the preset machine learning model based on the unencrypted data, which improves the efficiency of model training, and lays a foundation for reducing the usage of the computing resources and storage resources in the process of federated learning.]
Regarding claim 5, modified LV teaches the method of claim 1 wherein the first data and/or the second data include ii) key storage status data [LV, para. 117 discloses the devices used in this mode include a quantum key generation device, a quantum key charging device, and a mobile quantum key storage medium. The specific process is as follows. The quantum key generation device generates a quantum key and stores the quantum key in its own key pool. The quantum key charging device extracts the quantum key from the key pool and charges the quantum key into the mobile quantum key storage medium. The mobile quantum key storage medium is connected to the client device, such as the device of the participant, and the corresponding data of the participant are encrypted or decrypted according to the authority.], but LV does not teach wherein the first data and/or the second data include i) real-time service data.
However, Palamadai does teach wherein the first data and/or the second data include i) real-time service data. [Palamadai, para. 38 discloses The user profile data 120 can include information that uniquely identifies different users (e.g., user “user identities” via a unique name, username, account name/number, etc.) and their associated CD or CDs (e.g., via unique device identifiers). The user profile data 120 can further include equipment information for each user's CD (or CDs) that defines or indicates the current hardware and software components of the CD, and the corresponding features and functionalities thereof. For example, the user profile data for a particular user can identify the current hardware associated with the user's CD (e.g., current device type, make, model, etc.) as well as the current software associated with the user's CD (e.g., firmware, applications, logic, programs, operating system, etc.). In various embodiments in which the one or more communication networks 132 include a communication network associated with a particular carrier/provider, this user profile data (and any other user profile information described herein) may be collected by the data collection component 106 as associated in customer/subscriber account information (e.g., provided by the communication service provider server 134) for the respective subscribed users of the particular carrier/provider (e.g., the subscriber information can provide a record of current hardware and software on all customer devices).]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Flament’s system with LV’s system, with a motivation for the machine learning model may have been trained by, for example, a policy configured to provide feedback to the machine learning model based on an accuracy of the machine learning model's predictions. [Flament, para. 108]
Regarding claim 6, modified LV teaches the method of claim 1 wherein the key storage status data includes at least one of i) a key number and ii) a key life cycle [LV, para. 117 discloses the devices used in this mode include a quantum key generation device, a quantum key charging device, and a mobile quantum key storage medium. The specific process is as follows. The quantum key generation device generates a quantum key and stores the quantum key in its own key pool. The quantum key charging device extracts the quantum key from the key pool and charges the quantum key into the mobile quantum key storage medium. The mobile quantum key storage medium is connected to the client device, such as the device of the participant, and the corresponding data of the participant are encrypted or decrypted according to the authority.], but LV does not teach wherein the real-time service data includes at least one of i) a service type, ii) a security level, and iii) a required key quantity.
However, Flament does teach wherein the real-time service data includes at least one of i) a service type, ii) a security level, and iii) a required key quantity. [Flament, para. 38 discloses the inventors have recognized that certain machine learning techniques can be trained to initiate automatic, real-time polarization compensation by making predictions of polarization drift based on historical polarization data. Such machine learning models (e.g., time series forecasting models) can be configured to make predictions (“forecast”) with respect to regular or periodic points in time (“forecast points”) when polarization measurements are obtained. Whenever the machine learning model predicts that the polarization drift and model error exceed a certain threshold, the network may be taken down for polarization compensation maintenance. By using a machine learning model to predict such network downtimes, rather than periodically forcing such downtimes, network downtime may be overall reduced.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Flament’s system with LV’s system, with a motivation for the machine learning model may have been trained by, for example, a policy configured to provide feedback to the machine learning model based on an accuracy of the machine learning model's predictions. [Flament, para. 108]
As per claim 7, modified LV teaches the method of claim 5, wherein the control action includes an action related to scheduling and utilization of a key resource. [LV, para. 58 discloses the first intermediate training result is encrypted based on the first quantum key, and then the encrypted first intermediate training result is to be transmitted to other trusted nodes participating in federated learning, such as the second trusted node, to improve the security of data transmission. Here, due to the use of quantum encryption technology, compared with other encryption modes, the security is higher, which further improves the security of data transmission.]
Regarding claim 8, modified LV teaches the method of claim 1, wherein the first data and/or the second data include at least one of ii) a key consumption rate and service requirement, [LV, para. 117 discloses the devices used in this mode include a quantum key generation device, a quantum key charging device, and a mobile quantum key storage medium. The specific process is as follows. The quantum key generation device generates a quantum key and stores the quantum key in its own key pool. The quantum key charging device extracts the quantum key from the key pool and charges the quantum key into the mobile quantum key storage medium. The mobile quantum key storage medium is connected to the client device, such as the device of the participant, and the corresponding data of the participant are encrypted or decrypted according to the authority.] wherein the control action includes an optimal key relay route [LV, para. 58 discloses the first intermediate training result is encrypted based on the first quantum key, and then the encrypted first intermediate training result is to be transmitted to other trusted nodes participating in federated learning, such as the second trusted node, to improve the security of data transmission. Here, due to the use of quantum encryption technology, compared with other encryption modes, the security is higher, which further improves the security of data transmission.], but LV does not teach wherein the first data and/or the second data include at least one of i) a quantum key distribution (QKD) link parameter, ii) a key consumption rate and service requirement, and iii) a QKDN topology.
However, Flament does teach wherein the first data and/or the second data include at least one of i) a quantum key distribution (QKD) link parameter, [Flament, para. 66 discloses probe photons are generated by probe photon source 102 and encoded with a known polarization state (e.g., H, V, D, A, R, and/or L polarization states) by polarization modulator 105. Polarization modulator 105 may be any suitable polarization modulator (e.g., a mechanical polarization modulator as described in connection with FIGS. 2A-2C herein, an electro-optic modulator (EOM), or a nonlinear optical material as described in connection with FIG. 2D herein). The photons generated by probe photon source 102, after being encoded with a known polarization state, then propagate along communications optical fiber 104 from left to right towards polarization correction system 110.] and iii) a QKDN topology. [Flament, para. 38 discloses the inventors have recognized that certain machine learning techniques can be trained to initiate automatic, real-time polarization compensation by making predictions of polarization drift based on historical polarization data. Such machine learning models (e.g., time series forecasting models) can be configured to make predictions (“forecast”) with respect to regular or periodic points in time (“forecast points”) when polarization measurements are obtained. Whenever the machine learning model predicts that the polarization drift and model error exceed a certain threshold, the network may be taken down for polarization compensation maintenance. By using a machine learning model to predict such network downtimes, rather than periodically forcing such downtimes, network downtime may be overall reduced.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Flament’s system with LV’s system, with a motivation for the machine learning model may have been trained by, for example, a policy configured to provide feedback to the machine learning model based on an accuracy of the machine learning model's predictions. [Flament, para. 108]
Regarding claims 9 – 16, they recite features similar to features within claims 1 – 8, therefore, they are rejected in a similar manner.
Regarding claim 17, it recites features similar to features within claim 1, therefore, it is rejected in a similar manner.
Conclusion
Pertinent prior art made of record however relied upon:
US 20240020105 A1 to Palamadai et al.
“Techniques are described for providing targeted software and hardware updates for user equipment (UE) based on specific user profiles related to their usage thereof. In one example embodiment, a system includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. These operations include clustering user identities into different user groups based on different goals determined to be applicable to the user identities related to usage of user equipment respectively associated with the user identities. The operations further include determining different equipment updates for the user equipment tailored to the different user groups, and provisioning the different equipment updates to the user equipment corresponding to the different user groups.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phuc Pham whose telephone number is (571)272-8893. The examiner can normally be reached Monday - Thursday 7:30 AM - 4:30 PM; Friday 8:00 AM - 12:00 PM.
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, Linglan Edwards can be reached at (571) 270-5440. 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.
/P.P./Patent Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408