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 .
1. This is in response to communication filed 7/21/25 in which claims 1-13 and 15-17 are pending.
Response to Arguments
2. Applicant’s arguments with respect to claims 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.
Drawings
3. The drawings are objected to because the unlabeled rectangular boxes shown in the drawings should be provided with descriptive text labels. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
4. 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-2, 6, 13, 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2018/0278486 to Mermoud et al.
a. As per claim 1, Mermoud et al teaches a method for predicting a quality of service of a communication service (See paragraph [0073]), the method comprising: receiving data for predicting the quality of service of the communication service (See paragraph [0009], a device in a network receives data regarding a plurality of predefined health status rules that evaluate one or more observed conditions of the network); processing the data for predicting the quality of service of the communication service by a hybrid machine learning model to generate a prediction of the quality of service (QoS) of the communication service (See paragraph [0056]), wherein the hybrid machine-learning model includes: a first module configured to determine and/or predict one or more characteristics of the communication service, wherein the first module is not trained and encodes expert knowledge concerning the communication service in an algorithm configured to determine and/or predict the one or more characteristics of the communication service module (See paragraph [0054], predefined network health status rules may be manually designed for a network assurance system by administrator with a deep knowledge of real-world problems, operation of the monitored network, and remediation or mitigation strategies); and a trained second module coupled to the first module, wherein the trained second module receives data from the first module and/or provides data to the first module for predicting of the quality of service of the communication service (See paragraph [0060, 0061 and 0063]); and wherein the data provided to the trained second module and/or the data received from the trained second module is processed based on the encoded expert knowledge by executing a defined set of rules and/ or a defined sequence of steps executed by the first module (See paragraph [0060, 0066 and 0068]).
b. As per claim 2, Mermoud et al teaches the claimed invention as described above. Furthermore, Mermoud et teaches wherein the trained second module generates input data for the first module and/or wherein the first module generates input data for the trained second module (See paragraph [0034-0035]).
c. As per claim 6, Mermoud et al teaches the claimed invention as described above. Furthermore, Mermoud et al teaches wherein the data for predicting the quality of service of the communication service includes one or more of information regarding a user equipment taking part in the communication service, information regarding network equipment involved in the communication service, requirement specifications for the communication service and external information characterizing an environment of the communication service, wherein the external information is based on sensor data obtained by sensors in the environment of a component involved in the communication service, the component selected from a group consisting of a user equipment and vehicle (See paragraph [0011]).
d. As per claim 13, Mermoud et al teaches a system for predicting a quality of service of a communication service, the system comprising: an electronic processor (See paragraph [0027 and 0029]; a memory (See paragraph [0027]); a hybrid machine-learning model (See paragraph [0055], Mixing Rule-Based and Machine Learning-Based Indicators in Network Assurance Systems) comprising: a first module configured to determine and/or predict one or more characteristics of the communication service (See paragraph [0060]), wherein the first module is not trained and encodes expert knowledge concerning the communication service in an algorithm configured to determine and/or predict the one or more characteristics of the communication service (See paragraph [0054], predefined network health status rules may be manually designed for a network assurance system by administrator with a deep knowledge of real-world problems, operation of the monitored network, and remediation or mitigation strategies) wherein expert knowledge includes technical or physical causalities related to a condition leading to a characteristic of the communication service, wherein the first module, using a defined set of rules the received data defines a sequence of events related to the condition leading to the characteristic of the communication service and determines and/ or predicts the one or more characteristics of the communication service based on the sequence of events (See paragraph [0063]); and a trained second module coupled to first module and configured to receive data from the first module and configured to provide data to the first module for predicting of the quality of service (QoS) of the communication service, wherein the electronic processor is configured to execute the hybrid machine-learning model by receiving data for predicting the quality of service of the communication service ((See paragraph [0060, 0061 and 0063]) and processing the data for predicting the quality of service of the communication service (See paragraph [0073]); wherein the trained second module receives data from the first module and/or provides data to the first module for predicting of the quality of service of the communication service. (See paragraph [0060-0061 and 0063]), and wherein the data provided to the trained second module and/ or the data received from the trained second module is processed based on the encoded expert knowledge by executing the defined set of rules and/ or a defined sequence of steps by the first module (See paragraph [0060, 0066 and 0068]).
e. As per claim 15, Mermoud et al teaches a non-transitory computer-readable medium or signal containing instructions that when executed by a computer cause the computer to predict a quality of service of a communication service, by: receiving data for predicting the quality of service of the communication service (See paragraph [0073]); processing the data for predicting the quality of service of the communication service via a hybrid machine learning model to generate a prediction of the quality of service (QoS) of the communication service (See paragraph [0056]), wherein the hybrid machine-learning model includes: a first module configured to determine and/or predict one or more characteristics of the communication service, wherein the first module is not trained and encodes expert knowledge concerning the communication service in an algorithm configured to determine and/or predict the one or more characteristics of the communication service (See paragraph [0054], predefined network health status rules may be manually designed for a network assurance system by administrator with a deep knowledge of real-world problems, operation of the monitored network, and remediation or mitigation strategies) and a trained second module coupled to the first module, wherein the trained second module receives data from the first module and/or provides data to the first module for predicting of the quality of service of the communication service (See paragraph [0061-0061 and 0063]), and wherein the data provided to the trained second module and/or the data received from the trained second module is processed based on the encoded expert knowledge by executing a defined sets of rules and /or a defined sequence of steps executed by the first module (See paragraph [0060, 0066 and 0068]).
f. As per claim 16, Mermoud et al teaches the claimed invention as described above. Furthermore, Mermoud teaches wherein expert knowledge includes technical or physical causalities related to a condition leading to a characteristic of the communication service (See paragraph [0054], predefined network health status rules may be manually designed for a network assurance system by administrator with a deep knowledge of real-world problems, operation of the monitored network, and remediation or mitigation strategies)
g. As per claim 17, Mermoud et al teaches the claimed invention as described above. Furthermore, Mermoud et al teaches wherein the first module, using a defined set of rules the received data, defines a sequence of events related to the condition leading to the characteristic of the communication service and determines and/or predicts the one or more characteristics of the communication service based on the sequence of events (See paragraph [0009, 0032 and 0050]).
h. As per claim 18, Mermoud et al teaches the claimed invention as described above. Furthermore, Mermoud et al teaches wherein the first module is configured to determine and/or predict a connection failure or service interruption based on a relative speed between the user equipment and network equipment exceeding a threshold (See paragraph (See paragraph [0033, 0066, 0069, 0072]).
i. As per claim 19, Mermoud et al teaches the claimed invention as described above. Furthermore, Mermoud et al teaches wherein the data exchange between the trained second module and the first module includes multiple stages of exchanging data in the process of predicting the quality of service of the communication service, wherein the first module is configured to determine and/or predict one or more characteristics of the communication service based on the received data and the data received from the trained second module including a future value of the received data (See paragraph [0037-0038]).
Claim Rejections - 35 USC § 103
6. 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.
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.
7. Claims 3-5, 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2018/0278486 to Mermoud et al in view of U.S. Publication No. 2021/0204152 to Vasudevan et al.
a. As per claim 3, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach wherein the trained second module is configured to receive a subset of the data for predicting the quality of service of the communication service and to predict one or more parameters of the communication service.
Vasudevan et al teaches wherein the trained second module is configured to receive a subset of the data for predicting the quality of service of the communication service and to predict one or more parameters of the communication service (See paragraph [0012, 0067 and 0082]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Mermoud et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
b. As per claim 4, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach wherein the first module receives the predicted one or more parameters of the communication service and determines and/or predicts the one or more characteristics of the communication service.
Vasudevan et al teaches wherein the first module receives the predicted one or more parameters of the communication service and determines and/or predicts the one or more characteristics of the communication service (See paragraph [0007-0008 and 0061]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Mermoud et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
c. As per claim 5, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach wherein the trained second module includes a first sub-module which receives a subset of the data for predicting the quality of service of the communication service and determines and/or predicts one or more parameters of the communication service, and a second sub-module which receives the determined and/or predicted one or more characteristics of the communication service and predicts the quality of service of the communication service.
Vasudevan teaches wherein the trained second module includes a first sub-module which receives a subset of the data for predicting the quality of service of the communication service and determines and/or predicts one or more parameters of the communication service, and a second sub-module which receives the determined and/or predicted one or more characteristics of the communication service and predicts the quality of service of the communication service (See paragraph [0012, 0061—0062]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Mermoud et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
d. As per claim 7, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach wherein the first module is configured to determine and/or predict one or more characteristics including a service interruption, a service failure, a normal service and a state being the result of a rare event.
Vasudevan et al teaches wherein the first module is configured to determine and/or predict one or more characteristics including a service interruption, a service failure, a normal service and a state being the result of a rare event. (See paragraph [0012, 0026, 0069, 0071-0072], The anomaly detector 174 can be used to improve the machine learning model 176 and the system 100 automatically. The anomaly detector 174 can be configured to filter out or label irregular traffic, e.g., traffic that does fit expected patterns. For example, regular traffic can be traffic patterns that have a distribution that matches or is within a threshold level of similarity to a distribution observed in training data for the machine learning traffic classifier model. The anomaly detector 174 can be or can include a machine learning model, such as a neural network, that evaluates the extent of this similarity).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Tapia et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
e. As per claim 8, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach wherein the first module encodes expert knowledge regarding a communication protocol employed in the communication service, optionally a sequence of events being defined in the communication protocol.
Vasudevan et al teaches wherein the first module encodes expert knowledge regarding a communication protocol employed in the communication service, optionally a sequence of events being defined in the communication protocol (See paragraph [0066-0067], Changes in protocols, such as from TCP to QUIC, can be addressed by collecting new data and training the model by adjusting the model parameters or adjusting the rules and/or conditions according to the new traffic patterns).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Mermoud et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
f. As per claim 9, Tapia et al teaches the claimed invention as described above. However, Tapia et al fails to explicitly teach wherein the first module is configured to determine and/or predict one or more of a handover of a user equipment involved in the communication service or service interruption or a connection failure, based on insufficient network coverage, a predetermined event in the environment of the communication service.
Vasudevan et al teaches wherein the first module is configured to determine and/or predict one or more of a handover of a user equipment involved in the communication service, a service interruption or a connection failure, optionally a connection failure, a connection failure or service interruption due to insufficient network coverage, or a connection failure or service interruption due to a predetermined event in the environment of the communication service (See paragraph [0011, 0057, 0072, 0085]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Tapia et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
g. As per claim 10, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach a method for improving a quality of a communication service; comprising: predicting a quality of service of a communication service according to claim 1; and triggering a response if the predicted quality of service of the communication service fulfills one or more predetermined criteria.
Vasudevan et al teaches a method for improving a quality of a communication service; comprising: predicting a quality of service of a communication service according to claim 1; and triggering a response if the predicted quality of service of the communication service fulfills one or more predetermined criteria (See paragraph [0061 and 0101], The model update module 225 will collect information about such events and trigger an update to the anomaly detector and traffic classifier models).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Tapia et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
h. As per claim 11, Mermoud et al teaches the claimed invention as described above. However, Mermoud et al fails to explicitly teach wherein the response includes one or more of a measure to counter-act a predicted drop in quality of service or a measure to mitigate a predicted drop in quality of service
Vasudevan et al teaches wherein the response includes one or more of a measure to counter-act a predicted drop in quality of service or a measure to mitigate a predicted drop in quality of service (See paragraph [0011, 0049]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Vasudevan et al in the claimed invention of Mermoud et al in order to o assign a quality of service class or priority for service in the network (See paragraph [0003]).
8. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2018/0278486 to Mermoud et al in view of U.S. Publication No. 2021/0204152 to Vasudevan et al as applied to claim 1 above and further in view of U.S Publication No. 2006/0104230 to Gidwani.
a. As per claim 12, Mermoud et al in view of Vasudevan et al teaches the claimed invention as described above. However, Mermoud et al fails to teach wherein the response includes switching a communication channel used to deliver the communication service; establishing an additional communication channel for the communication service; adapting one of more control parameters of the communication service; and adapting an admission control of users of the communication service; adapting an employment of network resources used to deliver the communication service.
Gidwani teaches wherein the response includes switching a communication channel used to deliver the communication service; establishing an additional communication channel for the communication service; adapting one of more control parameters of the communication service; and adapting an admission control of users of the communication service; adapting an employment of network resources used to deliver the communication service (See paragraph [0252-0253, 0134 and 0138]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Gidwani in the claimed invention of Mermoud et al in view of Vasudevan et al in order to support multiple applications, multiple streams in a scalable and reliable manner.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DJENANE BAYARD whose telephone number is (571)272-3878. The examiner can normally be reached 9-5.
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/DJENANE M BAYARD/Primary Examiner, Art Unit 2444