DETAILED ACTION
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 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.
Claim 19 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
As to claim 19, the claim language is contradictory and, therefore, is ambiguous. In particular, it is unclear how a “single and integrated” ML model can comprise multiple parts that are ML models because “single and integrated” means there are not parts. Appropriate correction or explanation is required.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-12 and 14-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kokkula et al. (US 2017/0111233 A1).
As to claim 1, Kokkula teaches a method, comprising:
predicting, by a computing system, future provisioning demands for an Internet protocol ("IP") -based communications system based on analysis of past IP-based communications patterns and based on at least one of one or more trigger events or analysis of current network condition data and current event data [predicting future load and demand and balancing of current load at various network devices] (par. [0043]-[0044]);
identifying, by the computing system, first resource allocation based on the predicted future provisioning demands for the IP-based communications system [capacity planning for future demand] (par. [0044]-[0045]); and
initiating, by the computing system, changes in allocation of network resources for the IP-based communications system based on the identified first resource allocation, by performing at least one of instructing mobilization of more network resources in one or more first locations, instructing reassignment of network resources in one or more second locations, instructing reduction of network resources in one or more third locations, adapting network routing, changing IP-based communications routing, or implementing load balancing of network resources [dynamically allocating licenses, traffic profiles, caching and compression techniques] (par. [0045]-[0047]).
As to claim 2, Kokkula teaches that the IP-based communications system comprises at least one of a voice over Internet Protocol ("VoIP") communications system, an IP-based video communications system, or a unified communications and collaboration ("UC&C") communications system, wherein the UC&C communications system includes two or more of a voice service platform, a VoIP platform, an email platform, an instant messaging or chat platform, a collaboration facilitator platform, a web conferencing platform, an audio conferencing platform, or a video conferencing platform (par. [0020], [0042]).
As to claim 3, Kokkula teaches that the current network condition data includes at least one of current network traffic data, current call volume data, current call routing data, current quality of service ("QOS") data, wherein the past IP-based communications patterns each includes at least one of network traffic patterns, call volume patterns, call routing patterns, or QOS change patterns, wherein the current network traffic data includes at least one of network congestion data, network failure data, network failover data, or unresponsive network node data, wherein the current QOS data includes at least one of latency data, jitter data, packet loss data, bit rate data, throughput data, transmission delay data, availability data, service response time data, signal-to-noise ratio ("SNR") data, or loudness level data (par. [0042]-[0043], [0045], [0052] Table 1).
As to claim 4, Kokkula teaches that the future provisioning demands include at least one of future VoIP call volumes, future VoIP call durations, future VoIP call destinations, future IP-based video call volumes, future IP-based video durations, future IP-based video destinations, future network traffic volume, future network peak traffic durations, or future network traffic concentrations (par. [0059] Table 2).
As to claim 5, Kokkula teaches that the current event data includes at least one of current network event data, current news data, current weather event data, current natural disaster alert data, current manmade emergency alert data, or current social event data, wherein the current network event data includes at least one of power outage data, fiber cut data, or communications line damage data, wherein the current social event data includes at least one of entity-wide call meeting invitation, entity-wide work from home alert, or entity-wide shelter at home alert, community-wide shelter at home alert, area wide sporting event alert, area wide concert alert, area wide dignitary visit alert, area wide parade alert, area wide holiday alert, area wide road condition alert, area wide power outage alert, area wide disaster alert, or area wide terrorist alert, wherein the current network condition data or the current network event data includes current trigger event data, which corresponds to one or more trigger events including at least one of a successful call event, an unsuccessful call event, or an abnormal call event (par. [0042]-[0045], [0053]).
As to claim 6, Kokkula teaches monitoring or collecting, by the computing system, current network condition data and current event data, wherein the current network condition data includes at least one of data collected by one or more network gateway devices [gateway 120], data collected from one or more soft switches [switch module 320], data collected from one or more session border controllers ("SBCs"), data collected from call detail records ("CDRs"), data collected from log files, or simple network management protocol ("SNMP") data; and analyzing, by the computing system, at least one of the current network condition data or the current event data to identify the one or more trigger events (par. [0042]-[0045], [0053]).
As to claim 7, Kokkula teaches performing at least one of: analyzing, by the computing system, historical network data and historical event data to identify the past IP-based communications patterns (par. [0055]); or determining, by the computing system, whether the predicted future IP-based communications patterns necessitate changes to network resource provisioning (par. [0045]).
As to claim 8, Kokkula teaches training or updating a first machine learning ("ML") model to predict the future IP-based communication patterns based on analysis of past IP-based communications patterns and based on one or more trigger events that are identified from analysis of current network condition data and current event data [module training] (par. [0055]); wherein predicting the future IP-based communications patterns comprises predicting, by the computing system and utilizing the first ML model, the future IP-based communication patterns based on analysis of past IP-based communications patterns and based on one or more trigger events that are identified from analysis of current network condition data and current event data (par. [0059] Table 2).
As to claim 9, Kokkula teaches training or updating a second ML model to identify second resource allocation based on the predicted future provisioning demands [utilizing a model to dynamically allocate network resources] (par. [0044]-[0045]); wherein identifying the first resource allocation comprises identifying, by the computing system and utilizing the second ML model, the second resource allocation based on the predicted future provisioning demands [different models are utilized for different tasks] (par. [0059] Table 2).
As to claim 10, Kokkula teaches correlating, by the computing system, one or more first IP-based communications patterns among the past IP-based communications patterns with a particular entity based on at least one of one or more telephone numbers, a trunk group, or a fully qualified domain name ("FQDN") each associated with the particular entity [detection of a network traffic pattern; based on the determined content type and the associated traffic pattern, a set of configurations by the appliance can be determined] (par. [0042]); wherein predicting future provisioning demands comprises predicting, by the computing system and utilizing the first ML model, future provisioning demands by the particular entity based on the one or more first IP-based communications patterns [prediction of future load] (par. [0043]); wherein identifying the first resource allocation comprises identifying, by the computing system and utilizing the second ML model, third resource allocation based on the predicted future provisioning demands by the particular entity [utilizing a model to dynamically allocate network resources] (par. [0044]-[0045]); and wherein initiating changes in allocation of network resources comprises initiating, by the computing system, changes in allocation of network resources for the IP-based communications system for meeting the predicted future provisioning demands by the particular entity based on the identified third resource allocation [resource allocation is performed dynamically] (par. [0045]-[0046]).
As to claim 11, Kokkula teaches receiving, by the computing system, QOS results in response to a preceding set of initiated changes in allocation of network resources [updating the models periodically with the current network metrics data] (par. [0058]); and generating, by the computing system, data based on the received QOS results, wherein the data is stored in a ML data store as metadata that is used by the second ML for training [training models based on metrics data] (par. [0058]); wherein training or updating the second ML model to identify the second resource allocation is further based on the metadata (par. [0052] Table 1).
As to claim 12, Kokkula teaches analyzing, by the computing system and using a third ML model, network performance data to identify network bottlenecks [anomaly detection] (par. [0063]-[0064]); and in response to identifying one or more network bottlenecks, dynamically routing, by the computing system, network traffic for the IP-based communications system around the identified one or more network bottlenecks [routing the traffic away from the overloaded network device] (par. [0064]).
As to claim 14, Kokkula teaches a system (Figs. 1B, 3), comprising:
a processing system (par. [0004]); and
memory coupled to the processing system, the memory comprising computer executable instructions that, when executed by the processing system (par. [0004]), causes the system to perform operations comprising:
monitoring or collecting current network condition data and current event data (par. [0041]);
identifying past Internet protocol ("IP") -based communications patterns for an IP-based communications system based on analysis of historical network data and historical event data (par. [0058]);
predicting future provisioning demands for the IP-based communications system based on analysis of the past IP-based communications patterns and based on analysis of current network condition data and current event data (par. [0058]-[0059], Table 2);
determining whether the predicted future IP-based communications patterns necessitate changes to network resource provisioning [dynamic allocation of resources] (par. [0044]-[0045]); and
based on a determination that the predicted future IP-based communications patterns necessitate changes to network resource provisioning, performing the following tasks:
identifying first resource allocation based on the predicted future provisioning demands for the IP-based communications system (par. [0044]-[0045]); and
initiating changes in allocation of network resources for the IP-based communications system based on the identified first resource allocation [dynamically changing license requirement, capacity allocation, traffic profiles, etc.] (par. [0044]-[0046]).
As to claims 15-16, Kokkula teaches all the elements, as discussed per corresponding method claims 8 and 12 above.
As to claim 17, Kokkula teaches a method, comprising:
monitoring or collecting, by a computing system, current network condition data and current event data (par. [0041]);
predicting, by the computing system and using a first machine learning ("ML") model, future provisioning demands for an Internet protocol ("IP") -based communications system based on analysis of past IP-based communications patterns and based on analysis of current network condition data and current event data par. [0058]-[0059], Table 2);
identifying, by the computing system and using a second ML model, first resource allocation based on the predicted future provisioning demands for the IP-based communications system (par. [0044]-[0045]); and
initiating, by the computing system, changes in allocation of network resources for the IP-based communications system based on the identified first resource allocation [dynamically changing license requirement, capacity allocation, traffic profiles, etc.] (par. [0044]-[0046]).
As to claims 19-20, Kokkula teaches all the elements, as discussed per corresponding method claims 8-9 and 11 above. It is noted that as to claim 19, having multiple models grouped and called a “fourth” model is a mere nametag that does not require a transformation. In particular, the claim does not recite (or require) a step of integrating multiple disparate models into a single model.
Claim Rejections - 35 USC § 103
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.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kokkula et al. in view of Uppal et al. (US Patent 10,778,757 B1).
As to claim 13, Kokkula teaches that initiating changes in allocation of network resources for the IP-based communications system comprises performing updating, by the computing system, records to indicate caching decision (par. [0047]).
Kokkula fails to expressly teach updating a time-to-live ("TTL") value for the DNS records to indicate how long the user devices should cache information obtained from the DNS records.
Uppal is directed to load balancing traffic via dynamic DNS records TTLs (abstract). In particular, Uppal teaches updating, by the computing system, time-to-live ("TTL") value for the DNS records to indicate how long the user devices should cache information obtained from the DNS records (Fig. 2, col. 14 line 64 to col. 15 line 23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system of Kokkula by updating, by the computing system, time-to-live ("TTL") value for the DNS records to indicate how long the user devices should cache information obtained from the DNS records in order to perform caching decisions in Kokkula utilizing the DNS load balancing technique of Uppal (col. 8 lines 8-24 in Uppal).
Relevant Prior Art
Kocberger et al. (US 2025/0224993 A1) is directed to optimizing resources such as CPU, memory, I/O allocated to a database server using one more machine learning models (abstract, par. [0015]). Therefore, Kocberger is deemed relevant to the pending claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLEG SURVILLO whose telephone number is (571)272-9691. The examiner can normally be reached 9:00am - 5:00pm.
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, Ario Etienne can be reached at 571-272-4001. 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.
/OLEG SURVILLO/Primary Examiner, Art Unit 2457
Prosecution Insights