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 .
Claims 1-27 are presented.
Amendments as filed 03/13/2026 is/are entered
Response to Arguments
Remarks dated 03/13/2026 have been fully considered but they are not persuasive.
Specifically Applicant contends the reference Majjiga does not disclose “automatically generating network slices predicted to be required for the future period of time from slice blueprints to which the predicted slices correspond and the predicted slice parameters corresponding to predicted slices”.
Specifically, per page 11 and 12 of the Remarks, Applicant contends Majjiga merely discloses generation of the time slot prediction, not the generation of the network slices.
Applicant argues “While the reference indicates that the time slot prediction that is generated may include "a predicted network slice" this merely indicates what slice is being
predicted. The network slice which is "predicted" is NOT generated as part of generating the time slot prediction as the Examiner seems to assert. As explained in the abstract the time slot prediction specifies "a network slice". They prediction does not generate the network slice”
The examiner respectfully disagrees. ¶0079 of Majjiga explicitly said “Process 700 may further include generating a set of time slot predictions” and that “The time slot prediction may further include a predicted network slice”. The language is crystal clear: the time slot prediction is generated, and said prediction also include a predicted network slice. Thus the predicted network slice is part of said generating.
Furthermore, Applicant’s interpretation of the generating step is not consistent with the claim language. Applicant appears to take on a narrow reading of the wording “generating network slices” to imply the network slices ‘being originally created from scratch’. This reading cannot reconcile with the actual claim limitation which reads “generating network slices predicted to be required for the future period of time”, which implies these network slices must have been first predicted before generation step (i.e. already existed or at formulated at the time of prediction.) by Applicant’s very same logic applied in the argument presented. As such, in this case, the generating step can simply means generating prediction data indicative of the predicted network slices.
Per ¶0068 of Majjiga, the predicted slices are not just previously used slices, they can be formulated based on dynamic combination of historical usage parameters such as KPI values, latency, throughput, error rate. Since these parameters combinations are dynamic and unique per application, the predicted slices can also include dynamically formulated slices.
As such, in either reading of the wording “generating network slices”, Majjiga covers the limitation adequately
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are in claims 15-20.
Specifically:
a network communications core;
an artificial intelligence engine;
a model training module;
OSS (operations support system);
Terminologies such as core, engine, module, and system are generic placeholders that do not define a specific structures.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-27 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 recites “the AI engine” and “the slice utilization”. Claim 21, as well as the base claim 1, have failed to establish proper antecedent basis for claim 21.
Claims 22-27 inherit the short comings above in light of dependency and fall together with base claim 21.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 5-9, and 15 is/are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Majjiga et al. (US 2024/0205810).
As to claim 1:
Majjiga discloses:
A communications method, the method comprising:
collecting information relating to UE use of a communications network during a first period of time;
(¶0047, 0066, 0069, collecting various performance associated with historical slice usage by users across various types of KPIs (connection drop rate, throughput, latency etc.) during one or more historical periods to create historical slice usage. Historical slice usage data can be used to train a predictive machine learning model)
training a slice and slice parameter prediction model using the information relating to UE use of the communications network during the first period of time;
(¶0066, 0069, the machine learning model is trained using the training set of historical slice usage, and is configured to predict one or more network slice for use in a future time slot for client device UAV 110)
operating an artificial intelligence engine to use the slice and slice parameter prediction model to automatically predict slices and slice parameters corresponding to predicted slices which are required for a future period of time;
(¶0069, slice predictor 560 may predict a network slice, that is to be selected, which satisfies the service requirements associated with application (e.g., latency) at a predicted location and time while using the least amount of network resources. The predictor 560 uses a trained machine learning model to predict a network slice. slice predictor 560 may predict a ranked list of network slices that are to be selected for UAV 110 for a particular time slot)
and automatically generating network slices predicted to be required for the future period of time from slice blueprints to which the predicted slices correspond and the predicted slice parameters corresponding to predicted slices.
(¶0079, generating said predicted network slices for use by client devices 110s. ¶0069, a network slice, that is to be selected, which satisfies a blue print , namely ‘the service requirements associated with application (e.g., latency) at a predicted location and time while using the least amount of network resources’ and following rules of a ranked list of network slices that are to be selected for UAV 110 for a particular time slot)
As to claim 15:
Majjiga discloses:
A communications system, the system comprising:
a network communications core (Fig. 2, core 150) configured to collect information relating to UE use of a communications network during a first period of time; (¶0047, 0066, 0069, core network collecting various performance associated with historical slice usage by users across various types of KPIs (connection drop rate, throughput, latency etc.) during one or more historical periods to create historical slice usage. Historical slice usage data can be used to train a predictive machine learning model)
and an artificial intelligence engine including: a model training module configured to train a slice and slice parameter prediction model using collected information relating to UE use of the communications network during the first period of time; (¶0066, 0069, the machine learning model is trained using the training set of historical slice usage, and is configured to predict one or more network slice for use in a future time slot for client device UAV 110)
a slice and slice parameter prediction model configured to automatically predict slices and slice parameters corresponding to predicted slices, which are required for a future period of time; (¶0069, slice predictor 560 may predict a network slice, that is to be selected, which satisfies the service requirements associated with application (e.g., latency) at a predicted location and time while using the least amount of network resources. The predictor 560 uses a trained machine learning model to predict a network slice. slice predictor 560 may predict a ranked list of network slices that are to be selected for UAV 110 for a particular time slot)
and an OSS in said network communications core configured to automatically generate network slices predicted to be required for the future period of time from slice blueprints to which the predicted slices correspond and the predicted slice parameters corresponding to predicted slices. (¶0079, generating said predicted network slices for use by client devices 110s. ¶0069, a network slice, that is to be selected, which satisfies a blue print , namely ‘the service requirements associated with application (e.g., latency) at a predicted location and time while using the least amount of network resources’ and following rules of a ranked list of network slices that are to be selected for UAV 110 for a particular time slot)
As to claim 5:
Majjiga discloses all limitations of claim 1, wherein training the slice and slice parameter prediction model includes using collected information corresponding to the first period of time as training data, different sets of collected information corresponding to different network slices used during said first period of time being used as labeled training data to train the slice and slice prediction model. (See Majjiga, ¶0069, “The machine learning model may be trained using a training set of historical slice usage data labeled by a domain expert with manually selected network slices for particular combinations of application requirements, locations, and KPI values. In some implementations”, ¶0068, “Historical slice usage DB 565 may store historical slice usage for different applications, locations, and/or time periods”, i.e. different slices at different locations/periods)
As to claim 6:
Majjiga discloses all limitations of claim 5, wherein collected slice resource utilization information includes slice parameter information; and wherein training the slice and slice parameter prediction model includes using a value in the slice parameter as a labeled parameter value during training of the slice and slice parameter prediction model (See Majjiga, ¶0069, “The machine learning model may be trained using a training set of historical slice usage data labeled by a domain expert with manually selected network slices for particular combinations of application requirements, locations, and KPI values. In some implementations”.)
As to claim 7:
Majjiga discloses all limitations of claim 1, wherein automatically predicting, using an artificial intelligence engine, a set of network slices which will be required during the future period of time includes predicting types of slices and slice parameters for one or more of the different predicted types of slices. (¶0079, generating said predicted network slices for use by client devices 110s. ¶0045, 0093-0097, generating the network slices which includes a plurality of S-NSSAI, each associated with a future time/location. S-NSSAI intrinsically includes a SST value that indicates slice type. Slice parameters include identifier as well as associated TAI-n)
As to claim 8:
Majjiga discloses all limitations of claim 7, wherein predicted slice parameters a parameter indicating a function type to be implemented. (See at least ¶0093, a parameter indicating whether roaming feature is required, i.e. roaming is a type of function to allow seamless transfer of coverage/service between home network and a foreign/visited network. Furthermore, ¶0016, 0017 indicates the network slices have parameters indicative a service (i.e. function) type, i.e. URLL communication, gaming, V2X etc.)
As to claim 9:
Majjiga discloses all limitations of claim 1, further comprising: collecting communications network information and slice utilization information during an initial period of time; operating the artificial intelligence engine to perform machine learning in the form of clustering to cluster the collected communications network information and corresponding slice utilization information corresponding to the initial period of time; (See ¶0068, “Historical slice usage DB 565 may store historical slice usage for different applications, locations, and/or time periods. For example, historical slice usage DB 565 may store a set of KPI values, such as latency, throughput, packet drop rate, packet error rate, etc. for an application using a network slice at a location”. The ‘initial period of time’ can be read as any of the previous historical time period corresponding the respective historical slide usage set of data.)
defining a default set of slice blueprints, generating a default set of slices from the default set of slice blueprints; and using the default set of slices during said first period of time (¶0079, generating said predicted network slices for use by client devices 110s. ¶0069, a network slice, that is to be selected, which satisfies a blue print , namely ‘the service requirements associated with application (e.g., latency) at a predicted location and time while using the least amount of network resources’ and following rules of a ranked list of network slices that are to be selected for UAV 110 for a particular time slot. ¶0093, “slices updates field 1160 store information identifying whether any network slices were updated for the time slot”, which implicitly indicates the presence of initial set of network slices, thus “updates”)
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.
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.
Claim(s) 2-4, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjiga et al. (US 2024/0205810) in view of Kwok et al. (US 2024/0406069).
As to claim 2:
Majjiga discloses all limitations of claim 1, wherein collecting information relating to UE use of the communications network during the first period of time includes:
collecting quality of service information corresponding to the UEs accessing the communications network during the first period of time; (¶0047, “service integrity KPIs (e.g., downlink average throughput, downlink maximum throughput, uplink average throughput, uplink maximum throughput, packet drop rate, etc.”, “radio link latency, transport network latency, end-to-end latency, packet arrival time, etc.”)
and collecting information on slice utilization during said first period of time (¶0047, 0068, “ traffic KPIs (e.g., downlink traffic volume/throughput, uplink traffic volume/throughput, average number of users, maximum number of users, a number of voice bearers, a number of video bearers, etc.”)
Majjiga discloses a non-limiting example list of historical usage information used as a basis to update/train the machine learning model as discussed extensively above, however is silent on specific ones such as:
“collecting UE device information corresponding to UEs accessing a communications network during a first period of time;
collecting UE application type information indicting the type or types of applications being used by UEs accessing the communications network during the first period of time; ”, as claimed.
Kwok, in a related field of network slicing using AI, discloses a system/method for network slicing using AI, wherein the AI model is trained by training data such as previous processing (¶0031), wherein previous sessions including collecting data:
collecting UE device information corresponding to UEs accessing a communications network during a first period of time; (¶0025, 0018-0019, 0012, collecting information from a previous time period including data regarding the UE devices such as location, device information, IDs)
collecting UE application type information indicting the type or types of applications being used by UEs accessing the communications network during the first period of time; (¶017, 0018, 0025, list of previous applications and their performance/activities/types)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the historical usage information in Majjiga’s system to further include device information and application types information as in Kwok. This implementation advantageously train the model to adaptively allocate resources for user devices to match application requirements, as Kwok said in ¶008, “the techniques can improve network capacity, security, and/or quality of service for various services and/or applications the UE may request in the future”.
As to claim 3:
Majjiga in view of Kwok discloses all limitations of claim 2, wherein the collected information on slice utilization includes information on which network slices were utilized. (See Majjiga, ¶0068, “historical slice usage DB 565 may store a set of KPI values, such as latency, throughput, packet drop rate, packet error rate, etc. for an application using a network slice at a location”, which indicates a particular network slice being associated with the set of KPI. See Kwok, also ¶0031, collected data includes “previous output by the server”, i.e. including previous outputted network slices)
As to claim 4:
Majjiga in view of Kwok discloses all limitations of claim 3, wherein the collected information on slice utilization includes information on the resources corresponding to each of the utilized slices which were used during said first period of time, different resources corresponding to different slice parameters.
(Majjiga, ¶0047, information on the resources which were used during the previous time, “traffic KPIs (e.g., downlink traffic volume/throughput, uplink traffic volume/throughput”, ¶0068, “Historical slice usage DB 565 may store historical slice usage for different applications, locations, and/or time periods”, i.e. different slices at different locations/periods)
As to claim 16:
Majjiga discloses all limitations of claim 15, wherein the network core includes: AMF and UPF (Fig. 2/3, AMF, UPF as entry points for collecting data from access network 210), and collecting quality of service information corresponding to the UEs accessing the communications network during the first period of time; (¶0047, “service integrity KPIs (e.g., downlink average throughput, downlink maximum throughput, uplink average throughput, uplink maximum throughput, packet drop rate, etc.”, “radio link latency, transport network latency, end-to-end latency, packet arrival time, etc.”)
Majjiga discloses a non-limiting example list of historical usage information used as a basis to update/train the machine learning model as discussed extensively above, however is silent on specific ones such as:
“collecting UE device information corresponding to UEs accessing a communications network during a first period of time;
collecting UE application type information indicting the type or types of applications being used by UEs accessing the communications network during the first period of time; ”, as claimed.
Kwok, in a related field of network slicing using AI, discloses a system/method for network slicing using AI, wherein the AI model is trained by training data such as previous processing (¶0031), wherein previous sessions including collecting data:
collecting UE device information corresponding to UEs accessing a communications network during a first period of time; (¶0025, 0018-0019, 0012, collecting information from a previous time period including data regarding the UE devices such as location, device information, IDs)
collecting UE application type information indicting the type or types of applications being used by UEs accessing the communications network during the first period of time; (¶017, 0018, 0025, list of previous applications and their performance/activities/types)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the historical usage information in Majjiga’s system to further include device information and application types information as in Kwok. This implementation advantageously train the model to adaptively allocate resources for user devices to match application requirements, as Kwok said in ¶008, “the techniques can improve network capacity, security, and/or quality of service for various services and/or applications the UE may request in the future”.
As to claim 17:
Majjiga in view of Kwok discloses all limitations of claim 16, wherein the network core further includes an OSS for capturing network slice utilization information. (See Majjiga, ¶0068, “historical slice usage DB 565 may store a set of KPI values, such as latency, throughput, packet drop rate, packet error rate, etc. for an application using a network slice at a location”, which indicates a particular network slice being associated with the set of KPI. See Kwok, also ¶0031, collected data includes “previous output by the server”, i.e. including previous outputted network slices)
As to claim 18:
Majjiga in view of Kwok discloses all limitations of claim 17, wherein the collected information on slice utilization includes information on the resources corresponding to each of the utilized slices which were used during said first period of time, different resources corresponding to different slice parameters.
(Majjiga, ¶0047, information on the resources which were used during the previous time, “traffic KPIs (e.g., downlink traffic volume/throughput, uplink traffic volume/throughput”, ¶0068, “Historical slice usage DB 565 may store historical slice usage for different applications, locations, and/or time periods”, i.e. different slices at different locations/periods)
As to claim 19:
Majjiga in view of Kwok discloses all limitations of claim 16, wherein training the slice and slice parameter prediction model includes using collected information corresponding to the first period of time as training data, different sets of collected information corresponding to different network slices used during said first period of time being used as labeled training data to train the slice and slice prediction model. (See Majjiga, ¶0069, “The machine learning model may be trained using a training set of historical slice usage data labeled by a domain expert with manually selected network slices for particular combinations of application requirements, locations, and KPI values. In some implementations”, ¶0068, “Historical slice usage DB 565 may store historical slice usage for different applications, locations, and/or time periods”, i.e. different slices at different locations/periods)
As to claim 20:
Majjiga in view of Kwok discloses all limitations of claim 19, wherein collected slice resource utilization information includes information which can be specified in a slice parameter, said information which can be specified in a slice parameter being used as a labeled parameter value corresponding to the particular network slice to which said information which can be specified in a slice parameter corresponds. (See Majjiga, ¶0069, “The machine learning model may be trained using a training set of historical slice usage data labeled by a domain expert with manually selected network slices for particular combinations of application requirements, locations, and KPI values. In some implementations”.)
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjiga et al. (US 2024/0205810) in view of Young et al. (US 2021/0258221)
As to claim 10:
Majjiga discloses all limitations of claim 9, however regarding: namely the default set of slices includes at least a first slice of a first slice type corresponding to a first slice type blueprint, a second slice of a second slice type corresponding to a second slice type blueprint, a third slice of a third slice type corresponding to a third slice type blueprint and a fourth slice of a fourth slice type corresponding to a fourth slice type blueprint. Majjiga in ¶0069 discloses at least a first type of slice corresponding to machine type blueprint, namely for UAV with slices specifically designed for its application purposes. Majjiga, however are silent on other categories of slices/blue print as claimed.
The examiner asserts categorization of network slices into a specific number of categories are merely matter of preferences/design choice rather than an inventive element.
Indeed, Young in a related field of designing and allocating slice infrastructure discloses in at least ¶0008, 0045, 0055, 0047 different categories of slices to be allocated for different service/device types, for example UE’s internet browsing slice (i.e. smart phone), gaming network slice, vehicle (V2X) machine, streaming lice, each are configured based on different sets/distinct goal/requirement/resource block (i.e. blue prints) for respective customer experience, for example “For example, the video streaming network slice may provide UWB video streaming service using the UWB infrastructure and the eMBB infrastructure”, “V2X infotainment services using the eMBB infrastructure”, “ the AR/VR network slice may provide U-LLC, U-HV, AR/VR functions using the UWB infrastructure and the U-LLC and U-HV infrastructure”, “Each network slice may be configured to give a distinct customer experience (e.g., ultra-reliable (UR) services, ultra-high bandwidth (UHB), extremely low-latency, ultra-reliable low-latency communication (URLLC), and/or the like)”.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Majjiga to further diversify slice categories in manner disclosed by Young. Young in at least ¶0007, 0008 expresses the demands for diversifying slices to meet different multiple services application for conservation/optimization of resource and QoE purposes. Thus slice prediction based on service types help improve efficiency/performance (¶0011 of Young).
Claim(s) 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjiga et al. (US 2024/0205810) in view of Young et al. (US 2021/0258221) in view of Kwok et al. (US 2024/0406069).
As to claim 11:
Majjiga in view of Young discloses all limitations of claim 10,
Regarding:
operating an artificial intelligence engine to use the slice and slice parameter prediction model to automatically predict slices and slice parameters which are required for a future period of time includes: predicting fewer slices being required in the future period of time than are included in said default set of slices, said fewer slices including a first slice of the first slice type, a second slice of the second slice type, and a third slice of the third slice type but not a slice of the fourth type slice.
While the limitations are not recited in Majjiga/Young word for word, however the limitation(s) above are directed to extremely specific situational circumstance i.e. when the future usage happens to be less demanding (fewer slices) and dominated by a specific sub-sets of service types. Kwok in a related field of slice prediction adaptation discloses such a system (See a Kwok, ¶0018, 0022, the prediction system to predict which particular application types to be likely used at the future time period, and understand the requirements of each application at a granular level (real time, non-real, live, …categories, etc.). ¶0024-0025 generate or create new slice types best suited when one or more particular service types are likely to be use over other application/services).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Majjiga/Young to accomplish such a situational output detailed in claim 11 in view of Kwok. Recall that Majjiga discloses ¶0079, generating different network slices for use by client devices 110 at each predicted type slots . ¶0045, 0093-0097, generating the network slices which includes a plurality of S-NSSAI, each associated with a future time/location/application. The limitations of claim 11 are driven by the environment and demands, rather than being an inventive step. Any adaptive slice prediction system can meet this limitation because their outputs are shaped by the future need rather being an inventive feature. This adaptive slice allocation allow for optimization of performance (¶0022 of Kwok).
As to claim 12:
Majjiga in view of Young and Kwok discloses all limitations of claim 11, wherein automatically generating the network slices predicted to be required for the future period of time includes allocating at least some resources previously allocated to the fourth slice corresponding to the fourth slice type to one or more of the first, second and third slices of the first set of slices.
Examiner’s note: this limitation is merely directed to a situational environment of demand, wherein the situation shapes the outputs, rather than the system itself performs an active role/step.
(Young, ¶0043, different types of slices might share one or more building blocks, ¶0048, different service types can utilize blocks of other slice types (i.e. services ½ might use video streaming slice as needed), ¶0018, 0022, 0042, depending on situation, the system might be allocating more/less of blocks for a service to be used based on updated selection of existing blocks)
As to claim 13:
Majjiga in view of Young and Kwok discloses all limitations of claim 12, wherein the default set of slices includes a first slice which is a live stream slice, a second slice which is a gaming slice, a third slice which is a smart phones slice and a fourth slice which is a machines slice; and wherein the first set of slices generated for said future period of time includes a live stream slice, a gaming slice and a smart phones slice, some network resources allocated to the machines slice of the default set of slices being reallocated to one or more of the slices in the first set of slices as part of automatically generating said first set of slices.
(Young discloses in at least ¶0008, 0045, 0055, 0047 different categories of slices to be allocated for different service/device types, for example UE’s internet browsing slice (i.e. smart phone), gaming network slice, vehicle (V2X) machine, streaming lice, each are configured based on different sets/distinct goal/requirement/resource block (i.e. blue prints) for respective customer experience, for example “For example, the video streaming network slice may provide UWB video streaming service using the UWB infrastructure and the eMBB infrastructure”, “V2X infotainment services using the eMBB infrastructure”, “ the AR/VR network slice may provide U-LLC, U-HV, AR/VR functions using the UWB infrastructure and the U-LLC and U-HV infrastructure”, “Each network slice may be configured to give a distinct customer experience (e.g., ultra-reliable (UR) services, ultra-high bandwidth (UHB), extremely low-latency, ultra-reliable low-latency communication (URLLC), and/or the like)”)
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjiga et al. (US 2024/0205810) in view of Young et al. (US 2021/0258221) in view of Kwok et al. (US 2024/0406069) and in further view of Pannem (US 2016/0162314)
As to claim 14:
Majjiga in view of Young and Kwok discloses all limitations of claim 13, wherein said some network resources reallocated to one or more slices includes a number of processors, an amount of bandwidth, and an amount of storage. (Young, ¶0010, resources include: processing resources, memory resources, communication resources. Kwok, ¶0023, network slices includes bandwidth to meet bandwidth requirement.)
While Young does not explicitly disclose the disclosed ‘processing resource’ in terms of number of processor.
Pannem, in a related field of resource allocation in computing environment, discloses in at least ¶0021 that processing resources can be a number of a processor.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that Young’s computing resources can be a number processors per Pannem’s disclosure. In the context of multi-core/multi-processors in Majjiga (¶0025, “110 may include processors”, it’s natural that processing resources to be allocated can include a number of the plurality of processors available, allowing for parallel processing for improving efficiency.
Claim(s) 21-23, 25, 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjiga et al. (US 2024/0205810) in view of Kwok et al. (US 2024/0406069)
As to claim 21:
Majjiga discloses all limitations of claim 1, further comprising:
prior to said first period of time collecting a first set of communications network information including information on UE use of existing network slices; (¶0080, 0084, collect historical slice usage of network over time and store them in database)”
Except Majjiga does not explicitly disclose:
operating the AI engine to perform a clustering operation on the slice utilization information to produce clustered information; and generating, based on the clustered information, a default set of network slice blueprints.
In a related field of endeavor, Knok discloses a AI-driven slice generator system, wherein a machine learning model is used to facilitate categorization of network slice information (i.e. clustering), based on which to enable the network slice generator to generate an original set of network slices (i.e. blueprint) best suitable for use by a UE (See at least 0031, 0024-0025, and 0020) which can be modified later dynamically.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Majjiga to include a AI-driven slice generator system, wherein a machine learning model is used to facilitate categorization of network slice information (i.e. clustering), based on which to enable the network slice generator to generate a first set of network slices. This implementation allows for best choices of network slices and optimizes performance (Kwok, ¶0025).
As to claim 22:
Majjiga in view of Kwok discloses all limitations of claim 21, wherein the default set of network slice blueprints includes slice blue prints corresponding to different clusters of information included in said clustered information. (Kwok, ¶0022, “ the network slice generator 112 may classify an application based on a set of criteria including but not limited to: latency requirements (e.g., a subcategory of “real-time” “non-real-time”, “live”, etc. may be used), security requirements (e.g., a finance application may require different security than a social media application), etc., By defining additional sub-categories, the network slice generator 112 can generate network slices that meet the requirements of different application”)
As to claim 23:
Majjiga in view of Kwok discloses all limitations of claim 21, wherein the default set of network slice blueprints includes multiple default slice blueprints, different default slices blueprints in said multiple default slice blueprints corresponding to different types of slices. (Kwok, ¶024, 0025, a plurality of first network slices generated, including a plurality of types, each of which corresponding to respective applications from a list of application)
As to claim 25:
Majjiga in view of Kwok discloses all limitations of claim 22, further comprising: generating default slices from the default slice blueprints; using the default slice blueprints during said first period of time to provide services to UEs. (See Kwok, at least 0031, 0020-0025, generate slices and supply for the UE for usage)
As to claim 26:
Majjiga in view of Kwok discloses all limitations of claim 25, further comprising: modifying default slice blueprints based on the predicted slices and slice parameters corresponding to the future period of time to generated updated slice blueprints; and wherein the slice blueprints used in automatically generating the network slices predicted to be required for the future period of time are the updated slice blueprints. (See ¶0024, 0028, and 0031 of Kwok, network slices can be updated/modified in future depending on new conditions and new changes of application requirement, usage etc)
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majjiga et al. (US 2024/0205810) in view of Kwok et al. (US 2024/0406069) and in further view of Chong (WO 2020/143384).
As to claim 24:
Majjiga in view of Kwok discloses all limitations of claim 23, however is/are silent on said default slice blue prints include an initial allocation of resources which is uniform with network resources being allocated evenly by default slice blueprints corresponding to different types of slices.
Chong in a related field of endeavor discloses a system/method for allocating resources for network slices, wherein in at least page 66, first paragraph, wherein in case the network has not yet obtain new network information, it might initially (i.e. default setting) allocate resources for network slices evenly and leaves possibility for modification later on as new information being gathered.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Majjiga/Kwok might initially allocate resources evenly. This implementation constitute a good first reference point ensuring fairness while allowing flexible modification in view of future/predicted demands and/or usage change.
Allowable Subject Matters
Claim 27 is/are 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.
The references of Majjiga’s combination discloses all limitation discussed in the section above, they however does not disclose: wherein said default set of network slice blueprints includes a first slice blueprint, the first slice blueprint including a first service type indicator, multiple network function type indicators including a user plane function (UPF) function type indicator and a session management function (SMF) type indicator along with information indicating resources required for a network slice implemented in accordance with the first slice blueprint, information indicating resources required for the network slice implemented in accordance with the first slice blueprint indicating i) a first bandwidth, ii) a first number of compute resources and iii) a first amount of storage for the network slice implemented in accordance with the first slice blueprint.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0403568 - Example apparatus are to implement an actor-critic neural network to predict a quality of service metric for a network slice based on a long short-term memory representative of one or more prior slicing decisions, compare the quality of service metric with a target service level specification, and update the long short-term memory based on the comparison.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUAN M HUA whose telephone number is (571)270-7232. The examiner can normally be reached 10:30-6:30.
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, Anthony Addy can be reached at 571-272-7795. 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.
/QUAN M HUA/Primary Examiner, Art Unit 2645
Prosecution Insights