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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/23/2026 has been entered.
Claims 9 and 19 have been cancelled. Claims 21-23 are newly added. Claims 1-7, 10-17, and 20-23 remain pending.
Response to Arguments
Applicant’s amendments and remarks filed with an RCE have been fully considered.
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7, 10-17, and 20-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Veggalam et al. (US Patent Application Publication 2020/0344641; hereinafter Veggalam) in view of Svennebring et al. (USPN 11,159,408; hereinafter Svennebring).
Regarding claim 1 Veggalam discloses a method of resource allocation within a network, the method comprising:
receiving first identification data with respect to one or more user devices (paragraphs 0023, 0035, 0059; storing data for connected UEs);
receiving second identification data with respect to one or more cell sites (paragraphs 0035, 0069, 0074; cell parameters, cell neighbor list); and
generating a probability of network traffic with respect to each of the one or more cell sites based on the received first and second identification data (paragraphs 0072-0076; wherein a prediction is generated with respect to the cells based on the received data and monitored data).
Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: wherein the second identification data indicates one or more cell sites to which the one or more user devices connect while travelling along a network traffic path (col. 18, lines 28-34, 44-53; col. 57, lines 6-67; col. 58, lines 1-30; col. 59, line 42 – col. 60, line 20; cell IDs are used by the model to identify a travel path (current or historical) for UEs in the network); and grouping the second identification data based on the first identification data and the second identification data, wherein the grouping comprises identifying identification data of the one or more cell sites to which the one or more user devices has connected while traveling along the network traffic path, and creating a group of the identified identification data based on the identifying (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cells are grouped as part of a set/subset, and the cell set/subsets are used to identify a current or potential travelling path of a UE); generating a probability of network traffic based on the grouping (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cell sets/subsets are used on a predictive model to identify a current or future potential path for a UE); and allocating one or more network resources to the one or more cell sites based on the generated probability of network traffic (col. 17, lines 40-49; col. 30, line 64 – col. 31, line 11; the predictive model is used for resource allocation along distinct routes/paths as needed). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 2 Veggalam discloses the method of claim 1, further comprising: grouping the first and second identification data with respect to at least one of one or more times of day, time period and time range (paragraphs 0035, 0045, 0074; different time periods like week/month/days, among others).
Regarding claim 3 Veggalam discloses the method of claim 1, wherein the step of generating a probability of network traffic with respect to each of the one or more cell sites is further based on a defined time of day, time period, or time range (paragraphs 0035, 0045, 0074; different time periods like week/month/days, among others).
Regarding claim 4 Veggalam discloses the method of claim 1, wherein the step of generating a probability of network traffic with respect to each of the one or more cell sites is further based on neural network (NN) embeddings of a NN embedding model (paragraphs 0036-0037, 0044-0045, 0070, 0072, 0075; machine learning mechanisms). Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses wherein the NN embedding model is configured to: dynamically re-train the NN embedding model to learn one or more alternative paths of the one or more user devices (as seen in col. 14, lines 15-39; col. 15, lines 5-15, the described invention uses training data to train/retrain the machine learning model; col. 54, lines 50-65, col. 56, lines 3-10, col. 57, lines 55-67; the data is continually received and used to train and refine the model); and predict updated probabilities with respect to network traffic and cell site utilization based on the retraining (col. 54, lines 50-65, col. 56, lines 3-10, col. 57, lines 55-67; the data is continually received and used to train and refine the model). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 5 Veggalam discloses the method of claim 4. Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: vectorizing the one or more cell site IDs associated with the one or more cell sites (col. 55, line 46 – col. 56, line 30; utilizing the UEs movement path and the cell IDs in the path are used as a movement vector); inputting the vectorized one or more cell site IDs to the NN embedding model (col. 56, lines 5-59; col. 57, line 6-60; travel/path data is continuously sent to the model); and generating the probability of the network traffic based on the input (col. 56, lines 5-59; col. 57, line 6-60; the data is used to train and refine the model prediction). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 6 Veggalam discloses the method of claim 4, further comprising: generating a cluster for the numeric representation of each of the one or more cell sites (paragraphs 0014, 0036, 0071; clustering models).
Regarding claim 7 Veggalam discloses the method of claim 1. Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: determining a travel path for the one or more user devices based on the generated probability of network traffic with respect to each of the one or more cell sites (figs. 9-10, col. 57, lines 5-60, col. 59, lines 42-63; cell transition and potential travel path(s) are determined for the UEs in the network). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 10 Veggalam discloses the method of claim 1, wherein the step of allocating network resources to each of the one or more cell sites further comprising managing operational times of one or more servers in communication with the one or more cell sites (paragraphs 0041, 0073; automatic adjustments of parameters).
Regarding claim 11 Veggalam discloses an apparatus for resource allocation within a network, comprising: a memory storage storing computer-executable instructions (memory 560); and a processor communicatively coupled to the memory storage (processor 502), wherein the processor is configured to execute the computer-executable instructions and cause the apparatus to:
receive first identification data with respect to one or more user devices (paragraphs 0023, 0035, 0059; storing data for connected UEs);
receive second identification data with respect to one or more cell sites (paragraphs 0035, 0069, 0074; cell parameters, cell neighbor list); and
generate a probability of network traffic with respect to each of the one or more cell sites based on the received first and second identification data (paragraphs 0072-0076; wherein a prediction is generated with respect to the cells based on the received data and monitored data).
Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: wherein the second identification data indicates one or more cell sites to which the one or more user devices connect while travelling along a network traffic path (col. 18, lines 28-34, 44-53; col. 57, lines 6-67; col. 58, lines 1-30; col. 59, line 42 – col. 60, line 20; cell IDs are used by the model to identify a travel path (current or historical) for UEs in the network); and group the second identification data based on the first identification data and the second identification data, wherein the grouping comprises identifying identification data of the one or more cell sites to which the one or more user devices has connected while traveling along the network traffic path, and creating a group of the identified identification data based on the identifying (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cells are grouped as part of a set/subset, and the cell set/subsets are used to identify a current or potential travelling path of a UE); generate a probability of network traffic based on the grouping (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cell sets/subsets are used on a predictive model to identify a current or future potential path for a UE); and allocate one or more network resources to the one or more cell sites based on the generated probability of network traffic (col. 17, lines 40-49; col. 30, line 64 – col. 31, line 11; the predictive model is used for resource allocation along distinct routes/paths as needed). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 12 Veggalam discloses the apparatus of claim 11, wherein the computer-executable instructions, when executed by the processor, further cause the apparatus to: group the first and second identification data with respect to one or more times of day, time period, or time range (paragraphs 0035, 0045, 0074; different time periods like week/month/days, among others).
Regarding claim 13 Veggalam discloses the apparatus of claim 11, wherein the step of generating a probability of network traffic with respect to each of the one or more cell sites is further based on a defined time of day, time period, or time range (paragraphs 0035, 0045, 0074; different time periods like week/month/days, among others).
Regarding claim 14 Veggalam discloses the apparatus of claim 11, wherein the step of generating a probability of network traffic with respect to each of the one or more cell sites is further based on neural network (NN) embeddings of a NN embedding model (paragraphs 0036-0037, 0044-0045, 0070, 0072, 0075; machine learning mechanisms). Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses wherein the NN embedding model is configured to: dynamically re-train the NN embedding model to learn one or more alternative paths of the one or more user devices (as seen in col. 14, lines 15-39; col. 15, lines 5-15, the described invention uses training data to train/retrain the machine learning model; col. 54, lines 50-65, col. 56, lines 3-10, col. 57, lines 55-67; the data is continually received and used to train and refine the model); and predict updated probabilities with respect to network traffic and cell site utilization based on the retraining (col. 54, lines 50-65, col. 56, lines 3-10, col. 57, lines 55-67; the data is continually received and used to train and refine the model). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 15 Veggalam discloses the apparatus of claim 14. Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: vectorizing the one or more cell site IDs associated with the one or more cell sites (col. 55, line 46 – col. 56, line 30; utilizing the UEs movement path and the cell IDs in the path are used as a movement vector); inputting the vectorized one or more cell site IDs to the NN embedding model (col. 56, lines 5-59; col. 57, line 6-60; travel/path data is continuously sent to the model); and generating the probability of the network traffic based on the input (col. 56, lines 5-59; col. 57, line 6-60; the data is used to train and refine the model prediction). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 16 Veggalam discloses the apparatus of claim 14, wherein the computer-executable instructions, when executed by the processor, further cause the apparatus to: generate a cluster for the numeric representation of each of the one or more cell sites (paragraphs 0014, 0036, 0071; clustering models).
Regarding claim 17 Veggalam discloses the apparatus of claim 11. Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses wherein the computer-executable instructions, when executed by the processor, further cause the apparatus to: determine a travel path for the one or more user devices based on the generated probability of network traffic with respect to each of the one or more cell sites (figs. 9-10, col. 57, lines 5-60, col. 59, lines 42-63; cell transition and potential travel path(s) are determined for the UEs in the network). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 20 Veggalam discloses a non-transitory computer-readable medium comprising computer-executable instructions for optimizing energy using traffic patterns within a network by an apparatus (col. 40, lines 20-50), wherein the computer-executable instructions, when executed by at least one processor of the apparatus, cause the apparatus to:
receive first identification data with respect to one or more user devices (paragraphs 0023, 0035, 0059; storing data for connected UEs);
receive second identification data with respect to one or more cell sites (paragraphs 0035, 0069, 0074; cell parameters, cell neighbor list); and
generate a probability of network traffic with respect to each of the one or more cell sites based on the received first and second identification data (paragraphs 0072-0076; wherein a prediction is generated with respect to the cells based on the received data and monitored data).
Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: wherein the second identification data indicates one or more cell sites to which the one or more user devices connect while travelling along a network traffic path (col. 18, lines 28-34, 44-53; col. 57, lines 6-67; col. 58, lines 1-30; col. 59, line 42 – col. 60, line 20; cell IDs are used by the model to identify a travel path (current or historical) for UEs in the network); and group the second identification data based on the first identification data and the second identification data, wherein the grouping comprises identifying identification data of the one or more cell sites to which the one or more user devices has connected while traveling along the network traffic path, and creating a group of the identified identification data based on the identifying (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cells are grouped as part of a set/subset, and the cell set/subsets are used to identify a current or potential travelling path of a UE); generate a probability of network traffic based on the grouping (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cell sets/subsets are used on a predictive model to identify a current or future potential path for a UE); and allocate one or more network resources to the one or more cell sites based on the generated probability of network traffic (col. 17, lines 40-49; col. 30, line 64 – col. 31, line 11; the predictive model is used for resource allocation along distinct routes/paths as needed). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 21 Veggalam discloses the method of claim 1, wherein the first identification data comprises data relating user device identification (paragraphs 0023, 0035, 0059; storing data for connected UEs). Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses and the second identification data comprises at least one of cell site identification and a time of day the specific user device connected to a particular cell site (col. 50, lines 42-60, col. 53, lines 31-44, col. 54, lines 5-20, col. 60, lines 40-55; cell data comprises cell id, and time of day of UE camping or moving through the cell). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Regarding claim 22 Veggalam discloses the method of claim 1, wherein the one or more cell sites comprises primary cell sites and corresponding neighboring cell sites (paragraphs 0013, 0035, 0041; SON network cell list comprising a cell and neighboring cells).
Regarding claim 23 Veggalam discloses the method of claim 1. Veggalam fails to explicitly disclose but Svennebring, in the same field of endeavor related to network management, discloses further comprising: generating a grouping of one or more cell sites within a travel path of the one or more user devices based on the generated probability of network traffic with respect to each of the one or more cell sites (col. 57, lines 6-67; col. 59, line 42 – col. 60, line 20; cells are grouped as part of a set/subset, and the cell set/subsets are used to identify a current or potential travelling path of a UE). Therefore, 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 teachings of Veggalam with the teachings of Svennebring, in order to improve network performance (Svennebring: col. 2, lines 37-65).
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication 2023/0245336 to Fonseca et al. – that discloses an environment may be discretized into a plurality of cells in which cell data represented in a data structure associated with the one or more cells may include vector data (e.g., a set of values) representing a distance and a direction from a reference point to a nearest object. While a center point of a cell is used as an example herein, a reference point may be any other point in two-dimensional space (or otherwise in higher dimensional representations), including a corner point of a cell or any other point. Vector data may take the form of a distance value indicating the distance from the a reference point to a nearest edge of a nearest object (e.g., a surface of the nearest object) in the environment and a direction value indicating the direction from the reference point to the nearest edge of the nearest object in the environment. Alternatively, vector data may take the form of coordinates of the endpoints of the nearest edge of the nearest object in the environment, which may be used to calculate a vector representing a distance and a direction from a reference point to the nearest edge of the nearest object in the environment.
US Patent Application Publication 2021/0303916 to Delp et al. – that discloses improving clustering of points within a point cloud. In one embodiment, a method includes grouping the points into cells of a grid. The grid divides an observed region of a surrounding environment associated with the point cloud into the cells. The method includes computing feature vectors for the cells that use cell features to characterize the points in the cells and relationships between the cells. The method includes analyzing the feature vectors according to a clustering model to identify clusters for the cells. The clustering model evaluates the cells to identify which of the cells belong to common entities.
US Patent Application Publication 2018/0191634 to Karthikeyan et al. – which discloses managing QoS models for traffic flows in a network are described. In particular, the method includes storing the QoS models and analysing at least one of the QoS models to determine whether the QoS model satisfies a suitability test. One or more QoS models are then selected based on the analysis and these selected models are offered to a client for use with a traffic flow in the network. In one embodiment, the method may be implemented based on a query from a client requesting reservation of network resources for a traffic flow. The analysis may be based on a characteristic of the or each QoS model, of the client, of a query received from the client requesting reservation of network resources for the traffic flow or of the network or an operator of the network.
Conclusion
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/Aixa Guadalupe-Cruz/
Examiner
Art Unit 2466
/FARUK HAMZA/Supervisory Patent Examiner, Art Unit 2466