DETAILED ACTION
1. Claims 1-20 have been examined and are pending.
Response to Amendment
2. In response to the amendments received in the Office on 4/22/2026, the Office acknowledges the current status of the claims: claims 1, 3, 7, 8, 14, and 15 have been amended, an no new matter appears to be included.
Response to Arguments
3. Applicant’s arguments with respect to claims 1, 8, and 15 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. The below rejection considers portions of United States Patent Application Publication 2023/0113822 A1 to Li et al. (hereinafter “Li2”) in view of the additional amendments. For example, Li2 discloses identifying a first set of daily busy hours for each of the sectors in response to the sectors having a highest sector volume of data downloaded in an hour for each of the days (Li: [0005] – “Particular embodiments described here relate to a method of determining root causes of low quality of experience (QoE) of a communication network based on a number of QoE metrics (e.g., download speed, download speed of busy hours, latency) and root-cause metrics (e.g., signal strength, congestion indicator, number of samples). The system may firstly collect application usage data in a number of areas (e.g., cells, tiles, regions) over a duration of N days (e.g., 7 days, 28 days). Then, the system may preprocess the collected data for filtering and cleaning and aggregate the collected data into data points per hour per individual day or per hour all N days.” See also [0041], [0045], [0087-0089] – the system facilitates collection of network metrics corresponding to congestion (high volume of users) per an hour or hours over the course of the day for a cell or region (sector).); identifying a second set of daily busy hours for each of the cells in response to the cells having a highest cell volume of data downloaded in an hour for each of the days (Li: [0005] – “Particular embodiments described here relate to a method of determining root causes of low quality of experience (QoE) of a communication network based on a number of QoE metrics (e.g., download speed, download speed of busy hours, latency) and root-cause metrics (e.g., signal strength, congestion indicator, number of samples). The system may firstly collect application usage data in a number of areas (e.g., cells, tiles, regions) over a duration of N days (e.g., 7 days, 28 days). Then, the system may preprocess the collected data for filtering and cleaning and aggregate the collected data into data points per hour per individual day or per hour all N days.” See also [0041], [0045], [0087-0089] – the system facilitates collection of network metrics corresponding to congestion (high volume of users) per an hour or hours over the course of the day for a cell or region (sector).); and applying a percentile method to remove outliers from the first set of daily busy hours and the second set of daily busy hours (Li: [0051-0052] – corresponds to determining minimum, maximum, and/or outlier values for ascertaining a predicted percentage value for one or more metrics to determine an out of capacity at an access point. See also [0039], [0045], [0098].).
Examiner respectfully maintains the rejections in view Li2.
Claim Rejections - 35 USC § 103
4. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
5. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication 2021/0250245 A1 to Li et al. (hereinafter “Li”), in view of United States Patent Application Publication 2023/0113822 A1 to Li et al. (hereinafter “Li2”), and further in view of United States Patent Application Publication 2024/0259868 A1 to Agrawal et al. (“Agrawal”).
Regarding Claim 1, Li discloses a process for detecting capacity breaches in an area of interest (AOI) of access network (Li: [0003], [0015] – corresponds to predicting/forecasting an out of capacity time for one or more access points in a cellular communication network.), the process comprising:
collecting performance data from a cell site of the RAN in the AOI, the cell site comprising sectors and the sectors comprising cells (Li: [0016] – “Particular embodiments of the system may use the data (e.g., application names, application types, time duration, quality of experience, network speed, latency, network coverage, network traffic volume, number of samples, etc.) collected at the application level to generate models for identifying the cells that have network capacity issues or are predicted to have network capacity issue in a future time. The system may collect data samples associated with a cell of interest and aggregate the collected data samples into a series of data points.” See also [0064] – “In particular embodiments, the system may identify one or more areas of interest covering one or more access points for predicting the amount of time until these access points are out of capacity. As an example, the system may identify an area covering one or more cells with network congestion as a geographic area of interest.” Li suggests the inclusion of sectors, as well as cells, when they are required for network enhancement.);
determining busy-hour indicators for the sectors and the cells based on the performance data from the cell site over a sampling window comprising days (Li: [0003]- “Particular embodiments described here relate to a method of predicting the amount of time until an access point of a communication network is out of capacity based on network performance data collected at the application level. The system may first collect network performance data at the application level over a period of time (e.g., W weeks) and aggregate the collected data samples into a series of data points. Each data point may be aggregated based on data samples of the same hour of one week (e.g., each data point having a network traffic value of one hour aggregated over one week).” See [0021], [0032-0033].), by:
applying a percentile method to remove outliers from…the data (Li: [0051-0055] – corresponds to determining minimum, maximum, and/or outlier values for ascertaining a predicted percentage value for one or more metrics to determine an out of capacity at an access point. See also [0036].);
forecasting a number of future subscribers using subscriber growth model in the AOI for a forecast period (Li: [0016-0017], [0018] – via social network systems and application use data from a plurality of users (number of samples that includes numerous metrics corresponding to user), the system is operable to predict a time when the area of interest will reach a capacity threshold. These metrics are utilized to draft one or more models for identifying cells of interest that have or will network capacity.);
extrapolating the busy-hour indicators based on the number of subscribers to generate forecast indicators for the forecast period (Li: [0022] – an extraction module in the system is utilized to extract one or more metrics, including time of day to the number of samples, in order to predict when the access point will reach capacity (a number of subscribers/users).);
applying a gain function to the forecast indicators to generate revised forecast indicators (Li: [0056-0057], [0071-0075] – interpreted to correspond to applying a prediction gain for optimization of the network, thus allowing for a dynamic (revised) number of performance metrics. “In particular embodiments, the system may generate network optimization plans for long-term optimization (e.g., cell densification, upgrading network to 4G/5G, adding fibers, strengthening fiber backhaul) or short-term optimization planning (e.g., tune tower antenna angles, balancing demands and capacity) based on the predicted network enhancement gain. In particular embodiments, the optimization recommendation may be generated using a machine-learning (ML) model which is trained based on historical data.”); and
detecting a capacity breach at a cell or a sector of the cell site in the AOI in response to an indicator from the revised forecast indicators and corresponding to the cell or the sector exceeding a capacity threshold (Li: [0048-0049] – “The system may incrementally update the aggregated data points based on the sliding time window and the calculated trend function periodically (e.g., daily, weekly, monthly). The incrementally updated data points and trends may provide more accurate prediction for future time by factoring in the recently collected and aggregated data points.” See also [0056-0059], [0071-0075] – the sliding scale and aggregated number of samples collected (updated) over a course of a time period thus revises the time when the access point (of a cell/sector) will reach a maximum capacity.).
Although Li discloses hourly collection of samples, Li does not expressly disclose identifying a first set of daily busy hours for each of the sectors in response to the sectors having a highest sector volume of data downloaded in an hour for each of the days and identifying a second set of daily busy hours for each of the cells in response to the cells having a highest cell volume of data downloaded in an hour for each of the days.
However, these features cannot be considered new or novel in the presence of Li2. Li2 is similarly concerned with determining network congestion in a communications network (Li2: [0001]). Li2 discloses identifying a first set of daily busy hours for each of the sectors in response to the sectors having a highest sector volume of data downloaded in an hour for each of the days (Li2: [0005] – “Particular embodiments described here relate to a method of determining root causes of low quality of experience (QoE) of a communication network based on a number of QoE metrics (e.g., download speed, download speed of busy hours, latency) and root-cause metrics (e.g., signal strength, congestion indicator, number of samples). The system may firstly collect application usage data in a number of areas (e.g., cells, tiles, regions) over a duration of N days (e.g., 7 days, 28 days). Then, the system may preprocess the collected data for filtering and cleaning and aggregate the collected data into data points per hour per individual day or per hour all N days.” See also [0041], [0045], [0087-0089] – the system facilitates collection of network metrics corresponding to congestion (high volume of users) per an hour or hours over the course of the day for a cell or region (sector).);
identifying a second set of daily busy hours for each of the cells in response to the cells having a highest cell volume of data downloaded in an hour for each of the days (Li: [0005] – “Particular embodiments described here relate to a method of determining root causes of low quality of experience (QoE) of a communication network based on a number of QoE metrics (e.g., download speed, download speed of busy hours, latency) and root-cause metrics (e.g., signal strength, congestion indicator, number of samples). The system may firstly collect application usage data in a number of areas (e.g., cells, tiles, regions) over a duration of N days (e.g., 7 days, 28 days). Then, the system may preprocess the collected data for filtering and cleaning and aggregate the collected data into data points per hour per individual day or per hour all N days.” See also [0041], [0045], [0087-0089] – the system facilitates collection of network metrics corresponding to congestion (high volume of users) per an hour or hours over the course of the day for a cell or region (sector).). Li2 further discloses applying a percentile method to remove outliers from the first set of daily busy hours and the second set of the daily busy hours (Li2: [0036], [0052).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the communication network of Li in view of the communication network and forecasting model of Li2 to determine busy hours of sectors and cells for the reasons of detecting real-time network congestion issues to facilitate network optimization (Li2: [0019-0020]).
Although Li discloses determining a forecasted capacity breaches in a cellular communication network comprising at least mobile network operators (MNO; see [0016], [0024], which suggests wireless communications), Li does not expressly disclose forecasting a network capacity in an open radio access network (O-RAN).
However, this feature cannot be considered new or novel in the presence of Agrawal. Agrawal discloses art of similar endeavor, particular to forecasting capacity breaches in a network (Agrawal: [0001]). Agrawal discloses applying a forecasting model that predicts a network out-of-capacity of open radio access network (O-RAN), among other types of wireless networks (Agrawal: Figure 1 with [0028-0032], [0035-0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the communication network of Li in view of the communication network and forecasting model of Agrawal to forecast network capacity of an open radio access network for the reasons of reducing coverage issues between user and base station (Agrawal: [0002]).
Regarding Claim 2, the combination of Li, Li2 and Agrawal discloses the process of claim 1, Li further discloses [further comprising] recommending a capacity expansion in response to detecting the capacity breach (Li: [0003] – “The system may generate prioritized recommendations for MNOs (mobile network operators) to optimize access points of the communication network based on the respective predicted time amounts for these access points to become out of capacity.” [0017] – “For example, the system may provide network optimization recommendations on whether to optimize the network in particular cells, which aspects to optimize for (e.g., network upgrading, network expansion, adding more cells, cell densification), where and when to implement the optimization (e.g., where and when to add cells for cell densification), etc.”).
Regarding Claim 3, the combination of Li, Li2, and Agrawal discloses the process of claim 1, wherein Li2 further discloses forecasting the number of subscribers in the AOI for the forecast period further comprises comparing a starting subscriber number at a beginning of the forecast period to a predicted subscriber number at an end of the forecast period (Li2: [0030] – corresponds to determining the number of devices connected over a sampling period. See also [0078], [0084] disclosing a predicted amount of network congestion (number of users).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the communication network of Li in view of the communication network and forecasting model of Li2 to determine busy hours of sectors and cells for the reasons of detecting real-time network congestion issues to facilitate network optimization (Li2: [0019-0020]).
Regarding Claim 4, the combination of Li, Li2, and Agrawal discloses the process of claim 1, wherein Agrawal further discloses rendering the cell site of the capacity breach on a map of the AOI (Agrawal: [0102] – corresponds to displaying not only elements of forecast capacity breaches, but also actions to take per the forecast model. See also Figure 3 with [0041-0042]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the communication network of Li in view of the communication network and forecasting model of Agrawal to forecast network capacity of an open radio access network for the reasons of reducing coverage issues between user and base station (Agrawal: [0002]).
Regarding Claim 5, the combination of Li, Li2, and Agrawal discloses the process of claim 4, wherein Agrawal further discloses the map of the AOI comprises cell sites located in the AOI (Agrawal: Figure 3 – area of interest corresponds to one or more cells and neighboring cells.), the cell sites located in the AOI including visual indicators of on-air sectors (Agrawal: Figure 3 with [0041-0042] – newly on air sites), sector breaks (Agrawal: Figure 3 with [0041-0042] – distance between cells), and planned sites (Agrawal: Figure 3 with [0041-0042] – planned sites).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the communication network of Li in view of the communication network and forecasting model of Agrawal to forecast network capacity of an open radio access network for the reasons of reducing coverage issues between user and base station (Agrawal: [0002]).
Regarding Claim 6, the combination of Li, Li2, and Agrawal discloses the process of claim 1, wherein Li further discloses applying the gain function to the forecast indicators simulates efficiencies gained by features of the RAN (Li: [0056-0057] – corresponds to applying a predicted gain to determine a level of efficiency (reduced load/congestion) when an access point is upgraded from the congested state.).
Regarding Claim 7, the combination of Li, Li2, and Agrawal discloses the process of claim 1, wherein Li further suggests determining the busy-hour indicators further comprises:
identifying busiest hours from the first set of daily busy hours for the sector of the cell site…during a sampling period (Li: [0076-0079] – a one hour per week of a plurality of network samples is collected over a period of time to determine if a threshold (capacity) is met. See also [0041], [0045], [0087-0089] – the system facilitates collection of network metrics corresponding to congestion (high volume of users) per an hour or hours over the course of the day for a cell or region (sector).); and
averaging performance data for the sector during the busiest hours for the sector…to generate a busy-hour indicator for the sector (Li: [0076-0079] – an average is calculated of the number of values collected of the amount of time that meets the network-capacity metric.).
Although Li does not explicitly detail a calculation of a busiest hour during a three day period, Li does disclose that any number of days may be used for calculating a threshold hour of network capacity (Li: [0021] – “Then, the collected data may be preprocessed and aggregated by the data aggregation module 204 into a series of data points (e.g., aggregated per hour per week or per hour per any number of days). The aggregated data may be stored in the database 206 and fed to the network out of capacity prediction module 210. In particular embodiments, the data may be aggregated at cell level for a cellular communication network. The aggregated data may be associated with corresponding cells as identified by the cell identifiers. The system may the data associated with a cell to predict when that cell will be out of capacity. For example, the network out of capacity prediction module 210 may access data associated with the cells of a communication network from the database 206 and predict the time when these cells will be out of capacity.” See also [0030-0032].). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the communication network of Li to include any measurement over any period of time as a matter of design choice. The busiest hour, busiest day, or busiest week (detailed by Li as an increase in one or more measured metrics over a determined amount of time) may be determined by the forecast model of Li simply by means of computer programming, such substitution has been judicially determined to have been obvious where the claimed differences involved to the substitution of interchangeable or replaceable equivalents (over one day, over three days, or over a week) and the reason for the selection of one equivalent for another was not to solve an existent problem. In re Ruff, 118, USPQ, 343 (CCPA 1958). This supporting is based on a recognition that the claimed difference exists not as a result of an attempt by applicant to solve a problem but merely amounts to selection of expedients known to the artisan of ordinary skill as design choices.
Claims 8-14, directed to an apparatus embodiment of claims 1-7, recite similar features as claims 1-7, respectively, and are therefore rejected upon the same grounds as claims 1-7. Please see above rejections of claims 1-7. Li further discloses the apparatus as a computer-based system comprising a processor in communication with a non-transitory memory in at least Figure 9 with [0096-0105].
Claims 15-20, directed to an article of manufacturing embodiment of claims 1-6, recite similar features as claims 1-6, respectively, and are therefore rejected upon the same grounds as claim 1-6. Please see above rejections of claims 1-6. Li further discloses the non-transitory, computer-readable medium in at least [0105].
Conclusion
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN H ELLIOTT IV whose telephone number is (571)270-7163. The examiner can normally be reached M, T, R, F 5:00 AM-5:00 PM, W 5:00 AM-3:00 PM (EDT).
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BENJAMIN H. ELLIOTT IV
Primary Examiner
Art Unit 2474
/BENJAMIN H ELLIOTT IV/Primary Examiner, Art Unit 2474 May 4, 2026
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