Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 recites the limitation “wherein non-network data comprises…..” in the last limitation which should be “wherein the non-network data comprises…..”. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: Claim 14 uses abbreviation LSTM, DNN without spelling it out. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: Claim 15 recites the limitation ……“wherein non-network data comprises…..” in the last limitation which should be “……wherein the non-network data comprises…..”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: From the claim language, it is not clear if long short-term memory and LSTM are the same component or different component. Similarly, It is not clear if neural network and DNN are the same component or different component. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 19 recite “A computer program ….…”. The Broadest Reasonable Interpretation (BRI) of a claimed “computer program”, when read in light of the specification and as interpreted by one of ordinary skill in the art, may cover both statutory and non-statutory embodiments. In particular, “computer program” can sometimes be broadly interpreted as a product (machine and/or article of manufacture) as well as other non-statutory embodiments (software per se and/or transitory forms of signal transmission). Therefore, with the broadest reasonable interpretation, the computer program claimed may be non-statutory.
Claim Rejections - 35 USC § 103
1. 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 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.
2. 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 of this title, 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.
Claims 1-5, 9, 11-12 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No US 2020/0092732 to Raj et al. (hereinafter Raj) in view of U.S. Publication No US 2024/0284246 to Guo et al. (hereinafter Guo)
As to claims 1, 19 and 20, Raj discloses a computer implemented method comprising:
performing a forecasting process to predict 5G usage data for a target geographical area to include generating at least one of first to third 5G usage data predictions for the target geographical area (Raj; [0012] discloses the predicted future capacity and demand scoring may be based on a combination of forecasted usage patterns of existing UEs, growth patterns, UEs' heterogeneous application-based peak bandwidth demands over time, and end users' bandwidth impact to the network as a whole. Using machine learning algorithms, a scoring platform may handle multiple inputs simultaneously to optimize the number and placement of wireless stations (e.g., 5G wireless stations) given multiple constraints while best satisfying predicted future demand using the wireless stations' bandwidth capacities. [0022] discloses scoring platform 230 may retrieve (e.g., from data collection platform 240) observed data from subscriber UEs 105 to determine past travel patterns, application-based bandwidth use patterns, device-type implications for bandwidth (e.g., screen size, etc.), and to predict future demand patterns. Here Raj is applied for the 1st alternative first 5G usage data prediction),
wherein generating the first 5G usage data prediction comprises:
using a first model, to generate a first intermediate prediction by predicting non-network data of the target geographical area of a future time period based on non- network data of the target geographical area of a past time period (Raj; [0039] discloses Population grid projection logic 410 may identify population growth patterns, which may identify historic population patterns (e.g., growth, stability, contraction) based on population data 402 and project population growth areas. In one implementation, machine learning may be used to apply a time series forecast for particular geographic units);
using a second model, to generate a second intermediate prediction by predicting non-5G network usage data of the target geographical area of the future time period based on the non-network data and non-5G network usage data of the target geographical area of the past time period (Raj; [0041] discloses Application-based peak bandwidth pattern estimator 430 may predict future data use patterns and peak bandwidth use (referred to herein as application-use projections) based on the existing data patterns (e.g., extrapolated using machine learned algorithms) along with the population growth projections and visiting projections. [0020] discloses wireless stations 110 may utilize LTE standards); and
using a third model, to generate the first 5G usage data prediction by predicting 5G usage data of the target geographical arca of the future time period based on the predicted non-network data of the first intermediate prediction and the predicted non-5G network usage data of the second intermediate prediction (Raj; [0044] discloses Predicted end-user demand score generator 450 may obtain results from population grid projection logic 410, UE location pattern projector 420, application-based peak bandwidth pattern estimator 430, and bandwidth impact calculator 440 (shown generically in FIG. 4 as projections 462). According to one implementation, predicted end-user demand score generator 450 may weight and combine the results from projections 462 to generate predicted end-user demand scores 464 for geographic units. Scores may be applied, for example, to geographic units, such as individual census blocks or census tracts (e.g., groups of adjoining census blocks). Predicted end-user demand score generator 450 may identify available bandwidth capacity (e.g., at peak time slots) for wireless stations with coverage areas that include a particular geographic unit to determine an impact of the UEs 105 in the particular geographic unit on the overall capacity of the wireless station. The highest scoring geographic units may indicate geographic locations where predicted user demand will have the most significant bandwidth impact on adjacent wireless stations' capabilities. In one implementation, scores may be calculated for a particular future time period),
wherein generating the second 5G usage data prediction (Raj is applied for the 1st alternative and therefore this limitation is not considered here) comprises:
using a fourth model, which has been trained using data of the target geographical area, to generate a third intermediate prediction by predicting non-5G network usage data of the target geographical arca of the future time period based on the non-network data and the non-5G network usage data of the target geographical area of the past time period (Raj is applied for the 1st alternative and therefore this limitation is not considered here); and
using the third model to generate the second 5G usage data prediction by predicting 5G usage data of the target geographical arca of the future time period based on the predicted non-network data of the first intermediate prediction and the predicted non-5G network usage data of the third intermediate prediction (Raj is applied for the 1st alternative and therefore this limitation is not considered here),
wherein generating the third 5G usage data prediction comprises (Raj is applied for the 1st alternative and therefore this limitation is not considered here):
using a fifth model, which has been trained using data of the at least one reference geographical area, to generate a fourth intermediate prediction by predicting combined network usage data of the target geographical area of the future time period based on the non-network data of the target geographical area of the past time period the combined network usage data comprising usage data relating to 5G and non-5G networks (Raj is applied for the 1st alternative and therefore this limitation is not considered here);
using a sixth model, which has been trained using data of the at least one reference geographical area, to generate a fifth intermediate prediction by predicting 5G usage data of the target geographical area of the future time period based on the non-network data and the non-5G network usage data of the target geographical area of the past time period (Raj is applied for the 1st alternative and therefore this limitation is not considered here);
subtracting the predicted 5G usage data of the fifth intermediate prediction from the combined network usage data of the fourth intermediate prediction to generate a sixth intermediate prediction comprising predicted non-5G network usage data of the target geographical area of the future time period (Raj is applied for the 1st alternative and therefore this limitation is not considered here); and
using the third model to generate the third 5G usage data prediction by predicting 5G usage data of the target geographical area of the future time period based on the predicted non-network data of the first intermediate prediction and the predicted non-5G network usage data of the sixth intermediate prediction (Raj is applied for the 1st alternative and therefore this limitation is not considered here),
wherein non-network data comprises any of location data, demographic data, weather data, infrastructure data, and traffic data (Raj; Fig.6:610 shows and discloses UE travel pattern data. Here Raj is applied for the last alternative).
Raj discloses of generating data, but fails to disclose using a first model, which has been trained using data of the target geographical area. However, Guo discloses
using a first model, which has been trained using data of the target geographical area (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future),
using a second model, which has been trained using data of at least one reference geographical area (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future); and
using a third model, which has been trained using data of the at least one reference geographical area (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future)
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings so that he NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future.
As to claim 2, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein non-5G network usage data comprises usage data of at least one non-5G telecommunications network (Raj; [0049] discloses scoring platform 230 may identify and assign recommended placement locations for new wireless stations 110 (e.g., 5G NR wireless stations) using a combination of projections derived from population data 402, customer data 404, and wireless station data 406).
As to claim 3, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein the forecasting process comprises generating at least two of the first to third 5G usage data predictions and combining the at least two 5G usage data predictions to generate a final 5G forecast (Raj; [0049]-[0050] discloses scoring platform 230 may identify and assign recommended placement locations for new wireless stations 110 (e.g., 5G NR wireless stations) using a combination of projections derived from population data 402, customer data 404, and wireless station data 406. scoring platform 230 may provide feedback of placement locations 510 from the first time period to dynamically determine predicted end-user demand scores for geographic units in the subsequent time period. Placement locations 520 may, thus, account for the impact of placement locations 510, among other input data)
As to claim 4, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein combining the at least two 5G usage data predictions comprises computing a mean 5G usage data prediction (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future).
As to claim 5, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein the forecasting process comprises generating at least two of the first to third 5G usage data predictions and combining the at least two 5G usage data predictions to generate a predicted range of 5G usage data (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future).
As to claim 8, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein
the first model has been trained based on non-network data of the target geographical area of a first time period and non-network data of the target geographical area of a second time period before the first time period (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future. Here Guois applied for the 1st alternative);
the second model has been trained based on non-5G network usage data of the at least one reference geographical area of the second time period and based on non-network data and non-5G network usage data of the at least one reference geographical area of the first time period (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future. Here Guois applied for the 1st alternative);
the third model has been trained based on 5G usage data, non-network data, and the non-5G network usage data of the at least one reference geographical area of the second time period (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future. Here Guois applied for the 1st alternative);
the fourth model has been trained based on non-5G network usage data of the target geographical area of the second time period and based on the non-network data and non-5G network usage data of the target geographical area of the first time period;
the fifth model has been trained based on combined network usage data of the at least one reference geographical area of the second time period and based on the non-network of the at least one reference geographical area of the first time period; and
the sixth model has been trained based on the 5G usage data of the at least one reference geographical area of the second time period and based on the non- network data and the non-5G network usage data of the at least one reference geographical area of the first time period.
As to claim 9, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses further comprising performing a training process before performing the forecasting process, the training process comprising training at least one of the first to sixth models (Guo; [0031] discloses a network data analytics function (NWDAF) network element (also referred to as an NWDAF) is a key network element that provides intelligence capability for the 5th generation mobile communication technology (5G) system. Through interaction with a core network element and operations administration and maintenance (OAM) (i.e., an OAM network element), the NWDAF network element can obtain data and generate an analytics result of relevant communication situation for an analytics target. “QoS sustainability” is one of analytics results that the NWDAF network element can analyze and notify, and “QoS sustainability” can provide QoS change statistics of the analytics target in a certain area over a certain time period in the past or QoS change predictions of the analytics target in a certain area over a certain time period in the future).
As to claim 11, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein the first to sixth models comprise encoder-decoder models (Raj; [0012] discloses the predicted future capacity and demand scoring may be based on a combination of forecasted usage patterns of existing UEs, growth patterns, UEs' heterogeneous application-based peak bandwidth demands over time, and end users' bandwidth impact to the network as a whole. Using machine learning algorithms, a scoring platform may handle multiple inputs simultaneously to optimize the number and placement of wireless stations (e.g., 5G wireless stations) given multiple constraints while best satisfying predicted future demand using the wireless stations' bandwidth capacities. [0022] discloses scoring platform 230 may retrieve (e.g., from data collection platform 240) observed data from subscriber UEs 105 to determine past travel patterns, application-based bandwidth use patterns, device-type implications for bandwidth (e.g., screen size, etc.), and to predict future demand patterns).
As to claim 12, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein the first to sixth models each comprises a self-attention network (Raj; [0012] discloses the predicted future capacity and demand scoring may be based on a combination of forecasted usage patterns of existing UEs, growth patterns, UEs' heterogeneous application-based peak bandwidth demands over time, and end users' bandwidth impact to the network as a whole. Using machine learning algorithms, a scoring platform may handle multiple inputs simultaneously to optimize the number and placement of wireless stations (e.g., 5G wireless stations) given multiple constraints while best satisfying predicted future demand using the wireless stations' bandwidth capacities. [0022] discloses scoring platform 230 may retrieve (e.g., from data collection platform 240) observed data from subscriber UEs 105 to determine past travel patterns, application-based bandwidth use patterns, device-type implications for bandwidth (e.g., screen size, etc.), and to predict future demand patterns).
As to claim 15, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein non- network data comprises a population or population density of the area concerned and location data indicating the location and extent of the area concerned (Raj; Fig.6:610 discloses population growth data. Here Raj is applied for the 1st alternative)
As to claim 16, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein 5G usage data, non-5G usage data, and combined network usage data each comprises values over time of at least one variable, the at least one variable comprising any of:
a number of active users (Raj; [0012] discloses the predicted future capacity and demand scoring may be based on a combination of forecasted usage patterns of existing UEs. Here Raj is applied for the 1st alternative);
a number and/or length of video streams and/or an amount of data/bandwidth used for video streaming;
a number and/or length of calls and/or an amount of data/bandwidth used for calls;
a number and/or size of SMS messages and/or an amount of data/bandwidth used for SMS messages; and
a usage amount of the internet and/or an amount of data/bandwidth used for internet-related processes and/or an amount of data/bandwidth exchanged via the internet.
As to claim 17, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein the predicted 5G usage data comprises predicted values over time of at least one variable, the at least one variable comprising any of:
a number of active users (Raj; [0012] discloses the predicted future capacity and demand scoring may be based on a combination of forecasted usage patterns of existing UEs. Here Raj is applied for the 1st alternative);
a number and/or length of video streams and/or an amount of data/bandwidth used for video streaming;
a number and/or length of calls and/or an amount of data/bandwidth used for calls;
a number and/or size of SMS messages and/or an amount of data/bandwidth used for SMS messages; and
a usage amount of the internet and/or an amount of data/bandwidth used for internet-related processes and/or an amount of data/bandwidth exchanged via the internet.
As to claim 18, the rejection of claim 1 as listed above is incorporated herein. In addition, Raj-Guo discloses wherein demographic data comprises at least one of a population, a population density, and an economic background (Raj; Fig.6:610 discloses population growth data. Here Raj is applied for the 1st alternative)
Allowable Subject Matter
Claims 6-7 and 10 are objected, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims
Conclusion
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/FAISAL CHOUDHURY/Primary Examiner, Art Unit 2478
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