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 1-17, 19-21 are pending.
Response to Arguments
Applicant's arguments filed 10/14/2025 have been fully considered but they are not persuasive. In page 10-12, Applicant seeks distinction with the cited prior art Azar et al. by invoking the descriptions disclosed in the Specification, ¶0021 through 0066, and Fig. 3. While descriptions are appreciated for purpose of understanding the invention, but it is improper to use the Specification against the cited reference in applicant's argument that the references fail to show certain features of the invention. It is noted that the specific features upon which applicant relies in the cited section of the Specifications are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In addition, Applicant merely offers a brief description of Azar’s disclosure and goes on to conclude the distinction with the claims without any further analysis and explanation. Thus, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant further argues:
Azar is directed to network planning and optimization with respect to coverage,
capacity, and potentially positioning metrics, but within a general radio planning context. (…), Azar does not disclose or suggest a tuning parameter used in a single max-min problem that regulates a throughput-positioning ratio for jointly optimizingnetworkthroughputandpositioningperformance,asrecitedinindependentclaims1,14and15.
Rather,theonlyparameterconsideredbyAzar,asnotedintheOfficeAction,is a parameter used to control the price of robustness in robust optimization. This parameter specifically is used to provide a trade-off between robustness(i.e., the degree of uncertainty vs. the cost of an additional feature) and energy consumption. See Azar, Section IV-A. 1) and
(2), page 4828, and Section IV-B.2), page 4830. Thus, this parameter does not regulate a throughput-positioning ratio or allow to jointly optimize regulates network throughput and positioning performance, as required by independent claims 1, 14 and 15.
The examiner respectfully disagrees.
The arguments
The claimed language is extremely vague, offering no details for one of ordinary skill in the art to formulate a specific equation for this so-called “joint throughput-positioning radio planning problem formulated as a single max-min problem “. This is analogous to a recipe with the tittle only without any specific instruction of how to prepare each ingredients and how to mix and cook them. With this non-existence level of descriptions offered by the claims, the examiner asserts it is unreasonable to demand such a high bar of disclosure from the reference of record.
Simply reciting “min-max problem” or “parameter does not regulate a throughput-positioning ratio” without defining a specific functional algorithm, variables, constraints, means the person of ordinary skill in the art would not able to determine the metes and bounds of the claim, especially when this is supposed to be the core of the invention which Applicant claims to be novel. Because Applicant uses generalized language rather than describing a concrete application, the claim does not provide an objective standard for what constitute the claimed limitations.
Furthermore, the weighting mechanism is disclosed explicitly as weighting mechanism. As in the cited sections of the rejections, the reference teaches to use linear combination of objective criteria to form a single objective function, where different objectives are given a certain weight between 0 and 1. It further specifies regulation of emphasis by assigning higher or lower weight so as more or less emphasis is/are given on a given objective (i.e. the system uses the weight parameter to regulate the importance of metric over another). The reference further identifies global throughput and positioning metrics like handover zones as these conflicting objectives. Therefore, a weight that puts more emphasis on one over another is functionally identical to the claimed “tuning parameter” that regulates a ratio.
As for the min-max problem, the min-max in itself intrinsic in the disclosed optimization. The reference discloses trade-off management in at least 4822-4823, Section II-A. The reference discusses Pareto optimality, noting that there is no solution that can improve one objective without degrading another. This is the math foundation of the trade-off in a min-max problem. The reference specifically discloses a parameter which is to control the price of robustness, which can also be an alternative for the tuning parameter.
Indeed, in compared to the high level of ambiguity of the claims, the reference of record offers elevated details that meets each and every single limitations. Thus the arguments are not persuasive.
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 1-17, 19-21 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.
Per MPEP 2173.05(g), “the use of functional language in a claim may fail "to provide a clear-cut indication of the scope of the subject matter embraced by the claim" and thus be indefinite. In re Swinehart, 439 F.2d 210, 213 (CCPA 1971). For example, when claims merely recite a description of a problem to be solved or a function or result achieved by the invention, the boundaries of the claim scope may be unclear”.
Datamize LLC v. Plumtree Software Inc., 417 F.3d 1342, 75 USPQ2d 1801 (Fed. Cir. 2005) A claim is indefinite because it is directed to an intended result and does not provide a clear cut indication of scope because it imposed no structural limits,
In this case, Claim 1, 14, and 15 are indefinite in view of the basis above. Specifically:
The claims as amended cite:
“obtaining, by executing a joint throughput-positioning radio planning problem formulated as a single max-min problem that uses a tuning parameter that regulates a throughput-positioning ratio for jointly optimizing network throughput and positioning performance”
The amendment simply rephrases the original languages and barely provides any further clarifications to the claims.
The term “a joint throughput-positioning radio planning problem formulated as a single min-max problem” lack sufficient clarity as to specific structures, steps, or implementation. It does not specify what parameters; metrics being used as input other than the “tuning parameter” being vaguely described as a regulator without any specific on how it is used. The so-called “min-max problem” is just another wordy way of saying optimization (minimizing extra costs to achieve max results in a realistic scenario), it does still not give the one of ordinary skill in the art how to formulate the so-called min-max problem, what equations, what parameters involves, the mathematical expressions involved, where in the equations the so-called tuning parameter is involved. Applicant cannot expect the audience to come up with their own min-max problem when it is supposed to be the point of novelty claimed by Applicant.
Furthermore, the term “regulating” is a high level term and lacks an objective technical clarity, (regulating in what way?). Essentially, the claims merely give a recipe with brief description of intended result and a single line of vague instructions without telling the audience what the ingredients and specific preparation directions. Thus a person of ordinary skill in the art cannot determine, with reasonable certainty, what constitutes the claimed “min-max problem” or a tuning parameter that regulates the process, and how to solve it, as well as how its execution would formulate and solving the claimed problem of “min-max”. This issue extend to claim 7 with similarly-constructed limitations.
The claims as presented are constructed in an ambiguous manner that is repleted with functional and conceptual abstractions without little to no practical, discrete, grounded steps, and in addition often mixing heavy use of generalized academic (math notation) language inside legal language (i.e. claim language).
As such claims 1, 14, and 15 are indefinite.
Dependent claims 2-13, 16-18 inherit the shortcoming and are rejected by the same reasoning. It is noted that the similarly ambiguous language of “the positioning optimization problem is formulated as a min-max problem” are repeated in several instances in several dependent claims, for example claims 7 and 8.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 19 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 19 as newly added recites “the tuning parameter defines a bit-rate with respect to accuracy of a user's position”.
Per MPEP 2163 - the issue of a lack of adequate written description arises, even for an original claim, when an aspect of the claimed invention has not been described with sufficient particularity such that one skilled in the art would recognize that the applicant had possession of the claimed invention. The section further states: “A lack of adequate written description issue also arises if the knowledge and level of skill in the art would not permit one skilled in the art to immediately envisage the product claimed from the disclosed process”.
An accuracy of the user’s position requires the system to know the true position, the calculation position to derive the accuracy parameter. The tuning parameter then is stated to define a relationship of a bit-rate (of what?) with respect to this accuracy.
The Specification shows no algorithm to calculate this true position, thereby to obtain an accuracy parameter, nor does the Specification explain how exactly the tuning parameter defines the relationship between the bit-rate with accuracy, or which communication path the bit-rate parameter belongs to. The serious lacking of details in this subject matter as claimed raise legimate question on enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same.
Applicant merely states an intended result but left one of ordinary skill in the art guessing on the specific algorithm/structures necessary to obtain the position accuracy and to define a specific mathematical trade off between bit-rate and said accuracy. Given the Specification is silent on such instructions required by the MPEP 2163, the claims are rejected.
Given the lacking in guidance of the Specification how the limitations is/are achieved, the evaluation of novelty of claim 19 is held in abeyance of Applicant’s clarification of the claim.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 12-17, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Azar et al. (NPL – Planning Wireless Cellular Networks of the Future: Outlook, Challenges and Opportunities” – 2017).
As to claims 1 and 15:
Azar discloses:
A computer-implemented method for optimizing positioning performance of a cellular network (Abstract – Introduction), the method comprising:
defining a target area; (See 4822 - Section II, 4829 – sub-Section 6, an area of interest (i.e. area to be served) is defined for planning the cells)
identifying a set (S) of base station deployment candidate sites (j) within the target area; (See 4824, 4836, “a set of candidate sites is first pre-determined and used as input to the CP, to incorporate the real estate constraints”, “a set of candidate locations, up to a given number of small cell sites in a deployed macro cell network”)
obtaining, by executing a joint throughput-positioning radio planning problem formulated as a single max-min problem (See at least 4822-4823, Section II-A, the disclosure describe a single optimization process with steps 1-4 for cell planning, wherein minimizing cost, while max), i.e. “a certain proportion of the area of each cell should overlap with neighboring cells to satisfy HO conditions. HO zones are essential to guarantee continuity of service between the sectors” . See 4825, “The best locations to install base stations; The types of base station optimal for each location;”, See 4828, “optimize the number of base stations and their locations for energy efficiency”, i.e. the performance of positioning the base stations is measured by energy efficiency. See 4833, “analyse the impact of location of RS”. ) that uses a tuning parameter that regulates a throughput-positioning ratio for jointly optimizing network throughput and positioning performance active candidate sites as a subset of the set (S) of base station deployment candidate sites (j);
(See 4824, “use a linear combination of different objective criteria to form a single objective function, where different objectives are given a certain weight between 0 and 1. (…) assigning higher or lower weight to put more or less emphasis on a given objective”. See also 4830, “robust optimization a parameter 0 is used to control the price of robustness, (…).by deploying less BSs or serving more users with the same number of BSs using the proposed robust optimization approach”. The reference identifies global throughput and positioning related metrics like handover zones, as these conflicting objectives. A weight is used to emphasis one over another is functionally identical to the claimed tuning parameter)
and determining the obtained active candidate sites as the sites at which base stations are to be deployed. (See at least 4824, “a set of candidate sites is first pre-determined and used as input to the CP, to incorporate the real estate constraints. The objective, thus, is to find the optimum subset of BS locations. These potential BS locations are determined by taking into account the constraints”. See 4825, cell planning output includes optimal number of base station, best types, best location. 4836 – “select, from a set of candidate locations, up to a given number of small cell sites in a deployed macro cell network”).
Claim 15 is directed to a CRM having instruction to perform a method (Abstract, Intro Section in page 4821, computer implementing the planning) similar to that of claim 1 and is rejected by the same reasoning.
As to claim 14:
Azar discloses:
A system for optimizing positioning performance of a cellular network, in particular a 5G-NR network or beyond, the system comprising memory and one or more processors, (Abstract, Intro Section in page 4821, computer implementing the planning) the system configured to: define a target area; (See 4822 - Section II, 4829 – sub-Section 6, an area of interest (i.e. area to be served) is defined for planning the cells)
determine a set (S) of base station deployment candidate sites (j) within the target area; (See 4824, 4836, “a set of candidate sites is first pre-determined and used as input to the CP, to incorporate the real estate constraints”, “a set of candidate locations, up to a given number of small cell sites in a deployed macro cell network”)
Set up a joint throughput-positioning radio planning problem formulated as a single max-min problem (See at least 4822-4823, Section II-A, the disclosure describe a single optimization process with steps 1-4 for cell planning, wherein minimizing cost, while max), i.e. “a certain proportion of the area of each cell should overlap with neighboring cells to satisfy HO conditions. HO zones are essential to guarantee continuity of service between the sectors” . See 4825, “The best locations to install base stations; The types of base station optimal for each location;”, See 4828, “optimize the number of base stations and their locations for energy efficiency”, i.e. the performance of positioning the base stations is measured by energy efficiency. See 4833, “analyse the impact of location of RS”. See also 4824, 4832) that uses a tuning parameter that regulates a throughput-positioning ratio for jointly optimizing network throughput and positioning performance;
(See 4824, “use a linear combination of different objective criteria to form a single objective function, where different objectives are given a certain weight between 0 and 1. (…) assigning higher or lower weight to put more or less emphasis on a given objective”. See also 4830, “robust optimization a parameter 0 is used to control the price of robustness, (…).by deploying less BSs or serving more users with the same number of BSs using the proposed robust optimization approach”. The reference identifies global throughput and positioning related metrics like handover zones, as these conflicting objectives. A weight is used to emphasis one over another is functionally identical to the claimed tuning parameter)
Obtaining, by solving the joint throughput-positioning radio planning problem, active candidate sites as a subset of the set (S) of base station deployment candidate sites (j);
and determine the obtained active candidate sites as the sites at which base stations are to be deployed. (See at least 4824, “a set of candidate sites is first pre-determined and used as input to the CP, to incorporate the real estate constraints. The objective, thus, is to find the optimum subset of BS locations. These potential BS locations are determined by taking into account the constraints”. See 4825, cell planning output includes optimal number of base station, best types, best location. 4836 – “select, from a set of candidate locations, up to a given number of small cell sites in a deployed macro cell network”).
As to claims 16, 17:
Azar discloses all limitations of claim 1/14/15, wherein the cellular network is a 5G-NR network. (Fig. 6, 5G network)
As to claim 2:
Azar discloses all limitations of claim 1, wherein executing the joint throughput-positioning radio planning problem includes: specifying a set (T) of test points (t) that sample the target area; and implementing decision variables x.sub.j∈{0,1} and a.sub.tjΣ{0,1}, wherein x.sub.j indicates whether a base station is deployed at a candidate site, and wherein a.sub.tj indicates the maximum Signal-to-Noise-and-Interference-Ratio (SINR), association between a user experience (UE) at a test point and the candidate site. (See Fig. 6, 4824, “Test point based traffic models are often used for CP traffic modeling, for the sake of practicality [6], [34]–[36]. In this model, an area is characterized over a time interval and all located mobile terminals are bundled into a single test point. This point represents the cumulative traffic, or traffic intensity, from all these terminals, over the determined interval. 2) Potential Site Locations: Theoretically, a base station can be installed anywhere. However in the real world, a set of candidate sites is first pre-determined and used as input to the CP, to incorporate the real estate constraints. The objective, thus, is to find the optimum subset of BS locations. These potential BS locations are determined by taking into account the constraints such as, socio-economic feasibility and availability of site(s), traffic density, building heights, terrain height(s) and preexistence of a site(s) by the same or other operators.” This explicitly show using test points sample over time/location, and choose whether and which a candidate site to activate (binary choice, yes or no, for a given candidate j), See 4834, “cell association based on maximum received signal strength (Max-RSS) strategy may yield different optimal cells for uplink and downlink”, “different cell association in uplink/downlink will result in different interference models and resulting SINRs in the two links e.g., UEs sharing same BS will be orthogonal to one another on the downlink while they may interfere with each other on the uplink if they are transmitting to different base stations”, i.e. the role of SINR-based association between test points and base stations for maximum SINR)
As to claim 3:
Azar discloses all limitations of claim 2, wherein a distribution of the test points (t) within the target area matches an expected distribution of cellular users within the target area. (See 4824, “User traffic distribution is a main factor that ultimately determines the cellular system plan and, hence, is a key input in the CP process. In GSM (mono-service systems), for instance, geographical characterization of the traffic distribution is sufficient (…) an area is characterized over a time interval and all located mobile terminals are bundled into a single test point. This point represents the cumulative traffic, or traffic intensity, from all these terminals, over the determined interval”. See 4835, “the simulated annealing approach move the small cells from their rectangular grid positions to a Gaussian deployment, following that of the user distribution. Such hotspot areas are characterised with temporary traffic surges, such as football stadiums, where the user density is very high around a football match and very low otherwise”, this show that simulation actually follow user distributions in realistic manner.)
As to claim 4:
Azar discloses all limitations of claim 1, wherein the set (S) of base station deployment candidate sites is derived from a pre-negotiation phase between operators and third parties in consideration of logistical and/or administrative constraints;
or wherein the set (S) of base station deployment candidate sites (j) is determined to be a superset of the sites where LTE base stations have already been deployed;
or wherein the set (S) of base station deployment candidate sites (j) is determined as some random sampling of the target area.
(See 4832, Section D, the set of base station whose locations are random. 4832, “assumes HSDPA system with pre-presence of fixed number of micro sites. For performance evaluation they make use of a planning tool and Monte Carlo method using real network data that includes: 3-D geolocations of the base stations, digital elevation map and digital clutter map data, antenna characteristics (pattern, tilt, and cable losses), total transmit power levels and spatial broadband traffic”, this explicitly disclose the idea that existing legacy base station are part at the testing target area and are used as performance reference to judge for optimization)
As to claim 5:
Azar discloses all limitations of claim 1, wherein executing solving a joint throughput-positioning radio planning problem determines a balance among test points (t), with respect to network throughput and positioning performance. (See 4832, “address BS location problem with a single objective of minimizing outage (evaluated by Monte Carlo simulations)”, See 4824, Section B, notably “use a linear combination of different objective criteria to form a single objective function, where different objectives are given a certain weight”. This explicitly discloses unifying all different objective into one planning problem that aims to maximizing or minimizing a criteria parameter (cost, coverage, etc.)
As to claim 6:
Azar discloses all limitations of claim 5, wherein an objective function of the joint throughput-positioning planning problem contains the tuning parameter that regulates the throughput-positioning ratio. (See 4824, Section B, notably “use a linear combination of different objective criteria to form a single objective function, where different objectives are given a certain weight between 0 and 1. (…) weighted multi-objective functions give more flexibility to the network planner by assigning higher or lower weight to put more or less emphasis on a given objective”.)
As to claim 12:
Azar discloses all limitations of claim 1, wherein the target area encompasses a number of pre-deployed legacy base stations that provide a performance baseline for the joint performance optimization. (See 4835, “While planning, conventional cellular networks consisting only the macro cells, even if the macro cells are not deployed at the ideal location but somewhat near the optimal location, the larger radius of the macro cell, and the ability to tweak antenna tilts and azimuths compensates the difference between optimal and the actual location”, 4832, “assumes HSDPA system with pre-presence of fixed number of micro sites. For performance evaluation they make use of a planning tool and Monte Carlo method using real network data that includes: 3-D geolocations of the base stations, digital elevation map and digital clutter map data, antenna characteristics (pattern, tilt, and cable losses), total transmit power levels and spatial broadband traffic”, this explicitly disclose the idea that existing legacy base station are part at the testing target area and are used as performance reference to judge for optimization)
As to claim 13:
Azar discloses all limitations of claim 1, wherein the tuning parameter is implemented in such a way that backward compatibility in the network deployment process is provided by setting the tuning parameter to 0. (See 4824, “One way is to use a linear combination of different objective criteria to form a single objective function, where different objectives are given a certain weight between 0 and 1”, “weighted multi-objective functions give more flexibility to the network planner by assigning higher or lower weight to put more or less emphasis on a given objective”. This explicitly disclose that weight (tuning parameter) can be set to zero to disable one objective, thus preserving backward compatibility with older throughput-only planning system).
As to claim 20:
Azar discloses all limitations of claim 1, wherein sampling the target area by a set (T) of test points (t), wherein a test point distribution of the test points (t) matches an expected distribution of cellular users in the target area. (See at least Section II-C, test point based traffic models, a set of test points are sampled, wherein a test point represented a bundle of users concentrated at a particular location associated with the test point during a given time interval. Additionally, section V-D further connects user distribution to cell planning by discussing how optimization objectives can be formulated as a product of base station functions and user density)
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) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azar et al. (NPL – Planning Wireless Cellular Networks of the Future: Outlook, Challenges and Opportunities” – 2017) and in further view of Tan (US 2016/016562).
As to claim 7:
Azar discloses all limitations of claim 1, regarding executing the joint throughput-positioning planning problem includes: solving a network throughput optimization sub-problem that aims at optimizing the network throughput performance, while keeping the positioning performances below a configurable positioning performance threshold; and solving a positioning optimization sub-problem that minimizing a positioning error bound (PEB) experienced at test point (t), while keeping the network throughput performance above a configurable network throughput performance threshold. (Azar discloses in 4824, section B, solving a multivariable objective problem with sub-problems for multiple respective objectives, such maximizing beneficial parameters (coverage, quality) and minimizing negative parameters (cost). In Tan, ¶0101-0103, optimization of performance include minimizing rate of failure occurrences)
Azar discloses in 4824, section B, solving a multivariable objective problem with sub-problems for multiple respective objectives, such as finding configuration to maximize coverage while minimizing cost/power consumption.
Azar, however, does not explicitly disclose using thresholds as a milestone to determining whether the goals are met.
Tan, in a related field of endeavor, discloses at least ¶0149, 0148, 0069, that maximization problem and minimization problem use threshold to determine whether the respective goals are achieved. For example, for cost (minimization), keeping cost under a threshold. For performance quality, keeping the performance over a performance threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the minimization and maximization problems to be solved in Azar to employ threshold to act as boundary. The use of threshold parameters are well known in the art as boundary-setting measure to ensure an expected level of performance is ensured (i.e. dictated by threshold, for example, maintaining at least a X Mbps through put per user per cell). This implementation not only to allow ease of setting a desired level of performance to also provide convenient and systematic way to adjust said level by simply changing the threshold value.
As to claim 8:
Kim in view of Tan discloses all limitations of claim 7, wherein the network throughput optimization sub-problem is formulated as a max-min problem maximizing ta minimum network throughput experienced at the test points t, and wherein the positioning optimization problem is formulated as a min-max problem that minimizes a maximum position error bound (PEB) experienced at the test points (t). (Azar discloses in 4824, section B, solving a multivariable objective problem with sub-problems for multiple respective objectives, such maximizing beneficial parameters (coverage, quality) and minimizing negative parameters (cost). In Tan, ¶0101-0103, optimization of performance include minimizing rate of failure occurrences)
As to claim 9:
Kim in view of Tan discloses all limitations of claim 7, wherein the positioning performance threshold and the network throughput performance threshold are configured by adaptively processing the optimal values of each of the optimization sub-problems. (Tan, Abstract, adaptively adjust the same currently considered optimal parameter in a progressively way until a condition is met)
Allowable Subject Matter
Claims 10-11, and 21 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, and all outstanding issues under 35 USC 101 and/or 112 discussed in the Office Action will have been sufficiently remedied.
The references discloses all limitations of claim 1, 7, and 9, however fail to disclose:
“solving the network throughput and positioning optimization sub-problems in a closed-loop fashion, including the steps of: determining a first optimized network throughput performance value by solving the network throughput optimization sub-problem, scaling the first optimized network throughput performance value and using the scaled value as network throughput performance threshold for solving the positioning optimization sub-problem, thereby obtaining a first optimized positioning performance value, and scaling the first optimized positioning performance value and using the scaled value as positioning performance threshold for solving the network throughput optimization sub-problem, thereby obtaining a second optimized network throughput performance value.”, and;
“he joint throughput-positioning radio planning problem takes as input the tuning parameter and the set (S) of the base station deployment candidate sites () and iteratively repeats until a convergence is reached that maximizes minimum network throughput experienced at test points (t) that sample the target area and minimizes a maximum position error bound (PEB) experienced at the test points (t), wherein the tuning parameter is a scalar factor that trades off between the network throughput and the positioning performance within the target area”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11172246 - A system for bitrate adaptation for low latency streaming includes an interface and a processor. The interface is configured to receive statistics, wherein the statistics comprise a server latency and a buffer level. The processor is configured to perform a set of checks based at least in part on the statistics, determine a streaming bitrate based at least in part on the set of checks, and indicate the streaming bitrate.
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.
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/QUAN M HUA/Primary Examiner, Art Unit 2645