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 .
Priority
All claims have priority date 10/19/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/07/2023. The submission is in compliance with the provisions of 37 CFR 1.97.
Drawings
The drawings are objected to because they are gray scale and not black and white. The drawings are objected to because Figures 1-7 all include gray elements. Drawings must be black and white (monochrome) except when another form (grayscale or color) is the only practicable medium for illustrating the claimed invention. For Figures 1-7 black and white drawings are sufficient to illustrate the claimed invention. Additionally figures on page 7-8 should be renumbered 7A and 7B, respectively.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1, 2, and 12 are objected to because of the following informalities: regarding claim 1 use of a period at line 11 after “another” should be modified to a semicolon. Appropriate correction is required. Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations. See Fressola v. Manbeck, 36 USPQ2d 1211 (D.D.C. 1995). See MPEP 608.01(m).
The claims 2 and 12 are objected to because they include reference characters KPIs which are not enclosed within parentheses. Examiner suggests amending to Key Performance Indicators (KPIs).
Reference characters corresponding to elements recited in the detailed description of the drawings and used in conjunction with the recitation of the same element or group of elements in the claims should be enclosed within parentheses so as to avoid confusion with other numbers or characters which may appear in the claims. See MPEP § 608.01(m).
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematical calculations and/or a mental process, as enumerated abstract ideas listed in the 2019 Revised Patent Subject Matter Eligibility Guidance. This judicial exception is not integrated into a practical application as analyzed below. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception as analyzed below.
CLAIM ANALYSIS
STEP 1:
YES. Claims 1-20 meet the statutory categories.
Claims 1-20 fall within a statutory category of process.
ANALYSIS INDEPENDENT CLAIMS 1 and 11
STEP 2A:
PRONG ONE YES. Claim 1 is directed to a judicial exception.
Claim 1 and 11 limitations fall within one of the groupings of abstract ideas enumerated in MPEP 2106.01(a)(2). A judicial exception being directed to an abstract idea. As a representative example, take Claim 1 and claim 11, where nonbold text represents the abstract idea steps and, additional elements appearing in bold analyzed in Steps 2A, 2B below).
Claim 1 A computer implemented method comprising:
accessing a plurality of previously derived cellular base station performance scores,
including accessing a previously derived cellular base station performance score for each of a plurality of similarly situated cellular base stations on a cellular network, the plurality of similarly situated cellular base stations having characteristics within a required similarity to one another and being associated with conditions within another required similarity to one another, the plurality of previously derived cellular base station performance scores including a previously derived cellular base station performance score and one or more other previously derived cellular base station performance scores;
comparing the plurality of previously derived cellular base station performance scores to one another.
calculating that the previously derived cellular base station performance score varies by more than a specified threshold from the one or more other previously derived cellular base station performance scores;
identifying the cellular base station corresponding to the previously derived cellular base station performance score; and
determining that the identified cellular base station is not delivering optimum performance based at least on the previously derived cellular base station performance score varying by more than the specified threshold from the one or more other previously derived cellular base station performance scores.
Claim 11 A computer implemented method comprising:
detecting that a cellular base station has been added to cellular network;
accessing characteristics of the cellular base station and conditions associated with the cellular base station;
identifying one or more similarly situated cellular base stations connected to the cellular network, having characteristics similar to the characteristics of the cellular base station, and having associated conditions similar to the conditions associated with the cellular base station;
accessing one or more previously derived performance scores, including accessing a previously derived performance score for each of the one or more similarly situated cellular base stations;
deriving a performance score for the cellular base station from the one or more previously derived performance scores; and
predicting performance of the cellular base station on the cellular network based at least on the derived performance score.
In plain language, the claim steps above in the broadest reasonable interpretation (BRI) comprise as follows:
“accessing” is claimed without any specifics, accessing a spreadsheet for performance scores can be a mental process including observation and evaluation, and can be done mentally in the human mind.
"comparing" is claimed generally without any specific algorithms and regardless would encompass mathematical calculations.
“calculating” is claimed generally without any specific algorithms and regardless would encompass mathematical calculations.
"identifying" encompasses observing and evaluate to identify (mental process)
"determining" encompasses mathematical calculations utilizing a mathematical model
"detecting" encompasses observing and evaluate to identify (mental process)
“deriving” is claimed generally without any specific algorithms and regardless would encompass mathematical calculations.
"predicting" is claimed generally without any specific algorithms and regardless would encompass mathematical calculations.
These steps are merely a mental process and/or mathematical calculations. Examiner notes mental process includes describe mental observations and evaluations that can be performed in the human mind using observation, evaluation, judgment, and opinion and also those performed with a pen/pencil or a general purpose computer (i.e. graphing, mapping, calculations), as noted in the case law cited above.
STEP 2A Prong Two:
NO. Evaluating additional elements recited in the claim individually and in combination, the claim as a whole does not integrate the exception into a practical application.
The claim as a whole does not integrate the recited judicial exception into a practical application of the exception and so the claim is “directed to” the judicial exception.
The additional elements recited in claim discussed previously do not constructively integrate the abstract idea into a practical application since no application is claimed other than the mathematical operation of deriving, predict, compare, determining and calculating or the mental process of accessing, identifying and detecting.
The additional elements of a computer implemented method in claim 1 and 11 amounts to using a generic computer to apply the abstract idea.
Thus, the judicial exception is not integrated into a practical application because Claims 1 and 11 do not provide an improvement to a tangible product or process.
STEP 2B:
NO. Evaluating additional elements recited, the claim as a whole does not recite additional elements that amount to significantly more than the judicial exception.
The analysis above in parts and re-evaluated again for the claims as a whole, the additional elements are mere description of generic wireless communication networks recited at a high level of generality and amount to little more than performing repetitive calculations, which is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
The limitations remain insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.
STEP 2A Dependent Claims 2-10, 12-20 :
Dependent claims recite additional mental process and/or mathematical concepts and no other additional elements that integrate the abstract idea into a practical exception.
The dependent claims further recite additional elements that are well known and understood descriptors of similarity and are mere data gathering, mathematical operations and technical qualifications (i.e. “5G”) recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data grouping based on similarity, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
STEP 2B Dependent Claims 2-10, 12-20 :
NO. Evaluating additional elements recited, the claim as a whole does not recite additional elements that amount to significantly more than the judicial exception.
The analysis above in parts and re-evaluated again for the claims as a whole, the additional elements are mere data grouping, categorization, gathering, mathematical operations and recited at a high level of generality and amount to data grouping and storage based on similarity in a cellular network, which is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
The limitations remain insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.
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.
Claims 1–3, and 7 are rejected under 35 U.S.C. § 102(a)(1) as anticipated by Chen et. al. ( US 20180220314 A1 hereafter Chen (reference in IDS)).
Consider claim 1. Chen discloses A computer implemented method comprising: ( Chen [0047] computer 122 may compare the temporal anomaly scores of a group 124 of access points 110 (such as access points 110-1 and 110-2) in order to determine spatial temporal anomaly scores for access points 110-1 and 110-2 that indicate a significance of the temporal anomaly scores in a spatial context.)
accessing a plurality of previously derived cellular base station performance scores, ( Chen [0047] scores of a group 124 of access points 110 )
including accessing a previously derived cellular base station performance score for each of a plurality of similarly situated cellular base stations on a cellular network, the plurality of similarly situated cellular base stations having characteristics ( Chen [0047] temporal anomaly scores (health index) of a group 124 of access points 110 ) within a required similarity to one another and being associated with conditions within another required similarity to one another, the plurality of previously derived cellular base station performance scores including a previously derived cellular base station performance score and one or more other previously derived cellular base station performance scores; ( Chen [0051] the computer may compare current values of the performance metric for the access points (base stations) with historical values of the performance metric )
comparing the plurality of previously derived cellular base station performance scores to one another.
calculating that the previously derived cellular base station performance score varies by more than a specified threshold from the one or more other previously derived cellular base station performance scores; ( Chen [0057] compare current values of the one or more performance metrics 318 for access points 110 with historical values 322 of the one or more performance metrics 318 for access points 110 )
identifying the cellular base station corresponding to the previously derived cellular base station performance score; and ( Chen [0100] benchmark of an access point may represent the expected values for its target access-point KPI at the target time )
determining that the identified cellular base station is not delivering optimum performance based at least on the previously derived cellular base station performance score varying by more than the specified threshold from the one or more other previously derived cellular base station performance scores. ( Chen [0100] thus it may serve as a measurement (score) of how anomalous (threshold) the target access-point KPI sample )
Consider claim 2, Chen also discloses all the limitations of claim 1, further comprising: accessing KPIs for each of the plurality of similarly situated cellular base stations; ( Chen [0078] KPI sample may include the target access point and access points that are deployed in the geographic vicinity )
accessing counters for each of the plurality of similarly situated cellular base stations; ( Chen [0064] pattern-breaking data points that do not conform to their expected values are identified )
deriving a plurality of cell performance indices including deriving cell performance index for each of the plurality of similarly situated cellular base stations from corresponding KPIs; ( Chen [0064] KPIs from a particular access point (e.g., RSSI, client counts, session length, traffic, etc.) may indicate significant functional issue )
deriving a plurality of cell health indices including deriving a cell health index for each of the plurality of similarly situated cellular base stations from corresponding counters; ( Chen [0062] In some embodiments, the detection technique may be used to detect anomalous values of key performance indicators (KPIs) of access points in large-scale Wi-Fi networks. Because of the dynamic and ad-hoc nature of these wireless networks, the detection technique may consider the temporal and spatial contexts (similar situated) of the target access-point KPI samples. The examiner notes remedial action due to identified anomaly FIG. 9 represent health status as Alarms and Events )
deriving the plurality of previously derived cellular base station performance scores from the plurality of cell performance indices and the plurality of cell health indices; and ( Chen [0064] As in other complex systems, anomalous samples of KPIs from a particular access point (e.g., RSSI, client counts, session length, traffic, etc.) may indicate significant functional issues )
storing the plurality of previously derived cellular base station performance scores in a database.( Chen [0056] processor 316 may store values (database scores) of the one or more performance metrics (derived performance) 318 )
Consider claim 3 Chen also discloses All the limitations of claim 2, wherein accessing a plurality of previously derived cellular base station performance scores comprises accessing the plurality of previously derived cellular base station performance scores from the database. ( Chen [0099] anomaly scores stored by the data module may be intermediate calculation results of previous instances of anomaly detection )
Consider claim 7. Chen also discloses all the limitations of claim 1, wherein the plurality of similarly situated cellular base stations having characteristics within a required similarity to one another comprises the plurality of similarly situated cellular base stations being located on similar terrain. ( Chen [0078] Note that the spatial context of the target access-point KPI sample may include the target access point and access points that are deployed in the geographic vicinity of or proximate to the target access point (such as access points in the same building, the same town, the same radio-frequency environment, etc.))
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4 and 10 are rejected under 35 U.S.C. § 103 as unpatentable over Chen et al. ( US 20180220314 A1 hereafter Chen (reference in IDS)) in view of Eleftheriadis et al ( US 20220400394 A1 hereafter Eleftheriadis).
Consider claim 4. Chen discloses all the limitations of claim 2.
However Chen fails to disclose KPIs corresponding to one or more of: accessibility, retainability, integrity, availability, and mobility of a cellular network.
While in a similar field of endeavor, Eleftheriadis suggests KPIs corresponding to one or more of: accessibility, retainability, integrity, availability, and mobility of a cellular network. ( Eleftheriadis [0061] KPIs may be reflective of any one of the following aspects how the network as a whole or a single node (a cell and its associated radio base station) is performing: accessibility, retainability, integrity, availability, mobility and energy efficiency.)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated accessing KPIs corresponding to specific characteristics of Eleftheriadis into the invention of Chen in order to improve cellular network performance.
Consider claim 10. Chen discloses all the limitations of claim 1.
However Chen fails to disclose wherein the cellular network is a 5G network.
While in a similar field of endeavor, Eleftheriadis suggests wherein the cellular network is a 5G network. ( Eleftheriadis [0002] 5th Generation, 5G )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the 5G of Eleftheriadis into the invention of Chen to improve cellular network performance.
Claims 5, and 8-9 are rejected under 35 U.S.C. § 103 as unpatentable over Chen et al. ( US 20180220314 A1 hereafter Chen(reference in IDS)) in view of Tarraf et al. (US 20160286411 A1 hereafter Tarraf (reference in IDS)).
Consider claim 5. Chen discloses all the limitations of claim 2.
However Chen fails to disclose access counters in one or more categories selected from among: access failures, access rejections, usage, throughput, transport, and radio environment.
While in a similar field of endeavor, Tarraf suggests wherein accessing counters for each of the plurality of similarly situated cellular base stations comprises access counters in one or more categories selected from among: access failures, access rejections, usage, throughput, transport, and radio environment ( Tarraf [0105] Number Of Blocked Calls,( access failures, access rejections) [0099] Total CE Usage (usage), [0087] UL and DL Stats For Each Sector/Carrier: Load, Erlangs (radio environment) and Throughput (throughput) [0114] Handoff Parameters (T_Add, T_Drop, Tt_Drop, T_Comp) (transport) )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated using access counters with throughput and other operator designated metrics of Tarraf into the invention of Chen to improve cellular network performance.
Consider claim 8. Chen discloses all the limitations of claim 1.
However Chen fails to explicitly disclose the plurality of similarly situated cellular base stations being positioned at similar heights.
While in a similar field of endeavor, Tarraf suggests the plurality of similarly situated cellular base stations being positioned at similar heights. ( Tarraf [0086] In step 401, the neighbor cells/sectors are determined based on the cells list table in the database 110. In step 402, the neighbor list is sorted by network statistics. As noted above, network statistics may include, but are not limited to, key performance Indicators (KPIs). and [0117] Antenna Height Above Ground And Sea Level )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the network parameters suggested by Tarraf to improve cellular network performance of the invention taught by Chen.
Consider claim 9. While Chen discloses all the limitations of claim 1.
However Chen fails to explicitly disclose the plurality of similarly situated cellular base station have similar tilts.
While in a similar field of endeavor, Tarraf suggests the plurality of similarly situated cellular base station have similar tilts. ( Tarraf [0086] In step 401, the neighbor cells/sectors are determined based on the cells list table in the database 110. In step 402, the neighbor list is sorted by network statistics. As noted above, network statistics may include, but are not limited to, key performance Indicators (KPIs). and [0118] Antenna Model, Azimuth BW, Elevation BW, Gain, Electrical And Mechanical Tilt )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the network parameters suggested by Tarraf to improve cellular network performance of the invention taught by Chen.
Claims 11-13, and 17 are rejected under 35 U.S.C. § 103 as unpatentable over Chen et al. ( US 20180220314 A1 hereafter Chen (reference in IDS)) in view of Tan et al ( US 20160162783 A1 hereafter Tan (reference in IDS).
Consider independent claim 11. Chen suggests, A computer implemented method comprising: ( Chen [0047] computer )
detecting that a cellular base station has been added to cellular network; ( access points (base stations) can detect each other Chen [0039][0040] )
accessing characteristics of the cellular base station and conditions associated with the cellular base station; ( Chen [0047] scores (characteristics and conditions) of a group 124 of access points 110 Note that group 124 may include access points that: have environments with a common characteristic, are geographically proximate to each other, and/or have approximately the same response to a change in an environmental factor. In some embodiments, prior to comparing the temporal anomaly scores of group 124)
identifying one or more similarly situated cellular base stations connected to the cellular network, having characteristics similar to the characteristics of the cellular base station, and having associated conditions similar to the conditions associated with the cellular base station; ( Chen [0047] temporal anomaly scores of a group 124 of access points 110. Note that group 124 may include access points that: have environments with a common characteristic, are geographically proximate to each other, and/or have approximately the same response to a change in an environmental factor. In some embodiments, prior to comparing the temporal anomaly scores of group 124 )
accessing one or more previously derived performance scores, including accessing a previously derived performance score for each of the one or more similarly situated cellular base stations; ( Chen [0100] benchmark of an access point may represent the expected values for its target access-point KPI at the target time)
deriving a performance score for the cellular base station from the one or more previously derived performance scores; ( Chen [0057] compare current values of the one or more performance metrics 318 for access points 110 with historical values 322 of the one or more performance metrics 318 for access points 110)
However Chen fails to explicitly disclose predicting performance of the cellular base station on the cellular network based at least on the derived performance score
While in a similar field of endeavor, Tan suggests predicting performance of the cellular base station on the cellular network based at least on the derived performance score. ( Tan [0123] KPI models for predictive analysis. In general, KPI predictive models are algorithms that identify which KPIs are likely to be a root cause of a poor key quality indicator (KQI), such as packet loss rate. For example, in the context of Coverage Capacity Optimization (CCO), antenna uptilt may be increased when a poor KQI is associated with an RSRP level, as that would indicate the root cause is poor coverage, while antenna downtilt may be increased when a poor KQI is associated with interference, as that would indicate the root cause is poor coverage. KPI predictive models for groups of similar cells can predict network performance given predictors such as traffic and resource consumption variables )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated using predictive analysis of Tan into the invention of Chen to improve cellular network performance based on past indicators.
Consider claim 12. Chen and Tan suggest all the limitations of claim 11, Chen also suggests further comprising: accessing KPIs for each of the one or more similarly situated cellular base stations; ( Chen [0078] KPI sample may include the target access point and access points that are deployed in the geographic vicinity )
accessing counters for each of the one or more similarly situated cellular base stations; ( Chen [0064] KPIs from a particular access point (e.g., RSSI, client counts, session length, traffic, etc.) may indicate significant functional issue )
deriving one or more cell performance indices including deriving cell performance index for each of the one or more similarly situated cellular base stations from corresponding KPIs; ( Chen [0058] processor 316 may optionally identify (deriving) a group (plurality) 326 of access points (base stations) using an unsupervised learning technique (deriving health scores) )
deriving one or more cell health indices including deriving a cell health index for each of the one or more similarly situated cellular base stations from corresponding counters; ( Chen [0002] detecting anomalies based on access-point performance indicators )
deriving the one or more previously derived performance scores from the one or more cell performance indices and the one or more cell health indices; and storing the one or more previously derived cellular base station performance scores in a database. ( Chen [0056] processor 316 may store values (database scores) of the one or more performance metrics (derived performance) 318 )
Consider claim 13. Chen and Tan suggest all the limitations of claim 12, Chen also suggests wherein accessing one or more previously derived performance scores comprises accessing the one or more previously derived performance scores from the database. ( Chen [0099] anomaly scores stored by the data module may be intermediate calculation results of previous instances of anomaly detection )
Consider claim 17. While Chen and Tan suggest all the limitations of claim 11, Chen also suggests wherein identifying the one or more similarly situated cellular base stations comprises determining the one or more similarly situated cellular base stations are located on similar terrain. ( Chen [0011] the group of access points may include access points that: have environments with a common characteristic, are geographically proximate to each other, i.e., similar terrain )
Claim 6 is rejected under 35 U.S.C. § 103 as unpatentable over Chen et al. ( US 20180220314 A1 hereafter Chen (Reference in IDS)) in view of Eleftheriadis et al. ( US 20220400394 A1 hereafter Eleftheriadis) in view of Admin “Tutorial Example: Understanding Cosine Similarity” hereafter Admin (Reference in IDS).
Consider claim 6. Chen discloses all the limitations of claim 1.
However Chen fails to disclose accessing a plurality of vectorized scores; and using a cosine similarity function.
While in a similar field of endeavor, Eleftheriadis suggests accessing a plurality of vectorized scores; ( Eleftheriadis [0112] In some example aspects, the machine-learning algorithm 50 may be a supervised learning algorithm (such as a neural network, a convolutional neural network, a support vector machine or an evolutionary algorithm, for example). )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated using vector scores of Eleftheriadis into the invention of Chen to improve cellular network performance.
However Chen and Eleftheriadis fails to explicitly disclose using a cosine similarity function.
While in a similar field of endeavor, Admin suggests using a cosine similarity function ( Admin See page 2 equation.)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the suggestions of Admin to use a cosine similarity function for calculating performance scores with Eleftheriadis to improve cellular network performance of the method taught by Chen.
Claims 14, 16 and 20 are rejected under 35 U.S.C. § 103 as unpatentable over Chen et al. ( US 20180220314 A1 hereafter Chen (Reference in IDS)) in view of Tan et al. ( US 20160162783 A1 hereafter Tan (Reference in IDS)) in view of Eleftheriadis et al. ( US 20220400394 A1 hereafter Eleftheriadis).
Consider claim 14. Chen and Tan suggest all the limitations of claim 12.
However Chen and Tan fail to explicitly disclose accessing KPIs for each one or more similarly situated cellular base stations comprises accessing KPIs corresponding to one or more of: accessibility, retainability, integrity, availability, and mobility of a cellular network
While in a similar field of endeavor Eleftheriadis also suggests wherein accessing KPIs for each one or more similarly situated cellular base stations comprises accessing KPIs corresponding to one or more of: accessibility, retainability, integrity, availability, and mobility of a cellular network. ( Eleftheriadis [0061] KPIs may be reflective of any one of the following aspects how the network as a whole or a single node (a cell and its associated radio base station) is performing: accessibility, retainability, integrity, availability, mobility and energy efficiency.)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated Eleftheriadis accessing KPIs corresponding to specific characteristics in order to improve Chen and Tan cellular network performance.
Consider claim 16. Chen and Tan suggest all the limitation of claim 11.
However Chen and Tan fail to explicitly disclose accessing a plurality of vectorized scores
While in a similar field of endeavor Eleftheriadis also suggests accessing a plurality of vectorized scores. ( Eleftheriadis [0112] In some example aspects, the machine-learning algorithm 50 may be a supervised learning algorithm (such as a neural network, a convolutional neural network, a support vector machine or an evolutionary algorithm, for example). ).
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated vectorized scores with AI and other algorithmic solutions suggested by Eleftheriadis to improve cellular network performance of the method taught by Chen and Tan.
Consider claim 20. Chen and Tan suggest all the limitations of claim 11.
However Chen and Tan fail to disclose wherein the cellular network is a 5G network.
While in a similar field of endeavor, Eleftheriadis suggests wherein the cellular network is a 5G network. ( Eleftheriadis [0002] 5th Generation, 5G ).
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the 5G of Eleftheriadis into the invention of Chen and Tan to improve cellular network performance.
Claims 15 and 18-19 are rejected under 35 U.S.C. § 103 as unpatentable over Chen et al. ( US 20180220314 A1 hereafter Chen (Reference in IDS)) in view of Tan et al ( US 20160162783 A1 hereafter Tan (Reference in IDS) in view of Tarraf et al ( US 20160286411 A1 hereafter Tarraf (Reference in IDS).
Consider claim 15. Chen and Tan suggest all the limitation of claim 12.
However Chen and Tan fail to explicitly teach access counters in one or more categories selected from among: access failures, access rejections, usage, throughput, transport, and radio environment.
While in a similar field of endeavor, Tarraf suggests access counters in one or more categories selected from among: access failures, access rejections, usage, throughput, transport, and radio environment ( Tarraf [0105] Number Of Blocked Calls (access rejections), [0104] Number Of Dropped And Lost Calls (failures) [0099] Total CE Usage (usage) [0087] UL and DL Stats For Each Sector/Carrier: Load, Erlangs (radio environment) and Throughput )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the network access counters of Tarraf with the invention taught by Chen and Tan to improve cellular network performance.
Consider claim 18. Chen and Tan suggest all the limitations of claim 11.
However Chen and Tan fail to explicitly disclose similarly situated cellular base stations are positioned at similar heights.
While in a similar field of endeavor, Tarraf suggests similarly situated cellular base stations being positioned at similar heights. ( Tarraf [0086] In step 401, the neighbor cells/sectors are determined based on the cells list table in the database 110. In step 402, the neighbor list is sorted by network statistics. As noted above, network statistics may include, but are not limited to, key performance Indicators (KPIs). [0117] Antenna Height Above Ground And Sea Level )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the network parameters suggested by Tarraf with to improve cellular network performance of the invention taught by Chen and Tan.
Consider claim 19. Chen and Tan suggest all the limitations of claim 11.
However Chen and Tan fail to explicitly disclose determining the one or more similarly situated cellular base station have similar tilts.
While in a similar field of endeavor, Tarraf suggests determining the one or more similarly situated cellular base station have similar tilts. ( Tarraf [0086] In step 401, the neighbor cells/sectors are determined based on the cells list table in the database 110. In step 402, the neighbor list is sorted by network statistics. As noted above, network statistics may include, but are not limited to, key performance Indicators (KPIs). and [0118] Antenna Model, Azimuth BW, Elevation BW, Gain, Electrical And Mechanical Tilt )
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the network parameters suggested by Tarraf with to improve cellular network performance of the invention taught by Chen and Tan.
Conclusion
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/FRANKLIN HAYES CASTLE/Examiner, Art Unit 2647
/Alison Slater/Supervisory Patent Examiner, Art Unit 2647