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 1-20 are pending.
Response to Arguments
Arguments presented on 03/27/2026 is/are considered but they are moot in view of a new ground of rejection to address the new limitations to which the arguments are directed to.
The ODP rejections of record are now withdrawn in view of the e-Terminal Disclaimers of 03/27/2026
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: operational engine, customer optimization module, reasoner, brokerage application, data analysis engine, which appear in many instances across claims 1-20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stanforth et al. (US 2008/0222021A1) in view of Dzierwa (US 2022/0128612) and in further view of Montalvo (US 2021/0345120).
As to claim 1:
Stanforth discloses:
A system for spectrum management in an electromagnetic environment (Abstract, [0009, system for brokering spectrum and policing brokered spectrum for wireless communication environment) comprising:
at least one monitoring sensor operable to create measured data for at least one signal from the electromagnetic environment; (…) and a data analysis engine, (…) wherein the at least one monitoring sensor is operable to transmit the measured data to the data analysis engine (¶0021, 0147, 0181, a plurality of deployed sensors for wireless measurements which are sent to analysis ); an operational engine; (Fig. 10, read as Spectrum Holder system, as well as expanded block diagram of the spectrum holder system in Fig. 12)
wherein the operational engine is operable to determine optimization objectives based on the measured data; (See [0181, using measurement data collected by sensors, the objectives for optimization are created, "Spectrum offers and/or spectrum commodity items that are generated by the spectrum analysis engine 172 may be forwarded to the management application 168 for further processing and forwarding to the broker interface application 164. The predictive engine of the spectrum availability algorithms 178 may analyze historical usage data, historical spectrum constraints, historical bandwidth availability, and information about known future spectrum usage, for example, to generate a prediction of spectrum that may be available in the future for use by user systems 102")
wherein the optimization objectives include constraints associated with signal characteristics; (¶0096, 0099, requirements comprises spectral mask (i.e. frequency), power, and protocol as part Application/Control Number: 19/061,431 Page 10 Art Unit: 2645 of decision making process and in the certificate itself. These requirements are constraints related to signal characteristics as they limit intensity, frequency and context of transmission and how the system optimizes. 90071, factors including bandwidth, power, interference and price as part of matching/optimization criteria. 0109, a service request include a list of demands for system such as transmit power, protocol information, frequency)
wherein the system is operable to assign the at least one signal a priority based on at least one customer application associated with the at least one signal; wherein the priority is used in dynamic allocation of spectrum in the electromagnetic environment for the at least one signal; ( Stanforth discloses in 0084, 0100, "the broker 14 also may have an additional capability, which is described as an exception handler to allow a higher priority user to preempt a lower priority user. When a certificate is to be preempted by a higher priority user, such as a public safety user or the spectrum holder, the exception handler of the broker 14 may be configured to rescind the certificate" i.e. users accessing the network are assigned priorities, some are higher than others. The certificate (i.e. assignment of resource allocation) might be dynamically revoked if the user is preempted by a higher priority).)
wherein the operational engine utilizes at least one prediction model to forecast future spectrum usage based on historical data and the measured data; (0181, predictive engine of the spectrum availability algorithm 178 to analyze historical usage data and measurements collected to predict spectrum availability/occupied in future)
wherein the operational engine utilizes the at least one prediction model to perform interference source modeling to forecast at least one future probability of a conflicting event; (¶0011—013, ¶0051, 0155, system to detect potential conflicts by identification of a spectrum user system that exceeds an authorized use of spectrum, the authorized use corresponding to the time window". occupation (i.e. usage) of spectrum are defined in time windows and strictly enforced, i.e. "scheduling" as to assign something a time slot and to enforce its compliance. ¶0051: "each segment of spectrum for which access permission may be transferred may be identified by several components and each component is defined by one or more variables. Exemplary components include a time window, a frequency-based spectral mask, a geography-based emission mask and a transmitted power limit. The time window may be a period of time that has a starting point given by a day and time and an ending point given by a day and time. Alternatively, the time window may be a period of time specified by a starting time and a duration". See also 0155, 0148-0149)
Regarding:
wherein the at least one prediction model includes descriptive analytics,; (¶0056-57, descriptions of characteristics of blocks of spectrums are such as time window, frequency mask, power limit, which are in turn analyzed for prediction of future usage, i.e. performing descriptive analytics, where descriptions of spectrums are identified and analyzed, See also ¶012, 0021) diagnostic analytics, (¶0012, 0022, 0167, 0226, data collected by sensors and usage data collected are analyzed to diagnose violations, i.e. cause of violation such as actual use exceeded authorization, also performing root-cause analysis of interference) predictive analytics, (See at least 0057, 0182, 0170, predictive of future spectrum usage based on past usage data) and prescriptive analytics (¶0230, upon a violation occurs, a corrective actions is prescribed and issued to correct said violation, such as reducing power level to a regulatory level)
Stanforth is silent on:
wherein the data analysis engine is operable to generate at least one mask based on the measured data, wherein the at least one mask is generated by averaging maximum power values of a spectrum of signals over time; and wherein the data analysis engine is operable to analyze the measured data from the electromagnetic environment using the at least one mask to identify at least one unwanted signal.
Dzierwa, in a related field of RF environment monitoring for interference, discloses a system/method for detecting interference signals, wherein the system is configured for identifying an interference signal that causes an interference alarm. Specifically, a mask is created by averaging signal powers over a period of time (¶0330, 0333). The mask is then used to compare against signals received over a period of time (¶0334-0337). The system will flag any signal that violates the mask’s conditions which causes the alarms, thereby identifying the signals that are unwanted (i.e. in violation of rules).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention to incorporate the mask-based interference detection using average signal strengths of Dzierwa to the system of Stanforth. In light of Stanforth’s expressing the desire to determine interference and noises (¶168 of Stanforth), it is beneficial to implement the above feature of Dzierwa as it directly addresses Stanforth’s concern. Furthermore, several improvements of Dzierwa are directly discussed in ¶0301, which include at least better capturing of RF events, time, measurements, interference detection)
None of Stanforth and Dzierwa disclose a statistical inference engine utilizes control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment.
Montalvo, in a related field of endeavor (See abstract), discloses such a predictive/diagnostic system for RF environment wherein per ¶0132-0133, wherein the learning model of the system includes a statistical inference and machine learning (ML) engine utilizes statistical learning techniques and/or control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system/method of Stanforth and Dzierwa to include the . Such an implementation advantageously allows for accurate estimate of future network condition, thus helps to prioritize and manage applications in the wireless communications spectrum, while also optimizing application performance (¶0024 of Montalvo)
As to claim 2:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 1, further comprising a reasoner operable to determine conditional constraints to be used by an optimizer for optimization of the at least one signal based on a policy for use of a resource. (See Stanforth, at least ¶085, condition and constraints based on spectrum governing policies)
As to claim 3:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 2, further comprising a resource brokerage application operable to control the use of the resource. ( Stanforth ¶0103, spectrum broker 14 controlling use of spectrum allocation)
As to claim 4:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 2, wherein the policy for the use of the resource is used for the dynamic allocation of spectrum for at least one frequency band in the electromagnetic environment. ( Stanforth,¶0213, channel/spectrum allocations are dynamic and may be re-allocated as appropriate)
As to claim 5:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 2, wherein the resource includes a portion of at least one frequency band in a spectrum in the electromagnetic environment. (See Stanforth, at least ¶02090-0211, one or more bands of frequency)
As to claim 6:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 1, wherein the data analysis engine is operable to identify an impact of interference on customer goals and/or customer operations. ( Stanforth, ¶0065, 0070, 0112, evaluation of interference and how much it will spread)
As to claim 7:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 1, wherein the operational engine includes a policy manager, wherein the policy manager is operable to create at least one rule and/or at least one policy. ( Stanforth Fig. 12, Spectrum rule function 174)
As to claim 8:
Stanforth in view of Dzierwa and Montalvo discloses all limitations of claim 1, wherein the operational engine is operable to send actionable data for optimization of the at least one signal to at least one semantic engine. ( Stanforth ¶0100-0102, sending spectrum offer/commodity to be accepted or denied)
Claim(s) 9-20 /are rejected under 35 U.S.C. 103 as being unpatentable over Stanforth et al. (US 2008/0222021A1) in view of Dzierwa (US 2022/0128612) and in further view of Kakirwar et al. (US 2019/0027134) and in further view of Montalvo (US 2021/0345120).
As to claim 9:
Stanforth discloses:
A system for spectrum management in an electromagnetic environment (Abstract, ¶0009, system for brokering spectrum and policing brokered spectrum for wireless communication environment) comprising:
at least one data analysis engine for analyzing measured data from the electromagnetic environment to provide information about the electromagnetic environment for at least one signal; (¶0021, 0147, analysis of measurements obtained from a plurality of deployed sensors for wireless measurements)
a certification and compliance application (Fig. 4, read as certificate agent 20); and an operational engine; (Fig. 10, read as Spectrum Holder system, as well as expanded block diagram of the spectrum holder system in Fig. 12)
wherein the operational engine is operable to create actionable data based on the information about the electromagnetic environment; (¶0100-0102, sending spectrum offer/commodity to be accepted or denied);
wherein the at least one signal includes a priority based on at least one customer application;
wherein the priority and the actionable data are used in dynamic allocation of spectrum in the electromagnetic environment for the at least one signal; ( Stanforth discloses in ¶0084, 0100, “the broker 14 also may have an additional capability, which is described as an exception handler to allow a higher priority user to preempt a lower priority user. When a certificate is to be preempted by a higher priority user, such as a public safety user or the spectrum holder, the exception handler of the broker 14 may be configured to rescind the certificate” i.e. users accessing the network are assigned priorities, some are higher than others. The certificate (i.e. assignment of resource allocation) might be dynamically revoked if the user is preempted by a higher priority).)
wherein the certification and compliance application is operable to determine if the at least one customer application and/or at least one customer device is behaving according to at least one rule and/or at least one policy; (¶0059, 0079, certificate agent 20 to ensure radios complies with rules/policies, and further to police the spectrum usage to ensure compliance. Spectrum certificate forces radio devices to operate in accordance with spectrum policies/rules under the certificate (rules pertaining time, frequency, power limit))
wherein the operational engine utilizes at least one prediction model to forecast future spectrum usage based on historical data and the measured data; (¶0181, predictive engine of the spectrum availability algorithm 178 to analyze historical usage data and measurements collected to predict spectrum availability/occupied in future);
wherein the operational engine is operable to send the actionable data to at least one semantic engine; ( Stanforth ¶0100-0102, sending spectrum offer/commodity to be accepted or denied)
wherein the at least one semantic engine is operable to establish at least one system rule and/or at least one system policy based on the actionable data ( Stanforth, ¶0177, 0085, rules established by spectrum holder system) and at least one natural language query;
Stanforth is silent on:
wherein the at least one data analysis engine is operable to generate at least one mask based on the measured data from the electromagnetic environment, wherein the at least one mask is generated by averaging maximum power values of a spectrum of signals over time;
and wherein the data analysis engine is operable to analyze the measured data from the electromagnetic environment using the at least one mask to identify at least one unwanted signal.
Dzierwa, in a related field of RF environment monitoring for interference, discloses a system/method for detecting interference signals, wherein the system is configured for identifying an interference signal that causes an interference alarm. Specifically, a mask is created by averaging signal powers over a period of time (¶0330, 0333). The mask is then used to compare against signals received over a period of time (¶0334-0337). The system will flag any signal that violates the mask’s conditions which causes the alarms, thereby identifying the signals that are unwanted (i.e. in violation of rules).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention to incorporate the mask-based interference detection using average signal strengths of Dzierwa to the system of Stanforth. In light of Stanforth’s expressing the desire to determine interference and noises (¶168 of Stanforth), it is beneficial to implement the above feature of Dzierwa as it directly addresses Stanforth’s concern. Furthermore, several improvements of Dzierwa are directly discussed in ¶0301, which include at least better capturing of RF events, time, measurements, interference detection)
Neither Stanforth nor Dzierwa discloses the establishing rules/policy can be also by virtue of and at least one natural language query;
Kakirwar, in a related field of natural query analyses for creating policy, discloses in at least Abstract, 0037-0043, a system/method that generate a set of rules and/or policies for any topics based in semantic analysis of user inputs that include natural language command.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Stanforth and others to incorporate the concept of interpreting natural language command to create a system rules/policy. This incorporation advantageously integrate the growing AI field into the existing system, allowing improved ease and productivity for system management, for example skipping manual coding of such rules all together.
None of Stanforth and others in the combination disclose a statistical inference engine utilizes control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment.
Montalvo, in a related field of endeavor (See abstract), discloses such a predictive/diagnostic system for RF environment wherein per ¶0132-0133, wherein the learning model of the system includes a statistical inference and machine learning (ML) engine utilizes statistical learning techniques and/or control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system/method of Stanforth to include the . Such an implementation advantageously allows for accurate estimate of future network condition, thus helps to prioritize and manage applications in the wireless communications spectrum, while also optimizing application performance (¶0024 of Montalvo)
As to claim 10:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 9, further comprising a reasoner operable to determine constraints to be used by an optimizer for optimization of spectrum based on the at least one policy or the at least one rule for usage of a resource. (See Stanforth at least ¶085, condition and constraints based on spectrum governing policies)
As to claim 11:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 10, further comprising a resource brokerage application operable to control usage of the resource based on the at least one policy or the at least one rule for usage of the resource. ( Stanforth ¶0103, spectrum broker 14 controlling use of spectrum allocation based on rules/policy)
As to claim 12:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 9, further comprising at least one monitoring sensor operable to create the measured data from the electromagnetic environment. ( Stanforth ¶0021, 0147, a plurality of deployed sensors for wireless measurements)
As to claim 13:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 9, wherein the operational engine includes a policy manager, wherein the policy manager is operable to create the at least one rule and/or the at least one policy. ( Stanforth, ¶0177, 0085, rules established by spectrum holder system)
As to claim 14:
Stanforth discloses:
A method for spectrum management in an electromagnetic environment (Abstract, ¶0009, system for brokering spectrum and policing brokered spectrum for wireless communication environment) comprising:
an operational engine (Fig. 10, read as Spectrum Holder system, as well as expanded block diagram of the spectrum holder system in Fig. 12) creating optimization objectives and sending the optimization objectives to a customer optimization module;
(See ¶0181, using measurement data collected by sensors, the objectives for optimization are created, “Spectrum offers and/or spectrum commodity items that are generated by the spectrum analysis engine 172 may be forwarded to the management application 168 for further processing and forwarding to the broker interface application 164. . . . The predictive engine of the spectrum availability algorithms 178 may analyze historical usage data, historical spectrum constraints, historical bandwidth availability, and information about known future spectrum usage, for example, to generate a prediction of spectrum that may be available in the future for use by user systems 102”) based on measured data for at least one signal from the electromagnetic environment (¶0021, 0147, a plurality of deployed sensors for wireless measurements)
the customer optimization module creating actionable data based on the optimization objectives; (¶0100-0102, sending spectrum offer/commodity to be accepted or denied, ¶0148-0149, collecting data of spectral occupancy in bands of interest, based on which to optimize spectrum use)
sending the actionable data to a semantic engine; (¶0100-0102, sending spectrum offer/commodity to be accepted or denied);
the semantic engine establishing at least one system rule and/or at least one system policy based on the actionable data (¶0177, 0085, rules established by spectrum holder system)
the operational engine creating and using at least one prediction model to forecast future spectrum usage based on historical data and the measured data; ( Stanforth ¶0181, predictive engine of the spectrum availability algorithm 178 to analyze historical usage data and measurements collected to predict spectrum availability/occupied in future).
Stanforth is silent on:
a data analysis engine generating at least one mask based on the measured data from the electromagnetic environment, wherein the at least one mask is generated by averaging maximum power values of a spectrum of signals over time; and the data analysis engine analyzing the measured data from the electromagnetic environment using the at least one mask to identify at least one unwanted signal.
Dzierwa, in a related field of RF environment monitoring for interference, discloses a system/method for detecting interference signals, wherein the system is configured for identifying an interference signal that causes an interference alarm. Specifically, a mask is created by averaging signal powers over a period of time (¶0330, 0333). The mask is then used to compare against signals received over a period of time (¶0334-0337). The system will flag any signal that violates the mask’s conditions which causes the alarms, thereby identifying the signals that are unwanted (i.e. in violation of rules).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention to incorporate the mask-based interference detection using average signal strengths of Dzierwa to the system of Stanforth. In light of Stanforth’s expressing the desire to determine interference and noises (¶168 of Stanforth), it is beneficial to implement the above feature of Dzierwa as it directly addresses Stanforth’s concern. Furthermore, several improvements of Dzierwa are directly discussed in ¶0301, which include at least better capturing of RF events, time, measurements, interference detection)
Neither Stanforth nor Dzierwa discloses the establishing rules/policy can be also by virtue of and at least one natural language query;
Kakirwar, in a related field of natural query analyses for creating policy, discloses in at least Abstract, 0037-0043, a system/method that generate a set of rules and/or policies for any topics based in semantic analysis of user inputs that include natural language command.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Stanforth and Dzierwa to incorporate the concept of interpreting natural language command to create a system rules/policy. This incorporation advantageously integrate the growing AI field into the existing system, allowing improved ease and productivity for system management, for example skipping manual coding of such rules all together.
None of Stanforth and others in the combination disclose a statistical inference engine utilizes control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment.
Montalvo, in a related field of endeavor (See abstract), discloses such a predictive/diagnostic system for RF environment wherein per ¶0132-0133, wherein the learning model of the system includes a statistical inference and machine learning (ML) engine utilizes statistical learning techniques and/or control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system/method of Stanforth to include the . Such an implementation advantageously allows for accurate estimate of future network condition, thus helps to prioritize and manage applications in the wireless communications spectrum, while also optimizing application performance (¶0024 of Montalvo)
As to claim 15:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 14, further comprising a certification and compliance application determining if at least one customer application and at least one customer device are behaving according to a rule and/or a policy.
(Stanforth ¶0059, 0079, certificate agent 20 to ensure radios complies with rules/policies, and further to police the spectrum usage to ensure compliance. Spectrum certificate forces radio devices to operate in accordance with spectrum policies/rules under the certificate (rules pertaining time, frequency, power limit))
As to claim 16:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 14, further comprising the data analysis engine analyzing the measured data to provide information associated with the at least one signal and/or the electromagnetic environment. ( Stanforth, ¶0021, 0147, analysis of measurements obtained from a plurality of deployed sensors for wireless measurements);
As to claim 17:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 14, wherein the at least one signal includes a priority based on at least one customer application( Stanforth discloses in ¶0084, 0100, “the broker 14 also may have an additional capability, which is described as an exception handler to allow a higher priority user to preempt a lower priority user. When a certificate is to be preempted by a higher priority user, such as a public safety user or the spectrum holder, the exception handler of the broker 14 may be configured to rescind the certificate” i.e. users accessing the network are assigned priorities, some are higher than others. The certificate (i.e. assignment of resource allocation) might be dynamically revoked if the user is preempted by a higher priority).)
As to claim 18:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 17, wherein the priority and/or the actionable data is used in dynamic allocation of spectrum in the electromagnetic environment for the at least one signal. (Stanforth discloses in ¶0084, 0100, “the broker 14 also may have an additional capability, which is described as an exception handler to allow a higher priority user to preempt a lower priority user. When a certificate is to be preempted by a higher priority user, such as a public safety user or the spectrum holder, the exception handler of the broker 14 may be configured to rescind the certificate” i.e. users accessing the network are assigned priorities, some are higher than others. The certificate (i.e. assignment of resource allocation) might be dynamically revoked if the user is preempted by a higher priority).)
As to claim 19:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 14, further comprising preprocessing the occupancy of the at least one signal and at least one second signal that exist in a frequency band based on interference between the at least one signal and the at least one second signal. ( Stanforth, ¶0065, 0070, 0112, evaluation of interference and how much it will spread)
As to claim 20:
Stanforth in view of Dzierwa and Montalvo and Kakirwar discloses all limitations of claim 14, further comprising a reasoner determining conditional constraints to be used by an optimizer for optimization of the at least one signal based on a policy for use of a resource. (See Stanforth, at least ¶085, condition and constraints based on spectrum governing policies)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2002/0078174 - Large payload files are selectively partitioned in blocks and the blocks distributed to a plurality of distribution stations at the edge of the network qualified to have the data. Each qualified station decides how much and what portion of the content to save locally, based on information such as network location and environment, usage, popularity, and other distribution criteria defined by the content provider. Different pieces of a large payload file may be available from different nodes, however, when a user requests access to the large payload file, for example, through an application server, a virtual file control system creates an illusion that the entire file is present at the connected node. However, since only selective portions of the large payload file may actually be resident at that node's storage at the time of request, a cluster of distribution servers at the distribution station may download the non-resident portions of the file as the application server is servicing the user.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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
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