DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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:
“at least one survey occupancy application is operable to determine occupancy in the at least one frequency band” in claim 1 which is interpreted as being implemented as electronic hardware or combination of software with electronic hardware as described in paragraph [00299] of the instant application.
“a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy” in claim 5 which is interpreted as being implemented as electronic hardware or combination of software with electronic hardware as described in paragraph [00299] of the instant application.
“a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques” in claim 7 which is interpreted as being implemented as electronic hardware or combination of software with electronic hardware as described in paragraph [00299] of the instant application.
“one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data” in claim 9 which is interpreted as being implemented as electronic hardware or combination of software with electronic hardware as described in paragraph [00299] of the instant application.
“t least one survey occupancy application is operable to use at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; andwherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band” in claim 9 which is interpreted as being implemented as electronic hardware or combination of software with electronic hardware as described in paragraph [00299] of the instant application.
“wherein the system further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy” in claim 12 which is interpreted as being implemented as electronic hardware or combination of software with electronic hardware as described in paragraph [00299] of the instant application.
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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5-6, 14, 16, and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2 and 9 of U.S. Patent No. 12,282,300. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-3, 5-6, 14, 16, and 17 of the instant application number 19/317,753
Claims 1-2 and 9 of U.S. Pat. No. 12,282,300
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is related to at least one customer application; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 9:
The system of claim 1, wherein the at least one application further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application is behaving according to the at least one rule and/or the at least one policy.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is related to at least one customer application; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is related to at least one customer application; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is related to at least one customer application; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 9:
The system of claim 1, wherein the at least one application further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application is behaving according to the at least one rule and/or the at least one policy.
Claims 1, 2, 4, 6, 14, and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of U.S. Patent No.12,279,126. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1, 2, 4, 6, 14, and 16 of the instant application number 19/317,753
Claims 1 and 11 of U.S. Patent No. 12,279,126
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; a programmable rules and policy editor; at least one server; and at least one resource brokerage application operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one data analysis engine is in communication with the at least one server; wherein the programmable rules and policy editor is in communication with the at least one server; wherein the at least one resource brokerage application is in communication with the at least one server; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein each of the at least one customer application and/or the at least one customer device is assigned a priority; and wherein the at least one server is operable to allocate at least one frequency band in the electromagnetic spectrum based on the priority, one or more of the at least one rule and/or the at least one policy, and/or the measured data.
Claim 11:
wherein the survey occupancy application is operable to preprocess at least two signals that exist in the same band based on interference between the at least two signals.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 1:
A system for prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; a programmable rules and policy editor; at least one server; and at least one resource brokerage application operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one data analysis engine is in communication with the at least one server; wherein the programmable rules and policy editor is in communication with the at least one server; wherein the at least one resource brokerage application is in communication with the at least one server; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein each of the at least one customer application and/or the at least one customer device is assigned a priority; and wherein the at least one server is operable to allocate at least one frequency band in the electromagnetic spectrum based on the priority, one or more of the at least one rule and/or the at least one policy, and/or the measured data.
Claim 11:
wherein the survey occupancy application is operable to preprocess at least two signals that exist in the same band based on interference between the at least two signals.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; a programmable rules and policy editor; at least one server; and at least one resource brokerage application operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one data analysis engine is in communication with the at least one server; wherein the programmable rules and policy editor is in communication with the at least one server; wherein the at least one resource brokerage application is in communication with the at least one server; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein each of the at least one customer application and/or the at least one customer device is assigned a priority; and wherein the at least one server is operable to allocate at least one frequency band in the electromagnetic spectrum based on the priority, one or more of the at least one rule and/or the at least one policy, and/or the measured data.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 1:
A system for prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; a programmable rules and policy editor; at least one server; and at least one resource brokerage application operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one data analysis engine is in communication with the at least one server; wherein the programmable rules and policy editor is in communication with the at least one server; wherein the at least one resource brokerage application is in communication with the at least one server; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein each of the at least one customer application and/or the at least one customer device is assigned a priority; and wherein the at least one server is operable to allocate at least one frequency band in the electromagnetic spectrum based on the priority, one or more of the at least one rule and/or the at least one policy, and/or the measured data.
Claim 11:
wherein the survey occupancy application is operable to preprocess at least two signals that exist in the same band based on interference between the at least two signals..
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application
Claim 1:
A system for prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; a programmable rules and policy editor; at least one server; and at least one resource brokerage application operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one data analysis engine is in communication with the at least one server; wherein the programmable rules and policy editor is in communication with the at least one server; wherein the at least one resource brokerage application is in communication with the at least one server; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein each of the at least one customer application and/or the at least one customer device is assigned a priority; and wherein the at least one server is operable to allocate at least one frequency band in the electromagnetic spectrum based on the priority, one or more of the at least one rule and/or the at least one policy, and/or the measured data.
Claim 11:
wherein the survey occupancy application is operable to preprocess at least two signals that exist in the same band based on interference between the at least two signals.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; a programmable rules and policy editor; at least one server; and at least one resource brokerage application operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one data analysis engine is in communication with the at least one server; wherein the programmable rules and policy editor is in communication with the at least one server; wherein the at least one resource brokerage application is in communication with the at least one server; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein each of the at least one customer application and/or the at least one customer device is assigned a priority; and wherein the at least one server is operable to allocate at least one frequency band in the electromagnetic spectrum based on the priority, one or more of the at least one rule and/or the at least one policy, and/or the measured data.
Claim 11:
wherein the survey occupancy application is operable to preprocess at least two signals that exist in the same band based on interference between the at least two signals.
Claims 1-6, 14, and 16-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, and 10 of U.S. Patent No. 11,190,946. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-6, 14, and 16-17 of the instant application number 19/317,753
Claims 1, 2, and 10 of U.S. Patent No. 11,190,946
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 2:
wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 10:
The system of claim 1, wherein the at least one application further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application and/or the at least one customer device is behaving according to the at least one rule and/or the at least one policy.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment; at least one data analysis engine for analyzing the measured data; at least one application; a semantic engine including a programmable rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the priority and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 10:
The system of claim 1, wherein the at least one application further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application and/or the at least one customer device is behaving according to the at least one rule and/or the at least one policy.
Claims 1-7, 14, 16-18, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4-6, 14, and 17-20 of U.S. Patent No. 11,985,510. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-7, 14, 16-18, and 20 of the instant application number 19/317,753
Claims 1-2, 4-6, 14, and 17-20 of U.S. Patent No. 11,985,510
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data; at least one application; an operational engine including a programmable rules and policy editor; and at least one server; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the at least one server is in communication with the at least one data analysis engine and the application; wherein the application is in communication with the operational engine; wherein the at least one server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the survey occupancy application is operable to use the priority, the one or more of the at least one rule and/or the at least one policy, and the actionable data to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 2:
wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data; at least one application; an operational engine including a programmable rules and policy editor; and at least one server; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; wherein the at least one server is in communication with the at least one data analysis engine and the application; wherein the application is in communication with the operational engine; wherein the at least one server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the survey occupancy application is operable to use the priority, the one or more of the at least one rule and/or the at least one policy, and the actionable data to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if 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.
Claim 6:
The system of claim 1, wherein the at least one application further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application and/or the at least one customer device is behaving according to the at least one rule and/or the at least one policy.
Claim 6:
The system of claim 1, 6. The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 4:
The system of claim 1, wherein the survey occupancy application is operable to determine occupancy in frequency bands.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 5:
The system of claim 1, further comprising a learning engine operable to learn the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: providing an operational engine including a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; monitoring the electromagnetic environment using at least one monitoring sensor, thereby creating measured data; analyzing the measured data using at least one data analysis engine, thereby creating analyzed data; determining occupancy in frequency bands and scheduling occupancy in at least one frequency band using a survey occupancy application; optimizing resources to improve performance of at least one customer application and/or at least one customer device using a resource brokerage application; assigning a priority to each of the at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority and the one or more of the at least one rule and/or the at least one policy.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: providing an operational engine including a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; monitoring the electromagnetic environment using at least one monitoring sensor, thereby creating measured data; analyzing the measured data using at least one data analysis engine, thereby creating analyzed data; determining occupancy in frequency bands and scheduling occupancy in at least one frequency band using a survey occupancy application; optimizing resources to improve performance of at least one customer application and/or at least one customer device using a resource brokerage application; assigning a priority to each of the at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority and the one or more of the at least one rule and/or the at least one policy.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 17:
The method of claim 14, further including a certification and compliance application determining if the at least one customer application and/or the at least one customer device is behaving according to the at least one rule and/or the at least one policy.
Claim 18:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference
Claims 1-8, 14, 16-18, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4-6, 14, and 17 and 20 of U.S. Patent No. 12,133,083. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-8, 14, 16-18, and 20 of the instant application number 19/317,753
Claims 1-2, 4-6, 14, and 17 and 20 of U.S. Patent No. 12,133,083
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data; at least one application; and at least one server; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one server is in communication with the at least one data analysis engine and the application; wherein the at least one server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the survey occupancy application is operable to use the priority and the actionable data to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data; at least one application; and at least one server; wherein the at least one application includes a survey occupancy application and a resource brokerage application, wherein the survey occupancy application is operable to schedule occupancy in at least one frequency band, and wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device; wherein the at least one server is in communication with the at least one data analysis engine and the application; wherein the at least one server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein each of the at least one customer application is assigned a priority; and wherein the survey occupancy application is operable to use the priority and the actionable data to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 5:
The system of claim 1, further comprising a certification and compliance application,wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 6:
The system of claim 1, wherein the at least one application further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application and/or the at least one customer device is behaving according to the at least one rule and/or the at least one policy.
Claim 6:
6. The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 4:
The system of claim 1, wherein the survey occupancy application is operable to determine occupancy in frequency bands.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 5:
The system of claim 1, further comprising a learning engine operable to learn the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 8:
The system of claim 1, wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 4:
The system of claim 1, wherein the survey occupancy application is operable to determine occupancy in frequency bands.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one monitoring sensor, thereby creating measured data; analyzing the measured data using at least one data analysis engine, thereby creating analyzed data; determining occupancy in frequency bands and scheduling occupancy in at least one frequency band using a survey occupancy application; optimizing resources to improve performance of at least one customer application and/or at least one customer device using a resource brokerage application; assigning a priority to each of the at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one monitoring sensor, thereby creating measured data; analyzing the measured data using at least one data analysis engine, thereby creating analyzed data; determining occupancy in frequency bands and scheduling occupancy in at least one frequency band using a survey occupancy application; optimizing resources to improve performance of at least one customer application and/or at least one customer device using a resource brokerage application; assigning a priority to each of the at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 17:
The method of claim 14, further including a certification and compliance application determining if the at least one customer application and/or the at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 18:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claims 1, 4, 6, 14, and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,096,226. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1, 4, 6, 14, and 16 of the instant application number 19/317,753
Claim 1 of U.S. Patent No. 12,096,226
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic spectrum management in an electromagnetic environment comprising: at least one monitoring sensor operable to create measured data based on the electromagnetic environment; at least one data analysis engine; a survey occupancy application; a rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine is in communication with the tip and cue server; wherein the tip and cue server is in communication with the survey occupancy application and the rules and policy editor; wherein the at least one data analysis engine is operable to perform interference source modeling or identify possible signals of interference for at least one customer device based on the measured data; wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy for the at least one customer device in at least one frequency band; wherein the rules and policy editor includes at least one rule and/or at least one policy; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data for spectrum allocation; and wherein the actionable data and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer device.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic spectrum management in an electromagnetic environment comprising: at least one monitoring sensor operable to create measured data based on the electromagnetic environment; at least one data analysis engine; a survey occupancy application; a rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine is in communication with the tip and cue server; wherein the tip and cue server is in communication with the survey occupancy application and the rules and policy editor; wherein the at least one data analysis engine is operable to perform interference source modeling or identify possible signals of interference for at least one customer device based on the measured data; wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy for the at least one customer device in at least one frequency band; wherein the rules and policy editor includes at least one rule and/or at least one policy; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data for spectrum allocation; and wherein the actionable data and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer device.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 1:
A system for dynamic spectrum management in an electromagnetic environment comprising: at least one monitoring sensor operable to create measured data based on the electromagnetic environment; at least one data analysis engine; a survey occupancy application; a rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine is in communication with the tip and cue server; wherein the tip and cue server is in communication with the survey occupancy application and the rules and policy editor; wherein the at least one data analysis engine is operable to perform interference source modeling or identify possible signals of interference for at least one customer device based on the measured data; wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy for the at least one customer device in at least one frequency band; wherein the rules and policy editor includes at least one rule and/or at least one policy; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data for spectrum allocation; and wherein the actionable data and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer device.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 1:
A system for dynamic spectrum management in an electromagnetic environment comprising: at least one monitoring sensor operable to create measured data based on the electromagnetic environment; at least one data analysis engine; a survey occupancy application; a rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine is in communication with the tip and cue server; wherein the tip and cue server is in communication with the survey occupancy application and the rules and policy editor; wherein the at least one data analysis engine is operable to perform interference source modeling or identify possible signals of interference for at least one customer device based on the measured data; wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy for the at least one customer device in at least one frequency band; wherein the rules and policy editor includes at least one rule and/or at least one policy; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data for spectrum allocation; and wherein the actionable data and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer device.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 1:
A system for dynamic spectrum management in an electromagnetic environment comprising: at least one monitoring sensor operable to create measured data based on the electromagnetic environment; at least one data analysis engine; a survey occupancy application; a rules and policy editor; and a tip and cue server; wherein the at least one data analysis engine is in communication with the tip and cue server; wherein the tip and cue server is in communication with the survey occupancy application and the rules and policy editor; wherein the at least one data analysis engine is operable to perform interference source modeling or identify possible signals of interference for at least one customer device based on the measured data; wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy for the at least one customer device in at least one frequency band; wherein the rules and policy editor includes at least one rule and/or at least one policy; wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine to create actionable data for spectrum allocation; and wherein the actionable data and the one or more of the at least one rule and/or the at least one policy are used to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer device.
Claims 1-7, 14, 16-17 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6-7, 14, 16-17 and 19-20 of U.S. Patent No. 12,160,751. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-7, 14, 16-17 and 19-20 of the instant application number 19/317,753
Claims 1-3, 6-7, 14, 16-17 and 19-20 of U.S. Patent No. 12,160,751
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, for creating measured data; at least one data analysis engine for analyzing the measured data; at least one survey occupancy application; and at least one server; wherein the at least one survey occupancy application is operable to schedule occupancy in at least one frequency band; wherein the at least one server is in communication with the at least one data analysis engine and the application; wherein the at least one server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein at least one customer application is assigned a priority; and wherein the survey occupancy application is operable to use the priority and/or the actionable data to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer application.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 3:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 7:
The system of claim 1, wherein the system further includes a certification and compliance application, 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 the at least one rule and/or the at least one policy.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, for creating measured data; at least one data analysis engine for analyzing the measured data; at least one survey occupancy application; and at least one server; wherein the at least one survey occupancy application is operable to schedule occupancy in at least one frequency band; wherein the at least one server is in communication with the at least one data analysis engine and the application; wherein the at least one server is operable to use analyzed data from the at least one data analysis engine to create actionable data; wherein at least one customer application is assigned a priority; and wherein the survey occupancy application is operable to use the priority and/or the actionable data to dynamically allocate the at least one frequency band in the electromagnetic spectrum for the at least one customer application.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 6:
The system of claim 1, further comprising a learning engine operable to learn the electromagnetic environment using machine learning (ML), artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one monitoring sensor, for creating measured data; analyzing the measured data using at least one data analysis engine, thereby creating analyzed data; determining occupancy in frequency bands and scheduling occupancy in at least one frequency band using a survey occupancy application; assigning a priority to at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum for the at least one customer application based on the priority.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 16:
The method of claim 15, The system of claim 1, wherein the survey occupancy application preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 17:
The method of claim 14, further including a certification and compliance application determining 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.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs),artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claims 1-7, 14, 16-17 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-7, 14-17 and 19-20 of U.S. Patent No. 12,273,734. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-7, 14, 16-17 and 19-20 of the instant application number 19/317,753
Claims 1-3, 5-7, 14-17 and 19-20 of U.S. Patent No. 12,273,734
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one monitoring sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; at least one survey occupancy application; and at least one server; wherein the at least one monitoring sensor includes an antenna array formed of multiple antennas resonating at different frequency bands and configured in a 1D (linear), 2D (planar), or 3D antenna configuration; wherein the at least one survey occupancy application is operable to schedule occupancy in at least one frequency band; wherein the at least one server is in communication with the at least one data analysis engine and the at least one survey occupancy application; wherein the at least one server is operable to use the analyzed data to create actionable data; wherein the system is operable to use machine learning (ML) to create prediction models to autonomously create forecasts for future spectrum usage based on historical data and the analyzed data; and wherein the survey occupancy application is operable to use the actionable data and a priority assigned to at least one customer application to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 3:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if 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.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to determine occupancy in frequency bands.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 7:
The system of claim 1, further comprising a learning engine operable to learn the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one monitoring sensor, thereby creating measured data; analyzing the measured data using at least one data analysis engine, thereby creating analyzed data; determining occupancy in at least one frequency band using a survey occupancy application; scheduling occupancy in the at least one frequency band using the at least one survey occupancy application; creating prediction models to autonomously create forecasts for future spectrum usage based on historical data and the analyzed data; assigning a priority to at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority; wherein the at least one monitoring sensor includes an antenna array formed of multiple antennas resonating at different frequency bands and configured in a 1D (linear), 2D (planar), or 3D antenna configuration.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 16:
The method of claim 14, wherein the survey occupancy application preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 17:
The method of claim 14, further including a certification and compliance application determining if 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.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines(SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claims 1-7, 14, 16-17 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-7, 14, 16-17 and 19-20 of U.S. Patent No. 12,369,043. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-7, 14, 16-17 and 19-20 of the instant application number 19/317,753
Claims 1-3, 5-7, 14, 16-17 and 19-20 of U.S. Patent No. 12,369,043
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; and at least one survey occupancy application; wherein the at least one sensor includes an antenna array formed of multiple antennas resonating at different frequency bands and configured in a 1D (linear), 2D (planar), or 3D antenna configuration; wherein the system is operable to use machine learning (ML) to create prediction models to autonomously create forecasts for future spectrum usage based on historical data or the analyzed data; and wherein the at least one survey occupancy application is operable to use a priority assigned to at least one customer application to dynamically allocate at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; and at least one survey occupancy application; wherein the at least one sensor includes an antenna array formed of multiple antennas resonating at different frequency bands and configured in a 1D (linear), 2D (planar), or 3D antenna configuration; wherein the system is operable to use machine learning (ML) to create prediction models to autonomously create forecasts for future spectrum usage based on historical data or the analyzed data; and wherein the at least one survey occupancy application is operable to use a priority assigned to at least one customer application to dynamically allocate at least one frequency band in the electromagnetic spectrum.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
The system of claim 1, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 3:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, 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.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP),control theory, and/or statistical learning techniques.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one sensor, thereby creating measured data; determining occupancy in at least one frequency band using a survey occupancy application; scheduling occupancy in the at least one frequency band using the at least one survey occupancy application; creating prediction models to autonomously create forecasts for future spectrum usage based on historical data or the measured data; assigning a priority to at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 16:
The method of claim 14, wherein the at least one survey occupancy application preprocesses at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if 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
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning technique
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claims 1-7, 14, 16-17 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-7, 14-17 and 19-20 of U.S. Patent No. 12,452,686. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1-7, 14, 16-17 and 19-20 of the instant application number 19/317,753
Claims 1-3, 5-7, 14-17 and 19-20 of U.S. Patent No. 12,452,686
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; and at least one survey occupancy application; wherein the system is operable to forecast future spectrum usage using machine learning (ML);wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one sensor operable to monitor the electromagnetic environment to create measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; and at least one survey occupancy application; wherein the system is operable to use machine learning (ML) to create prediction models to create forecasts for future spectrum usage based on historical data or the analyzed data; and wherein the at least one survey occupancy application is operable to use the prediction models and a priority assigned to at least one customer application to dynamically allocate at least one frequency band in the electromagnetic spectrum.
Claim 2:
The system of claim 1, further comprising at least one sensor operable to collect the measured data.
Claim 1:
A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one sensor operable to monitor the electromagnetic environment to create measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; and at least one survey occupancy application; wherein the system is operable to use machine learning (ML) to create prediction models to create forecasts for future spectrum usage based on historical data or the analyzed data; and wherein the at least one survey occupancy application is operable to use the prediction models and a priority assigned to at least one customer application to dynamically allocate at least one frequency band in the electromagnetic spectrum.
Claim 3:
The system of claim 2, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 2:
The system of claim 1, wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
Claim 4:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 3:
The system of claim 1, wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 5:
The system of claim 1, further comprising a certification and compliance application, 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.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 6:
The system of claim 1, wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 7:
The system of claim 1, further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: forecasting future spectrum usage based on historical data or measured data from the electromagnetic environment; dynamically allocating at least one frequency band in the electromagnetic spectrum based on at least one application type and the forecasted future spectrum usage; and determining occupancy in the at least one frequency band using a survey occupancy application.
Claim 14:
A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one sensor to create measured data; creating prediction models to create forecasts for future spectrum usage based on historical data or the measured data; assigning a priority to at least one customer application; and dynamically allocating at least one frequency band in the electromagnetic spectrum based on the priority and the prediction models.
Claim 16:
The method of claim 14, further comprising the survey occupancy application preprocessing at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 15:
The method of claim 14, further comprising determining occupancy in at least one frequency band using a survey occupancy application.
Claim 16:
The method of claim 15, further comprising the survey occupancy application preprocessing it least two signals that exist in the at least one frequency band based on interference between the at least two signals.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
Claim 17:
The method of claim 14, further comprising a certification and compliance application determining 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.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 19:
The method of claim 14, further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
Claim 20:
The method of claim 14, further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.
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) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shima (US 2018/0324595 A1, hereinafter Shima).
Regarding claim 1 and 14, taking claim 1 as exemplary:
Shima shows:
“A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; … wherein the system is operable to forecast future spectrum usage using machine learning (ML);” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
“and at least one survey occupancy application; … wherein the at least one survey occupancy application is operable to use the forecasted future spectrum usage and at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claim 2:
Shima shows the system of claim 1 as claimed and specified above.
And Shima shows “further comprising at least one sensor operable to collect the measured data.” (Paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.”)
Regarding claim 3:
Shima shows the system of claim 3 as claimed and specified above.
And Shima shows “wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.” (Paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.”)
Regarding claims 4 and 16, taking claim 4 as exemplary:
Shima shows the system and method of claims 1 and 14 as claimed and specified above.
And Shima shows “wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claims 5 and 17, taking claim 5 as exemplary:
Shima shows the system and method of claims 1 and 14 as claimed and specified above.
And Shima shows “further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.” (Paragraph [0041]: “Automated channel access recognition (ACAR) using deep learning manages and optimizes the radio frequency (RF) spectrum allocation by selecting the channel access mechanism and associated tuning parameters to adapt to the changing RF environment, which can consist of other collaborative radio networks, non-collaborative radio networks (which are incapable of adapting) and other potential interference sources. Successful network design will consider traffic priority, latency and data rate requirements as part of the machine learning techniques to product spectrum policies for the radio that determine when, where and how to utilize its resources to optimize the total spectrum usage.” And in paragraph [0085]: “In an example implementation, software or firmware instructions for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21. Rules for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 as persistent datastores. For example, a spectrum sensing and allocation module may be implemented with instructions stored in the memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21.”)
Regarding claim 6:
Shima shows the system of claim 1.
And Shima shows “wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claims 7 and 18, taking claim 7 as exemplary:
Shima shows the system and method of claims 1 and 14 as claimed and specified above.
And Shima shows “further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (Al), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claims 8 and 15, taking claim 8 as exemplary:
Shima shows the system and method of claims 1 and 14 as claimed and specified above.
And Shima shows “wherein the at least one application type includes traffic management, telemedicine, virtual reality, video streaming, social media, and/or autonomous transportation.” (Paragraph [0041]: “Automated channel access recognition (ACAR) using deep learning manages and optimizes the radio frequency (RF) spectrum allocation by selecting the channel access mechanism and associated tuning parameters to adapt to the changing RF environment, which can consist of other collaborative radio networks, non-collaborative radio networks (which are incapable of adapting) and other potential interference sources. Successful network design will consider traffic priority, latency and data rate requirements as part of the machine learning techniques to product spectrum policies for the radio that determine when, where and how to utilize its resources to optimize the total spectrum usage.” And in paragraph [0085]: “In an example implementation, software or firmware instructions for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21. Rules for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 as persistent datastores. For example, a spectrum sensing and allocation module may be implemented with instructions stored in the memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21.” And in paragraph [0071]: “Using this topology, each sub channel (after the channelizer in FIG. 4) is classified into smaller frequency and time cuts than the original data set. Instead of having to further channelize the sub-channels, mix each to baseband, filter and measure, the system disclosed herein allows the FCNN to compute localized ACAR metrics automatically, which can then be fed into downstream processing for traffic priority, tuning parameters, etc.” – The use of traffic priority is traffic management. Note that the claim is written in the alternative and not all claim elements (i.e. telemedicine, virtual reality, video streaming, social media, and/or autonomous transportation) needs to be recited for teaching by the reference to be satisfied.)
Regarding claim 9:
Shima shows:
“A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one data analysis engine for analyzing measured data from the electromagnetic environment to create analyzed data; … wherein the at least one data analysis engine is operable to learn the electromagnetic environment based on the analyzed data, (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
“and at least one survey occupancy application; … wherein the at least one survey occupancy application is operable to use at least one application type to dynamically allocate at least one frequency band in the electromagnetic spectrum; and wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
“creating a utilization mask; wherein the system is operable to forecast future spectrum usage based on the utilization mask;” (Paragraph [0008]: “If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claim 10:
Shima shows the system of claim 9 as claimed and specified above.
And Shima shows “further comprising a learning engine configured to learn the electromagnetic environment using artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs),Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claim 11:
Shima shows the system of claim 9 as claimed and specified above.
And Shima shows “wherein the at least one application type includes traffic management, telemedicine, virtual reality, video streaming, social media, and/or autonomous transportation.” (Paragraph [0041]: “Automated channel access recognition (ACAR) using deep learning manages and optimizes the radio frequency (RF) spectrum allocation by selecting the channel access mechanism and associated tuning parameters to adapt to the changing RF environment, which can consist of other collaborative radio networks, non-collaborative radio networks (which are incapable of adapting) and other potential interference sources. Successful network design will consider traffic priority, latency and data rate requirements as part of the machine learning techniques to product spectrum policies for the radio that determine when, where and how to utilize its resources to optimize the total spectrum usage.” And in paragraph [0085]: “In an example implementation, software or firmware instructions for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21. Rules for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 as persistent datastores. For example, a spectrum sensing and allocation module may be implemented with instructions stored in the memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21.” And in paragraph [0071]: “Using this topology, each sub channel (after the channelizer in FIG. 4) is classified into smaller frequency and time cuts than the original data set. Instead of having to further channelize the sub-channels, mix each to baseband, filter and measure, the system disclosed herein allows the FCNN to compute localized ACAR metrics automatically, which can then be fed into downstream processing for traffic priority, tuning parameters, etc.” – The use of traffic priority is traffic management. Note that the claim is written in the alternative and not all claim elements (i.e. telemedicine, virtual reality, video streaming, social media, and/or autonomous transportation) needs to be recited for teaching by the reference to be satisfied.)
Regarding claim 12:
Shima shows the system of claim 9 as claimed and specified above.
And Shima shows “wherein the system further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application of the at least one application type and/or at least one customer device is behaving according to at least one rule and/or at least one policy.” (Paragraph [0041]: “Automated channel access recognition (ACAR) using deep learning manages and optimizes the radio frequency (RF) spectrum allocation by selecting the channel access mechanism and associated tuning parameters to adapt to the changing RF environment, which can consist of other collaborative radio networks, non-collaborative radio networks (which are incapable of adapting) and other potential interference sources. Successful network design will consider traffic priority, latency and data rate requirements as part of the machine learning techniques to product spectrum policies for the radio that determine when, where and how to utilize its resources to optimize the total spectrum usage.” And in paragraph [0085]: “In an example implementation, software or firmware instructions for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21. Rules for providing spectral sensing and allocation may be stored in memory 22 and/or storage devices 29 or 31 as persistent datastores. For example, a spectrum sensing and allocation module may be implemented with instructions stored in the memory 22 and/or storage devices 29 or 31 and processed by the processing unit 21.”)
Regarding claim 13:
Shima shows the system of claim 9 as claimed and specified above.
And Shima shows “wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.”)
Regarding claim 19:
Shima shows the method of claim 14 as claimed and specified above.
And Shima shows “further comprising generating a conditional probability set, wherein the conditional probability set indicates an optimal outcome for a scenario.” ([0069] Furthermore, a computational enhancement falls out from utilizing the CNN. Instead of computing the channel quality (CQ) metric over the entire bandwidth of the sub-channel (i.e. 1 MHz), returning a single metric, the system extends the idea by transforming the same network into a fully-convolutional neural network (FCNN). The fully convolutional network provides channel quality metrics over sections of the entire input spectrogram, effectively localizing the CQ metric in time and frequency. The output of the network is a heat map that returns probabilities over each windowed section of the spectrogram.” And in paragraph [0070]: “FIG. 7 illustrates an example fully-convolutional neural network heat map probability output 702 of class “aircraft carrier”. Specifically, FIG. 7 shows an example of an image-based FCNN classifier heat map output for a single selected class. The heat map in FIG. 7 may result from analyzing RF spectrograms, depicted in FIG. 4 as an image 700 containing an aircraft carrier, and may contain channel quality probabilities for each pre-defined channel quality class.” And in paragraph [0071]: “Using this topology, each sub channel (after the channelizer in FIG. 4) is classified into smaller frequency and time cuts than the original data set. Instead of having to further channelize the sub-channels, mix each to baseband, filter and measure, the system disclosed herein allows the FCNN to compute localized ACAR metrics automatically, which can then be fed into downstream processing for traffic priority, tuning parameters, etc.” – The heat map probabilities for specific channels for traffic priority shows a probability for an analysis to determine an optimal scenario.)
Regarding claim 20:
Shima shows the method of claim 14 as claimed and specified above.
And Shima shows “further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.” (Paragraph [0007]: “In accordance with embodiments of the present disclosure, systems and methods are provided that utilize a learning algorithm, also referred to herein as deep learning or convolutional neural network, to enable the identification of RF signals within the RF spectrum or portions thereof. More particularly, embodiments of the present disclosure enable the detection of even low energy and low probability of detection, transient signals. Once a signal is detected, embodiments of the present disclosure provide for additional analysis. This additional analysis can include assessing a quality of a channel in which a signal has been found, to determine whether that channel can nonetheless be used for an additional signal. Alternatively or in addition, the occupied bandwidth can be analyzed, and a frequency mask that operates to identify areas of the spectrum that are unavailable can be created. These detection and analytical functions can be performed using deep learning, or deep neural network (DNN) systems, such as but not limited to convolutional neural network (CNN) systems. Embodiments of the present disclosure can be used to implement next-generation communications systems, such as but not limited to configurable RF systems, such as satellite communications or 5G communications systems, in which spectrum is allocated dynamically.” In paragraph [0008]: “In accordance with at least some embodiments of the present disclosure, data in the form of a spectrogram or short-time Fourier transform of a RF spectrum. The imaginary and real components of the Fourier transform are fed into the inputs of a DNN system, which in accordance with at least some embodiments of the present disclosure can be implemented as a CNN system. Moreover, embodiments of the present disclosure can incorporate a number of DNN systems. For example, the components of the transform can be provided to a first DNN system that operates to detect the presence of signals within the spectrum with a high degree of sensitivity. If this first CNN system determines that a signal is present in the spectrum or in a particular area of the spectrum, the operation of one or more additional DNN systems can be triggered. For instance, an additional DNN system that analyzes the quality of the spectrum in areas indicated as being occupied, and that outputs a heat map showing levels of channel occupancy, can be provided. Alternatively or in addition, an additional DNN system can be provided that analyzes the occupied bandwidth, as identified by the first DNN system or independently, and outputs a mask that identifies areas of the spectrum that are not available. If both of the additional DNN systems are provided, they can be operated in series or in parallel. The outputs of the various DNN systems can then be applied to determine or control characteristics of a transmission of a connected device, such as a software defined radio.” And in paragraph [0032]: “In both commercial and military communications, the overwhelming demand for data in a finite radio frequency (RF) spectrum has created a “spectrum crunch”. Smart allocation of spectral resources is paramount in order to ensure efficient communications. This is even more significant in a tactical or combat environment, where communication links are vital and interference or lost transmissions can cost lives.” – The “smart allocation” because of spectrum crunch is the allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhang et al., (US 2020/0092888 A1), part of the prior art made of record, describes spectrum management of claims 1, 9, and 14 in paragraph [0147] through frequency domain allocation based on signal synchronization of terminal devices.
Awad (US 2011/0222489 A1), part of the prior art made of record, describes the utilization mask of claim 9 in paragraph [0007] through a frequency resource allocation from a bit mask bitmap.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE D WOOLWINE whose telephone number is (571)272-4138. The examiner can normally be reached M-F 9:30-6:00 PM.
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SHANE D. WOOLWINE
Primary Examiner
Art Unit 2124
/SHANE D WOOLWINE/Primary Examiner, Art Unit 2124