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 .
Response to Amendment
As per the instant Application having Application number 19/317,753 the examiner acknowledges the applicant's submission of the amendment dated 03/27/2026. At this point, claims 1, 5, 9, 14, and 17 have been amended. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed 03/27/2026, with respect to the non-statutory double patenting rejection(s) of claims 1-20 have been fully considered and are persuasive. The non-statutory double patenting rejection(s) has been withdrawn.
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed 03/27/2026, with respect to 35 U.S.C. 102(a)(1) rejection of claims 1-20 have fully been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
“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.
“a semantic engine… wherein the semantic engine is operable to use using natural language processing” 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 semantic engine… wherein the semantic engine is operable to use using natural language processing” 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.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shima (US 2018/0324595 A1, hereinafter Shima) in view of Noll et al., (US 2020/0312103 A1, hereafter Noll) and Boyle et al., (US 2018/0349795 A1, Boyle).
Regarding claim 1:
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.”)
But Shima does not appear to explicitly recite “a semantic engine… wherein the at least one data analysis engine is operable to calculate a first derivative and a second derivative of power levels in the electromagnetic environment to create a mask;… wherein the semantic engine is operable to use using natural language processing (NLP) to establish at least one rule or at least one policy based on a user data input;”
However, Noll teaches “wherein the at least one data analysis engine is operable to calculate a first derivative and a second derivative of power levels in the electromagnetic environment to create a mask:” (Paragraph [0080]: “The band energy computation subunit 32 is configured to compute the energy of the signal in each DWT band E.sub.S,b. The SMR computation subunit 34 is configured to compute a signal-to-mask-ratio (SMR) for each DWT band based on the sum of the energy of the global masking threshold in each band E.sub.M,b and on the computed energy of the signal in each band E.sub.S,b. The SMR for each band is obtained by dividing E.sub.S,b by E.sub.M,b and representing the result in dB.”)
And Boyle teaches “a semantic engine… wherein the semantic engine is operable to use using natural language processing (NLP) to establish at least one rule or at least one policy based on a user data input;” (Paragraph [0060]: “the features may be based at least in part on natural language processing (NLP). For example, a computer system may extract information from text according to NLP techniques. Text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule-based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning or computer vision or NLP, and recommended for inclusion in a product design.”)
Shima, Noll, and Boyle are analogous in the arts because Shima, Noll, and Boyle all describe discerning through machines.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Shima, Noll, and Boyle before him or her, to modify the teachings of Shima to include the teachings of Noll in order to increase efficiency accuracy of frequency determination using power levels and to further include the teachings of Boyle to increase usability by including user preferences through natural signal processing.
Regarding claim 2:
Shima, Noll, and Boyle teach 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, Noll, and Boyle teach 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 claim 4:
Shima, Noll, and Boyle teach the system of claim 1 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 5:
Shima, Noll, and Boyle teach the system of claim 1 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 the at least one rule and/or the 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, Noll, and Boyle teach 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 claim 7:
Shima, Noll, and Boyle teach the system of claim 1 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 claim 8
Shima, Noll, and Boyle teach the system of claim 1 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.)
Claim(s) 9-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shima in view of Boyle.
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.”)
But Shima does not appear to explicitly recite “a semantic engine… wherein the semantic engine is operable to use natural language processing (NLP) to establish at least one rule or at least one policy based on a user data input;”
However, Boyle teaches “a semantic engine… wherein the semantic engine is operable to use natural language processing (NLP) to establish at least one rule or at least one policy based on a user data input;” (Paragraph [0060]: “the features may be based at least in part on natural language processing (NLP). For example, a computer system may extract information from text according to NLP techniques. Text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule-based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning or computer vision or NLP, and recommended for inclusion in a product design.”)
Shima and Boyle are analogous in the arts because Shima and Boyle all describe discerning through machines.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Shima and Boyle before him or her, to modify the teachings of Shima to include the teachings of Boyle to increase usability by including user preferences through natural signal processing.
Regarding claim 10:
Shima and Boyle teach 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 and Boyle teach 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 and Boyle teach 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 and Boyle teach 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 14:
Shima shows:
“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;” (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.”)
“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.” (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.”)
But Shima does not appear to explicitly recite “establishing at least one rule or at least one policy using natural language processing (NLP) based on a user data input;”
However, Boyle teaches “establishing at least one rule or at least one policy using natural language processing (NLP) based on a user data input;” (Paragraph [0060]: “the features may be based at least in part on natural language processing (NLP). For example, a computer system may extract information from text according to NLP techniques. Text generated by and about customers such as in product reviews, comment forms, social media, emails, and the like may be analyzed by an NLP system to determine customer preferences. For example, a customer may provide feedback (e.g., text) when they receive an item (e.g., 112 of FIG. 2). The feedback provided by the customer may be processed with NLP techniques to extract features. NLP techniques include rule-based engines, clustering, and classification to make determinations about characteristics of a product that might be considered a feature. Features may be identified by machine learning or computer vision or NLP, and recommended for inclusion in a product design.”)
Shima and Boyle are analogous in the arts because Shima and Boyle all describe discerning through machines.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Shima and Boyle before him or her, to modify the teachings of Shima to include the teachings of Boyle to increase usability by including user preferences through natural signal processing.
Regarding claim 15:
Shima and Boyle teach the method of claim 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 16:
Shima and Boyle teach the method of claim 14 as claimed and specified above.
And Shima shows “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.” (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 17:
Shima and Boyle teach the method of claim 14 as claimed and specified above.
And Shima shows “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 the at least one rule and/or the 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 18:
Shima and Boyle teach the method of claim 14 as claimed and specified above.
And Shima shows “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.” (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 and Boyle teach 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 and Boyle teach 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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
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