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 .
This action is responsive to the amended claims filed on 09/17/2024. Claims 1-6 and 9-15 are pending for examination.
This action is Final.
Response to Arguments
Applicant’s arguments with respect to the 35 U.S.C. 103 rejection of the claims have 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 Rejections - 35 USC § 103
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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6 and 9-15 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Thomas Rondeau (Bostian, C.W., & Rondeau, T.W. (2007). Application of artificial intelligence to wireless communications.), hereafter referred to as Rondeau and in further view of Kuru et al. (US 20220021469 A1), hereafter referred to as Kuru, Chelmins et al. (Chelmins, D., Briones, J., Downey, J., Clark, G., & Gannon, A. (2019, October). Cognitive communications for NASA space systems. In Advances in Communications Satellite Systems. Proceedings of the 37th International Communications Satellite Systems Conference (ICSSC-2019) (pp. 1-16). IET.), hereafter referred to as Chelmins, Shao (US 8532034), hereafter referred to as Shao, and Fang et al. (US 20220406311 A1), hereafter referred to as Fang.
PNG
media_image1.png
595
1147
media_image1.png
Greyscale
Figure 6.1 of Rondeau, Case-based Decision Theory with Optimization Process.
Claim 1: Rondeau teaches:
one or more transmitters configured to transmit an output signal utilizing a plurality of waveforms, each respective waveform of the plurality of waveforms associated with a respective communication protocol and a respective data capacity (Rondeau, page 7, section 2.1, paragraph 1, “A cognitive radio is a flexible and intelligent radio capable of creating any waveform and using any protocol supported by the radio hardware and software… Protocols are the rules by which network nodes transfer information. A cognitive radio develops waveforms and chooses protocols in real-time using artificial intelligence.”, When a waveform is developed, the cognitive engine is used to choose protocols that best fit it.
Page 48, paragraph 1, “Spectral efficiency represents the amount of information transferred in a given channel and is measured in bits per second per Hertz (bps/Hz). Although this concept is directly related to both bandwidth and throughput rates, I have developed it as a separate objective in order to represent the quality of service needs more thoroughly. When choosing to measure spectral efficiency, it offers another dimension to determine how suited the waveform is to a particular need between bandwidth occupancy and data rates.”, Spectral efficiency measures how much data is transferred in waveform, measured in bits per second. This represents the data capacity for a given waveform and is used in an objective function with the cognitive engine to help determine a best waveform.
Page 26, Figure 3.2 shows a high-level view of a radio transmitter, Page 31, Figure 3.4 shows a flow graph for the transmitter, indicated by txpath. This transmitter transmits waveforms output from the cognitive engine);
one or more receivers configured to receive an input signal utilizing the plurality of waveforms (Rondeau, page 31, paragraph 3, figure 3.4, “The signal is then passed to the receiver chain. The receiver first down-converts the signal from the center frequency back to baseband, goes through a channel filter that resamples in reverse of tx_resample and interp”, a receiver capable of converting a waveform (signal) from the plurality of waveforms created by the cognitive engine.);
one or more processors coupled to the one or more transmitters and the one or more receivers (Rondeau, Page 10 section 2.2 mentions “This aspect also enables distributed processing, where different components can reside on different processors”);
and a memory coupled to the one or more processors (Rondeau, Page 86 paragraph 2 mentions a program, “A simple Python program controls the sensor and the cognitive controller while the optimization GA is run as a separate process”, a cognitive radio can be defined as, a radio that can be programmed and configured dynamically to use the best wireless channels in its vicinity to avoid user interference and congestion, this Python program which controls the sensor and the cognitive controller implies that the instructions of this program are stored on a memory coupled to the one or more processors.);
and an artificial intelligence engine in communication with the one or more processors and the memory, wherein the artificial intelligence engine is configured to: receive operational data from one or more operation systems (Rondeau, Page 11 section 2.3.1 mentions “Sensors collect data from the radio or other systems to describe and model the environment. Environmental data can include almost anything that will help the radio adjust its behavior… The sensors collect the information by any means available or necessary: developed by a third-party, pre-built libraries, or specifically developed for use with the cognitive engine” Data collected from these sensors is effectively the same as operational data);
determine, based on the operational data, an artificial intelligence input including a selected waveform, wherein the selected waveform is different than a preceding waveform utilized by the communication sub-system, wherein the plurality of waveforms includes the selected waveform and the preceding waveform (Rondeau, page 78, paragraph 3, “Figure 6.1 presents the block diagram of the described system. An incoming problem is matched against the cases through a similarity function while the case results are compared to the radio performance to develop the utility of the case. The decision function is an equation like equation 6.3 that uses the similarity and utility to properly select the most representative case to the new problem. The results are then passed to the optimization process along with the new problem. Both the waveform solution and the objective functions' results are fed back to the case-base to be stored along with the problem model as a new case.”, as shown in figure 6.1 of Rondeau pictured above, an artificial intelligence input via genetic algorithms is given, wherein a selected waveform is different from the previous waveform based on Case-based Decision Theory (CBDT) and optimization of parameters from the optimization process. Page 14, section 2.3.2, “Depending on the implementation, the optimization may build a new waveform or select it from a list of pre-defined waveforms designed for specific problems.”, it should be noted that the Optimization Process does not only optimize a single waveform but may create an entirely new waveform or select one of any preceding waveforms via CBDT.);
and send the artificial intelligence input to the one or more processors, wherein the memory has instructions stored thereon, which when executed by the one or more processors cause the one or more processors to (Rondeau, page 26, paragraph 2, “Given the limitations of realizing the ideal SDR, hardware can perform some of the signal manipulation while processors of different types, such as field programmable gate arrays (FPGA), digital signal processors (DSP), and general purpose processors (GPP) can handle other parts of the signal processing. Figure 3.2 shows a very high-level view of a radio transmitter. A SDR architecture design must decide which components should be in hardware or software, and what type of processors should run the software based on design needs and trade-offs.”, the use of Field-programmable Gate Arrays (FPGA) means the use of memory which has instructions stored thereon for the FPGA to operate):
Chelmins, in the same field of artificial intelligent radio waveform optimization, teaches the following limitation which Rondeau fails to teach:
PNG
media_image2.png
331
627
media_image2.png
Greyscale
Figure 16.5 of Chelmins, Low-rate vs High-rate channel communication.
establish a link to a network corresponding to the selected waveform, before a switch of the communication sub-system from utilizing the preceding waveform to utilizing the selected waveform, (Chelmins, page 11, section 16.6.1, paragraph 2, “A request for service is received over the control channel by a central Event Manager that is aware of both spacecraft and communications network capability. The Event Manager contracts on behalf of the requesting spacecraft for communication service with any network capable of establishing a link with the mission spacecraft.”, Chelmins discloses “establishing a link” analogous to that of the claim’s language. The Event Manager establishes a network link before another data channel is activated, implying a pre-establishment process as disclosed in the claim.)
wherein the network is required for a use of the selected waveform, but not the preceding waveform, (Chelmins, page 12, paragraph 2, “The low-rate S-band multiple access service was used as a control channel to send requests to schedule high-rate Ka-band single access service for data transfer. Though both services were provided by the TDRSS, the high-performance Ka-band link is capable of supporting data rates 5,000 times greater than those of the S-band multiple access system [38].”, the Event Manger as disclosed above may switch from a low-rate S-band communication to the high-rate KA-band communication in order to communication with the ground station/relay satellite as shown in figure 16.5 of Chelmins above. The high-rate KA-band is required for communication with the satellite/ground station network.)
wherein the establishing is based on the artificial intelligence input; (Chelmins, page 6, section 16.4.2., “A new approach is to implement a cognitive engine that decides when to change modulation, coding, and transmission power based on observed channel conditions and mission platform constraints.”, here, “establishing based on the artificial intelligence input” maps directly to the use of a cognitive engine, which bases its decisions on observed conditions. The cognitive engine serves as the AI input that triggers changes in waveform parameters, aligning with the claim language.
Chelmins, page 14, section 16.7.1, “The hardware necessary to implement cognitive communications capabilities onboard spacecraft typically mimics the hardware that enables artificial intelligence and machine learning on the ground.”, this shows cognitive engines are implemented similar to artificial intelligence or machine learning. The paper outlines the use of various machine learning algorithms for its cognition. Therefore it is interpreted that an artificial intelligence input is used to establish cognitive radio waveforms.)
wherein the preceding waveform is configured for line-of-sight (LOS) conditions and (Chelmins, page 12, paragraph 2, “An on-orbit experiment using NASA’s SCaN Testbed demonstrated the concept with TDRSS. The low-rate S-band multiple access service was used as a control channel to send requests to schedule the high-rate Ka-band single access service for data transfer ”, in this case the “preceding waveform” is mapped to the “low-rate S-band multiple access service” that acts as the control channel to setup the high-speed satellite-based K-band service. Although the quote does not state “line-of-sight”, it is interpreted by the examiner that S-band control channels are ground-based and require a direct line-of-sight path. This teaching satisfies the claim’s limitation that the preceding waveform is configured for line-of-sight conditions. )
wherein the selected waveform corresponding to the link to the network comprises a satellite-based waveform (Chelmins, page 12, paragraph 2, “… the high-rate Ka-band single access service for data transfer ”, the selected waveform (the high-rate Ka-band) is delivered via satellite relays which directly teaches the claim’s language.)
It would have been obvious to someone of ordinary skill in the art before the effectively filing date of the claimed invention to have modified Rondeau to incorporate the teachings of Chelmins and include a method for establishing a connection link before switching as a solution to avoid a disconnection, (Kuru, paragraph 45, “for example, of a wireless channel between the UE and BS, and then may request or perform an increase in connection robustness (e.g., by using more robust modulation and coding scheme and/or activating additional multi-connectivity links) or a handover to another BS, e.g., to avoid a disconnection or in attempt to improve radio network performance for the UE.”).
Rondeau further teaches:
the switch of the communication sub-system from utilizing the preceding waveform to utilizing the selected waveform to transmit the output signal, wherein the switching is based on the artificial intelligence input (Rondeau, page 155, section D.5, paragraph 2, “The radio node sections are used to describe attached radios that the cognitive radio communicates with on the network to transmit waveform information.”, based off the AI input for a new waveform to switch to (given from the cognitive engine), the communication system switches the current waveform to the new waveform to transmit to other radio nodes),
Shao, in the same field of radio communication, teaches the following limitations which Rondeau and Kuru fail to teach:
wherein the switching is coordinated, ((Shao, claim 1 mentions “A method of providing coordination for managing communication of information over one or more wireless channels in a network including multiple wireless stations of different types operating at the same wireless frequency band but at different waveform formats” this method can be used to coordinate waveform switching as disclosed in the specification))
wherein the preceding waveform and the selected waveform are communicating a same message, (Shao, Page 3 paragraph 1 mentions “The XG program…is a dynamic spectrum access (DSA) system that provides seamless communications while changing frequencies to keep from interfering with other networks as well as taking interference.” Where seamless communication is referring to the waveform not having a delay or interrupting at least one characteristic of the same message)
It would have been obvious to someone of ordinary skill in the art before the effectively filing date of the claimed invention to have modified Rondeau to incorporate the teachings of Shao and include a method for coordinated communication. A motivation of which is to provide support for interoperability between waveform communications (Shao, col. 5, line 19, “In one embodiment, a specialized superframe structure and channel time allocation scheme with universal coordination are used by the UC 11 to support coexistence and interoperability among 60 GHz wireless stations with different types of waveforms.”).
Rondeau further teaches:
wherein at least one of the one or more transmitters are configured to transmit the output signal utilizing the selected waveform and at least one of the one or more receivers are configured to receive the input signal utilizing the selected waveform (Rondeau, page 31, section 3.4.1, paragraph 2, “The signal is then passed to the receiver chain. The receiver first down-converts the signal from the center frequency back to baseband, goes through a channel filter that resamples in reverse of tx_resample and interp. After filtering and resampling, the resulting signal is the transmitted signal plus noise plus any interference energy that exists within the signal bandwidth. The received signal then goes into rxpath where it is demodulated.”, the signal (waveform) given from the AI input is transmitted as an output signal as well as demodulated to be received by a receiver.),
Kuru, in the same field of artificial intelligent radio waveform optimization, teaches the following limitation which Rondeau fails to teach:
wherein the operational data comprises geographical data, wherein the geographical data comprises terrain data (Kuru, paragraph 54, “In a further example, the receiving sensor data associated with a sensor that detects a status or state of an object or portion of the physical environment may include, e.g., receiving sensor data from a Radar or LiDAR that performs detection, ranging, bearing or location determination of an object or portion of the physical environment.”, the sensor data which comprises the ASRI data is fed into the machine learning model. The sensor data includes geographical terrain data modeled by a Radar or LiDAR system. It is well known in the art that LiDAR has terrain mapping of an environment and therefore reads upon this limitation.).
It would have been obvious to someone of ordinary skill in the art before the effectively filing date of the claimed invention to have modified Rondeau and Kuru to incorporate the teachings of Shao and include a method for providing coordination as a solution to interference problems from unknown waveforms from other communication systems, (Shao, Page 17, Col 1, Lines 25-28, “If these wireless stations cannot detect and understand the waveform formats from each other, then interference problems may occur when the wireless stations operate in close proximity”).
Fang, in the same field of waveform communication, teaches the following limitations which the above prior art fails to teach:
wherein the switch of the communication sub-system from utilizing the preceding waveform to utilizing the selected waveform corresponding to the network is configured to be indicated at least one of audibly or visually to a pilot. (Fang, paragraph 93, “According to some embodiments, the method may further include: receiving a first switch input for the waveform associated with the audio information; displaying the real-time recognized text information on the user interface of the electronic device in response to the first switch input; receiving a second switch input for the real-time recognized text information; and displaying a waveform associated with the audio information on the user interface of the electronic device in response to the second switch input.”, a display is used to indicate to the user (the pilot) of a switch in radio waveform.)
It would have been obvious to someone of ordinary skill in the art before the effectively filing date of the claimed invention to have modified Rondeau, Kuru, and Shao to incorporate the teachings of Fang and include a display to the user for waveform switching, a motivation of which is to allow the user the ability to display waveform communication data in a display for greater freedom of user operation (Fang, paragraph 98, “In this way, the user can select whether to display the real-time recognized text information or the waveform associated with the audio information on the user interface as needed, resulting in greater freedom of user operation and improved user experience.”).
Claim 2: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
herein the artificial intelligence engine is further configured to apply a machine learning model to analyze the operational data (Page 30 paragraph 1 mentions “information is data of the environment collected through the available sensors…The information collected from the sensors feeds both the learning and the optimization routines to help them makes decisions.”).
Claim 3: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 2. Rondeau further teaches:
wherein the artificial intelligence engine is further configured to generate the machine learning model, comprising: collecting the operational data (Page 125 section 8.6 mentions “sensors that communicate with the simulation to collect the data and pass it to the cognitive engine”);
Claim 4: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
establish, prior to the switching, a communication link to transmit and receive the output signal utilizing of the selected waveform (Rondeau, page 94, section 7.1, paragraph 2, “The control channel is defined to use simple, robust waveforms on which all nodes are capable of communicating. In the worst case, if the cognitive radio nodes loose communications, they can revert to the control channel and wait for the new waveform information and then reestablish communications. The use of a control channel is also used to begin communications when a node wants to join a network that might be using any waveform or any frequency. The control channel allows the new node a way to communicate with the network and initialize communications. This concept is often referred to as rendezvous: the method by which a radio hails and enters a network.”, a method of initializing communications with other radios is given. Therefore, a communication link to transmit and receive signals output from the cognitive engine, is established prior to the optimization (the switching of waveforms).).
Claim 5: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
wherein the selected waveform comprises a primary waveform, an alternate waveform, a contingency waveform, or an emergency waveform (Page 7 section 2.1 mentions “A cognitive radio is a flexible and intelligent radio capable of creating any waveform and using any protocol supported by the radio hardware and software.”).
Claim 6: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
wherein the artificial intelligence input is further configured to instruct the one or more processors to deactivate the preceding waveform (Page 14 section 2.3.2 mentions “Depending on the implementation, the optimization may build a new waveform or select it from a list of pre-defined waveforms designed for specific problems.”, the new waveform being built is considered to be deactivating a preceding waveform).
Claim 9: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
wherein the switching of the preceding waveform to the selected waveform is seamless, wherein at least one characteristic of the same message is communicated without interruption or delay during the switching of the preceding waveform to the selected waveform (Page 3 paragraph 1 mentions “The XG program…is a dynamic spectrum access (DSA) system that provides seamless communications while changing frequencies to keep from interfering with other networks as well as taking interference.” Where seamless communication is referring to the waveform not having a delay or interrupting at least one characteristic of the same message).
Claim 10: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
wherein the one or more operation systems includes one or more of a navigation system, the communication system, a communication architecture, a surveillance system, a sensor system, a cyber security system, or a survivability system (Page 11 section 2.3.1 mentions “Environmental data can include almost anything that will help the radio adjust its behavior, including radio propagation, interference models (temperature), position and location, time, and possible visual cues”).
Claim 11: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1, Rondeau further teaches:
wherein the artificial intelligence input is further configured to instruct the one or more processors to enter a network (Page 93 chapter 7 mentions “A final challenge to enable the cognitive radio system’s basic functionality is the ability to transmit the cognitive engine’s information and solutions among the nodes operating on the network”).
Claim 12: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
wherein the artificial intelligence engine is further configured to determine unnecessary waveforms (Page 9 paragraph 1, “This environmental information helps provide optimization boundaries on the decision making and waveform development.” the optimization process is considered to comprise determining unnecessary waveforms)
that may be deactivated within the communication system (Page 14 section 2.3.2 mentions the optimization process, “Depending on the implementation, the optimization may build a new waveform or select it from a list of pre-defined waveforms designed for specific problems”, it is noted that the phrase “deactivated within the communication system” is non-limiting, because of the use of “may be”).
Claim 13: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the communication system of claim 1. Rondeau further teaches:
wherein the communication sub-system is disposed in a vehicle (Page 19 section 2.4 mentions vehicles, “Applications (of cognitive radios) range from voice communications under low power conditions, communications in high interference zones, to more complex, critical, and hostile military networks of interoperating vehicles and soldiers with many different network needs”).
Claim 14: Rondeau teaches:
receiving operational data from one or more operation systems, wherein the operational data is received by an artificial intelligence engine (Page 11 section 2.3.1 mentions “The sensors collect the information by any means available or necessary: developed by a third-party, pre-built libraries, or specifically developed for use with the cognitive engine”, where sensors provide the operational data and cognitive engine utilizes AI algorithms with that data);
determining, based on the operational data, an artificial intelligence input including the selected waveform, wherein the selected waveform is different than the preceding waveform, wherein a plurality of waveforms includes the selected waveform and the preceding waveform and each respective waveform of the plurality of waveforms is associated with a respective communication protocol and a respective data capacity (Rondeau, page 78, paragraph 2, “To develop the performance measurements, the cognitive engine uses the results of the optimization process and analyzes how closely those results match to the actual performance of the radio. The optimization process develops the waveform based on a set of mathematical models in the form of objective functions. The results of the objective functions are calculated performance measures of the waveform. When the waveform is then used in the environment, the resulting performance may differ from the calculated performance. This difference relates to the utility of the waveform. Figure 6.1 presents the block diagram of the described system. An incoming problem is matched against the cases through a similarity function while the case results are compared to the radio performance to develop the utility of the case. The decision function is an equation like equation 6.3 that uses the similarity and utility to properly select the most representative case to the new problem. The results are then passed to the optimization process along with the new problem. Both the waveform solution and the objective functions' results are fed back to the case-base to be stored along with the problem model as a new case.”, this is a general outline of the cognitive engine’s process for choosing a new waveform. Artificial intelligence via a Genetic Algorithm is used to optimize a given waveform that is either entirely new or from a previous case. Each waveform may have their own respective protocols and data capacity (spectral efficiency), thus making selected waveforms and preceding waveforms different from each other. The use of protocols and spectral efficiency is cited in claim 1.);
and sending the artificial intelligence input to a communication sub-system (Page 11 figure 2.3 depicts optimizers, which takes in environmental data and selects a new waveform using AI algorithms, sending information back to the cognitive controller);
wherein the switching is seamless such that the switching does not cause a time period of interruption of the output signal (Rondeau, page 3, paragraph 2, “The other areas that are directly related to the implementation of cognitive radio and cognitive radio-like technology include the XG program, the IEEE 802.22 standard, and the IEEE P.1900 e ort, now known as Standards Coordinating Committee (SCC) 41. The XG program [5] is a dynamic spectrum access (DSA) system that provides seamless communications while changing frequencies to keep from interfering with other networks as well as taking interference.”, the XG program provides seamless communication. Figure 2.3 of Rondeau shows an example cognitive engine which employs the XG program.)
Chelmins teaches the following limitation which Rondeau fails to teach:
establish a link to a network corresponding to the selected waveform, before a switch of the communication sub-system from utilizing the preceding waveform to utilizing the selected waveform, (Chelmins, page 11, section 16.6.1, paragraph 2, “A request for service is received over the control channel by a central Event Manager that is aware of both spacecraft and communications network capability. The Event Manager contracts on behalf of the requesting spacecraft for communication service with any network capable of establishing a link with the mission spacecraft.”, Chelmins discloses “establishing a link” analogous to that of the claim’s language. The Event Manager establishes a network link before another data channel is activated, implying a pre-establishment process as disclosed in the claim.)
wherein the network is required for a use of the selected waveform, but not the preceding waveform, (Chelmins, page 12, paragraph 2, “The low-rate S-band multiple access service was used as a control channel to send requests to schedule high-rate Ka-band single access service for data transfer. Though both services were provided by the TDRSS, the high-performance Ka-band link is capable of supporting data rates 5,000 times greater than those of the S-band multiple access system [38].”, the Event Manger as disclosed above may switch from a low-rate S-band communication to the high-rate KA-band communication in order to communication with the ground station/relay satellite as shown in figure 16.5 of Chelmins above. The high-rate KA-band is required for communication with the satellite/ground station network.)
wherein the establishing is based on the artificial intelligence input; (Chelmins, page 6, section 16.4.2., “A new approach is to implement a cognitive engine that decides when to change modulation, coding, and transmission power based on observed channel conditions and mission platform constraints.”, here, “establishing based on the artificial intelligence input” maps directly to the use of a cognitive engine, which bases its decisions on observed conditions. The cognitive engine serves as the AI input that triggers changes in waveform parameters, aligning with the claim language.)
wherein the preceding waveform is configured for line-of-sight (LOS) conditions and (Chelmins, page 12, paragraph 2, “An on-orbit experiment using NASA’s SCaN Testbed demonstrated the concept with TDRSS. The low-rate S-band multiple access service was used as a control channel …”, in this case the “preceding waveform” is mapped to the “low-rate S-band multiple access service” that acts as the control channel, and though the quote does not state “line-of-sight,” it is interpreted by the examiner that S-band control channels are ground-based and require a direct line-of-sight path. This indirect teaching satisfies the claim’s limitation that the preceding waveform is configured for line-of-sight conditions. )
wherein the selected waveform corresponding to the link to the network comprises a satellite-based waveform (Chelmins, page 12, paragraph 2, “… the high-rate Ka-band single access service for data transfer ”, the selected waveform (the high-rate Ka-band) is delivered via satellite relays which directly teaches the claim’s language.)
The rationale to combine Rondaeu with Chelmins is the same as the motivation set forth above for claim 1.
Shao teaches the following limitations which Rondeau and Kuru fail to teach:
wherein the switching is coordinated, ((Shao, claim 1 mentions “A method of providing coordination for managing communication of information over one or more wireless channels in a network including multiple wireless stations of different types operating at the same wireless frequency band but at different waveform formats” this method can be used to coordinate waveform switching as disclosed in the specification))
wherein the preceding waveform and the selected waveform are communicating a same message, (Shao, Page 3 paragraph 1 mentions “The XG program…is a dynamic spectrum access (DSA) system that provides seamless communications while changing frequencies to keep from interfering with other networks as well as taking interference.” Where seamless communication is referring to the waveform not having a delay or interrupting at least one characteristic of the same message)
wherein the selected waveform and the preceding waveform are communicating the output signal (Figure 6.1, displayed above, shows that all waveforms (preceding and selected) are output from the cognitive engine as the final output signal, furthermore, Rondeau page 30, paragraph 3, “The mixed signal is then stored in a file “output.dat" and passed to a USRP sink to transmit over the air.”, shows that the signal created from the cognitive engine is stored in an output file and passed to USRP (Universal Software Radio Peripheral) to transmit over the air),
The rationale to combine Rondaeu with Shao is the same as the motivation set forth above for claim 1.
Kuru teaches the following limitation which Rondeau fails to teach:
wherein the operational data comprises geographical data, wherein the geographical data comprises terrain data (Kuru, paragraph 54, “In a further example, the receiving sensor data associated with a sensor that detects a status or state of an object or portion of the physical environment may include, e.g., receiving sensor data from a Radar or LiDAR that performs detection, ranging, bearing or location determination of an object or portion of the physical environment.”, the sensor data which comprises the ASRI data is fed into the machine learning model. The sensor data includes geographical terrain data modeled by a Radar or LiDAR system. It is well known in the art that LiDAR has terrain mapping of an environment and therefore reads upon this limitation.).
The rationale to combine Rondaeu with Kuru is the same as the motivation set forth above for claim 1.
Fang, in the same field of waveform communication, teaches the following limitations which the above prior art fails to teach:
wherein the switch of the communication sub-system from utilizing the preceding waveform to utilizing the selected waveform corresponding to the network is configured to be indicated at least one of audibly or visually to a pilot. (Fang, paragraph 93, “According to some embodiments, the method may further include: receiving a first switch input for the waveform associated with the audio information; displaying the real-time recognized text information on the user interface of the electronic device in response to the first switch input; receiving a second switch input for the real-time recognized text information; and displaying a waveform associated with the audio information on the user interface of the electronic device in response to the second switch input.”, a display is used to indicate to the user (the pilot) of a switch in radio waveform.)
The rationale to combine Rondaeu with Kuru is the same as the motivation set forth above for claim 1.
Claim 15: Rondeau, Kuru, Chelmins, Shao, and Fang teaches the method of claim 14. Rondeau further teaches:
wherein the determining the artificial intelligence input based on the operational data further comprises at least one of: processing the operational data (Page 12 paragraph 1 mentions “When the sensor receives a request, it performs its data collection process…then packages the data into an eXtensible Markup Language (XML) format to describe the sensor data. The XML data is transmitted to the cognitive controller,”);
developing a machine learning model via the processed data (Page 14 section 2.3.3 mentions “The decision maker uses the sensor information to determine if reconfiguration is required due to poor performance or signs of decreasing performance. If optimization is required, the decision maker should also provide some context, such as an optimization goal (e.g., high throughput or low battery consumption) or a time limit for when a new waveform is required.”);
training the machine learning model (Page 22 section 2.5.2 mentions Hidden Markov Models that can be used in the optimization process as a training method, “The idea of developing such a model lends itself to cognitive radios. Rieser and I looked into using HMM’s in channel models using genetic algorithms as the training method over the Baum-Welch algorithm”);
evaluating the machine learning model (Page 59 section 5.1 mentions an evaluation function which is commonly used by genetic algorithms to determine the fitness of a chromosome, “The evaluation stage develops a ranking metric of chromosome fitness for each individual, which then determines their survival to the next generation” genetic algorithms are used here in this example for their optimization capabilities and robust search); and
tuning one or more parameters of the machine learning model (Page 75 chapter 6 describes the use of Case-based learning for decision making in cognitive radios, “Decisions can include what parameters to adapt, if adaption is required, or even the method by which to adapt.”);
and applying query data to the machine learning model to generate the artificial intelligence input (Page 83 paragraph 1 mentions “each sensor could be queried in this manner to find cases from each domain that represents the problem”).
Conclusion
THIS ACTION IS MADE FINAL.
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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.
The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HYUNGJUN B YI whose telephone number is (703)756-4799. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Jung can be reached on (571) 270-3779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.B.Y./Examiner, Art Unit 2124
/SHAHID K KHAN/Primary Examiner, Art Unit 2146