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 in response to the application filed on 15 January 2024.
Claims 38-57 are under examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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
Claims 38-43 and 48-53 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zhu et al. (US Publication 2023/0309144) in view of Shahi et al. (US Publication 2022/0394712).
With respect to claims 38 and 48, Zhu teaches A user equipment (UE) (UE, figure 1) comprising:
at least one processor; (processor, Paragraph 32) and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, (memory, Paragraph 32) cause the UE to perform the following operations:
obtaining sidelink resources of a sidelink channel for radio-frequency sensing from a network node; (the BS 5170 may employ the transmission (step 5618) of the common sensing ICS signal carrying DL communication to the first UE 5110A to transmit (step 5610) an ICS configuration indication to the first UE 5110A indicating that the ICS configuration strategy relates to a sensing-only ICS signal, paragraph 603)
transmitting, using the sidelink resources, a Frequency-Modulated Continuous Wave (FMCW) sidelink sensing signal; (The second UE 5110B then transmits (step 6118) SL communication to the first UE 5110A and the first UE 5110A transmits (step 6120) SL communication to the second UE 5110B. In particular, the second UE 5110B may employ the FMCW waveform when transmitting (step 51118) the SL communication, paragraph 605)
receiving, at the UE, a signal comprising at least one reflection of the transmitted FMCW sidelink sensing signal that has been passively reflected by at least one object within a physical environment, wherein the object does not actively retransmit the sidelink sensing signal; (the first UE 5110A may then carry out passive sensing (step 6122) by receiving and processing reflections of the FMCW waveform transmitted, by the second UE 5110B, in step 6118. As a result of carrying out (step 6122) the SL-based, bi-static, dedicated passive sensing, the first UE 110A may obtain more accurate information about the target of interest, paragraph 605. Examiner note: bi-static passive sensing is a novel radar technology that passively detects targets without actively emitting signals)
determining sidelink channel information based at least on a received power, phase, and Doppler shift of the received passively reflected signal; (The sensor system records each of the radar echoes to allow for processing. The processing of the information in these recorded radar echoes leads to a condensing of the information down to a limited set of features. The features may, for example, include: round-trip delay; angle of arrival; Doppler shift; and received power, paragraph 434)
Zhu doesn’t teach inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold; determining, based on an output of the neural network model, that a predicted value of a radio network performance parameter of the UE-network node radio link will fall below a threshold within the predefined time threshold; and transmitting, to the network node, a flag indicating the predicted decrease in radio network performance to enable the network node to perform a corrective action prior to degradation of the radio link.
Shahi teaches inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold; (the UE 120 includes means for determining a set of inputs to a neural network configured to predict RF channel conditions or a user context associated with the UE 120, wherein the set of inputs includes historical data related to a wireless environment, a communication pattern, or a behavior pattern associated with the UE, Paragraph 43)
determining, based on an output of the neural network model, that a predicted value of a radio network performance parameter of the UE-network node radio link will fall below a threshold within the predefined time threshold; (the neural network model may use the set of inputs, which may include historical data and/or one or more current observations related to the wireless environment, communication pattern, and/or behavior pattern associated with the UE, to predict RF channel conditions and/or a user context over a future duration (e.g., the next X milliseconds), Paragraph 58. reporting the predicted communication parameter(s) associated with the new observation may be based at least in part on a target variable value having a particular label (e.g., classification and/or categorization), may be based at least in part on whether a target variable value satisfies one or more thresholds (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, and/or falls within a range of threshold values), and/or may be based at least in part on a cluster in which the new observation is classified, paragraph 72) and
transmitting, to the network node, a flag indicating the predicted decrease in radio network performance to enable the network node to perform a corrective action prior to degradation of the radio link. (reporting the predicted communication parameter(s) associated with the new observation, Paragraph 72. Examiner note: “to enable the network node to perform a corrective action prior to degradation of the radio link” is intended use)
Thus it would have been obvious to one of ordinary skill in the art at the time of the invention to implement system of Zhu with inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold as taught by Shahi. The motivation for combining Zhu and Shahi is to be able to determine an optimal number of aggregated carriers to maximize one or more of a power parameter or a performance parameter.
With respect to claims 39 and 49, Zhu teaches wherein the determined channel information further comprises the following: a value of at least one channel-related parameter, including a value of: an amplitude, the received power, a reference signal received power (RSRP), and a received signal strength of the sidelink reference signal; a channel state information (CSI), including a channel quality indicator (CQI), a rank indicator (RI), CSI-RS resource indicator (CRI), SS and PBCH resource block indicator (SSBRI), Layer Indicator (LI) and a precoder matrix indicator (PMI). (The sensor system records each of the radar echoes to allow for processing. The processing of the information in these recorded radar echoes leads to a condensing of the information down to a limited set of features. The features may, for example, include: round-trip delay; angle of arrival; Doppler shift; and received power, paragraph 434)
With respect to claims 40 and 50, Zhu doesn’t teach wherein the predefined time threshold is less than or equal to 500 milliseconds from receiving the sidelink channel information.
Shahi teaches wherein the predefined time threshold is less than or equal to 500 milliseconds from receiving the sidelink channel information. (the neural network model may use the set of inputs, which may include historical data and/or one or more current observations related to the wireless environment, communication pattern, and/or behavior pattern associated with the UE, to predict RF channel conditions and/or a user context over a future duration (e.g., the next X milliseconds), Paragraph 58)
Thus it would have been obvious to one of ordinary skill in the art at the time of the invention to implement system of Zhu with inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold as taught by Shahi. The motivation for combining Zhu and Shahi is to be able to determine an optimal number of aggregated carriers to maximize one or more of a power parameter or a performance parameter.
With respect to claims 41 and 51, Zhu doesn’t teach wherein the neural network model is trained using supervised learning based on sidelink channel information and corresponding reference signal received power (RSRP) measurements of the radio link between the UE and the network node received within the predefined time threshold.
Shahi teaches wherein the neural network model is trained using supervised learning based on sidelink channel information and corresponding reference signal received power (RSRP) measurements of the radio link between the UE and the network node received within the predefined time threshold. (Inputs to the neural network model may include historical data related to a wireless environment associated with the UE, a communication pattern associated with the UE, and/or a behavior pattern associated with the UE. For example, in some aspects, the historical data may include a measurement history related to RF channel conditions in a wireless environment associated with the UE, such as an SNR measurement history, a signal-to-interference-plus-noise ratio (SINR) measurement history, an RSRP measurement history, and/or an RSRQ measurement history for a current serving cell and/or one or more neighbor cells. Furthermore, in some aspects, the historical data may include a reference signal history. For example, the UE may be configured to receive and measure one or more reference signals (e.g., a channel state information reference signal (CSI-RS) and/or a DMRS) in order to perform downlink channel estimation, whereby the historical data input to the neural network model may include a CSI-RS, DMRS, and/or other suitable reference signal history that can be used to estimate and/or predict complex fluctuations and/or trends in RF channel conditions, Paragraph 56)
Thus it would have been obvious to one of ordinary skill in the art at the time of the invention to implement system of Zhu with inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold as taught by Shahi. The motivation for combining Zhu and Shahi is to be able to determine an optimal number of aggregated carriers to maximize one or more of a power parameter or a performance parameter.
With respect to claims 42 and 52, Zhu teaches wherein the sidelink channel information input to the neural network model consists of Doppler shift measurements derived from the passively reflected FMCW sidelink sensing signal. (The sensor system records each of the radar echoes to allow for processing. The processing of the information in these recorded radar echoes leads to a condensing of the information down to a limited set of features. The features may, for example, include: round-trip delay; angle of arrival; Doppler shift; and received power, paragraph 434)
With respect to claims 43 and 53, Zhu doesn’t teach wherein the predicted value of the radio network performance parameter comprises a predicted reference signal received power (RSRP) of the radio link between the UE and the network node falling below a configured RSRP threshold.
Shahi teaches wherein the predicted value of the radio network performance parameter comprises a predicted reference signal received power (RSRP) of the radio link between the UE and the network node falling below a configured RSRP threshold. (Inputs to the neural network model may include historical data related to a wireless environment associated with the UE, a communication pattern associated with the UE, and/or a behavior pattern associated with the UE. For example, in some aspects, the historical data may include a measurement history related to RF channel conditions in a wireless environment associated with the UE, such as an SNR measurement history, a signal-to-interference-plus-noise ratio (SINR) measurement history, an RSRP measurement history, and/or an RSRQ measurement history for a current serving cell and/or one or more neighbor cells, Paragraph 56)
Thus it would have been obvious to one of ordinary skill in the art at the time of the invention to implement system of Zhu with inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold as taught by Shahi. The motivation for combining Zhu and Shahi is to be able to determine an optimal number of aggregated carriers to maximize one or more of a power parameter or a performance parameter.
Claims 44-47 and 54-57 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zhu et al. (US Publication 2023/0309144) in view of Shahi et al. (US Publication 2022/0394712) further in view of Lunardi et al. (US Publication 2023/0403606)
With respect to claims 44 and 54, Zhu in view of Shahi doesn’t teach wherein transmitting the flag indicating the predicted decrease in radio network performance comprises transmitting a single-bit indication without transmitting a predicted parameter value or magnitude of change.
Lunardi teaches wherein transmitting the flag indicating the predicted decrease in radio network performance comprises transmitting a single-bit indication without transmitting a predicted parameter value or magnitude of change. (the feedback information may be a ‘1-bit flag’ per predicted value of a measurement quantity in the second network node, Paragraph 167)
Thus it would have been obvious to one of ordinary skill in the art at the time of the invention to implement system of Zhu in view of Shahi with transmitting the flag indicating the predicted decrease in radio network performance comprises transmitting a single-bit indication without transmitting a predicted parameter value or magnitude of change as taught by Lunardi. The motivation for combining Zhu, Shahi and Lunardi is to be able to improve resource usage across multiple nodes in a RAN, reduced interference, and improved user experience.
With respect to claims 45 and 55, Zhu teaches wherein the passively reflected sidelink sensing signal is reflected by an object that does not include an active transceiver, reconfigurable intelligent surface, or relay functionality. (the first UE 5110A may then carry out passive sensing (step 6122) by receiving and processing reflections of the FMCW waveform transmitted, by the second UE 5110B, in step 6118. As a result of carrying out (step 6122) the SL-based, bi-static, dedicated passive sensing, the first UE 110A may obtain more accurate information about the target of interest, paragraph 605. Examiner note: bi-static passive sensing is a novel radar technology that passively detects targets without actively emitting signals)
With respect to claims 46 and 56, Zhu teaches wherein the sidelink resources used for transmitting the FMCW sidelink sensing signal are explicitly allocated by the network node in response to a sensing request transmitted by the UE. (The ICS configuration indication can, alternatively, be transmitted (step 5410) from the BS 5170 and received (step 5412) by the UE 5110, even if the UE 5110 were to perform the sensing. This may be related to the scenario in which the sensing is requested or instructed by the BS 5170 to be performed by the UE 5110, paragraph 533)
With respect to claims 47 and 57, Zhu doesn’t teach wherein the corrective action performed by the network node comprises initiating a handover of the UE to a different cell prior to degradation of the radio link below a minimum performance threshold.
Shahi teaches wherein the corrective action performed by the network node comprises initiating a handover of the UE to a different cell prior to degradation of the radio link below a minimum performance threshold. (the neural network may be configured to output information that indicates an optimal carrier aggregation configuration for the future duration based on the predicted RF channel conditions and/or user context expected to occur over the future duration, Paragraph 51)
Thus it would have been obvious to one of ordinary skill in the art at the time of the invention to implement system of Zhu with inputting the sidelink channel information to a neural network model stored at the UE, the neural network model having been trained to map sidelink channel information to future radio network performance of a radio link between the UF and the network node within a predefined time threshold as taught by Shahi. The motivation for combining Zhu and Shahi is to be able to determine an optimal number of aggregated carriers to maximize one or more of a power parameter or a performance parameter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Duan et al. (US Publication 2019/0313451) discloses The RISes 520, 521 are artificial structures with engineered electromagnetic (EM) properties. The RISes 520, 521 are configured to receive wireless signals from a transmitter (e.g., a base station or UE) and passively beamform and retransmit (e.g., without power amplification) the received signals via one or more beams, with the retransmitted signals referred to as reflected signals, toward a receiver (e.g., a base station or UE).
Zhang et al. (US Publication 2022/0091227) discloses the first wireless device transmits one or more radar pulses based at least in part on a radar transmission order being based on the first radar-detection information. In another aspect, a UE receives radar-detection information from multiple wireless devices. The UE transmits a radar transmission order to the multiple wireless devices based on the radar-detection information.
Any inquiry concerning this communication from the examiner should be directed to ABDULLAHI AHMED whose telephone number is (571) 270-3652. The examiner can normally be reached on M-F 8:00AM-4:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khalid Kassim can be reached on 571-270-3370. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDULLAHI AHMED/Examiner, Art Unit 2475
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