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 .
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.
Response to Arguments
Applicant's arguments filed 02/06/2026 regarding the prior art rejections of Claims 1 – 20 have been fully considered, but they are not persuasive.
The Examiner disagrees O’ Shea does suggest and teach the subject matter of claim 1. O’ Shea teaches both downlink and machine learning including labels. (0007 & 0012, the method includes receiving downlink data for a user device in response to sending the recovered data of the RF uplink signals to the destination device; and controlling transmission of an RF data downlink signal from the radio unit to the user device, the RF data downlink signal encoding the downlink data. training the first machine learning model and the second machine learning model in a joint training process. Training data in the joint training process includes RF resource grids, and labels for the training data include ground-truth inferred bits corresponding to the RF resource grids or ground-truth recovered data corresponding to the RF resource grids.)
The Examiner disagrees O’ Shea teaches training at the network side and the WTRU. (0192, the described processes, configurations of neural networks, and methods of neural network training/retraining can be performed on a user device (e.g., a smartphone, a wearable device, a personal computer, a connected vehicle, etc.), for receiving RF signals from a RAN RU and/or transmitting RF signals to a RAN RU. For example, for device-side processing, the above-described uplink signal processing can instead be downlink processing. For example, instead of input data being a sample of an uplink RF signal (e.g., a PUSCH resource grid), the input data can be a sample of a downlink RF signal (e.g., a resource grid of a downlink channel such as the physical downlink shared channel (PDSCH), the physical downlink control channel (PDCCH), or a downlink sounding signal for the process 400))
The Examiner agrees Levitsky's training does not AI/MlL-based joint receiver functions at the WTRU. However, Levitsky does teach downlink and configuration information for data allocation. (0145 & 0162, FIG. 4 may illustrate a single resource block including a number of physical downlink control channel (PDCCH) symbols 405; the transmitting device may influence the ratio between the resource allocation for data signaling and the resource allocation for control or pilot signaling;) In combination with O’Shea which teach machine learning, one skilled in the art could conclude the invention claimed.
The Examiner disagrees Carsello does cure the deficiencies of O'Shea and Levitsky. Carsello’s teaching of “Hopping is based on a defined pseudo random sequence” (0005) in combination with the teachings of data allocation of Levitsky and the machine learning of O'Shea one skilled in the art could conclude the invention claimed.
Claims 2 – 10 which depend from amended claim 1, have been considered and rejected.
Claims 12 – 20 which depend from amended claim 11, have been considered and rejected.
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.
Claim(s) 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over O'Shea (US 2023/0342590 A1) in view of Levitsky (US 2021/0376898 A1) in view of CARSELLO (US 2020/0112395 A1).
In regards to claim 1, O’Shea teaches:
A wireless transmit/receive unit (WTRU) comprising a processor, the processor configured to: receive configuration information, the configuration information comprising an indication of a first receiver function training allocation type, a second receiver function training allocation type, or a third receiver function training allocation type (0043, The neural receiver includes one or more machine learning models configured to receive, as input, samples of radio-frequency (RF) uplink signals received at a radio unit of the RAN, and provide, as output, at least one of channel estimates or recovered data corresponding to the RF uplink signals), wherein the first receiver training allocation type is associated with user data allocations, the second receiver training allocation type is associated with…, and third receiver function training allocation type is associated with user data allocations and PRD allocations (0085, A computing system of the RU 102 can be configured to perform L1 functions; a computing system of the DU 104 can be configured to perform L1 and/or L2 functions; and a computing system of the CU 106 can be configured to perform L2 and/or L3 functions. Combinations of two or more of these and/or other computing systems); receive a downlink transmission that comprises at least one of data allocations or PRD allocations (0141, The traffic queues 502 include respective downlink data for transmission to each user device, e.g., in the form of packets with information about which user device is to receive which packets. For example, the downlink data can be obtained from the Internet, e.g., with commands/instructions/labels indicating target user devices to receive different portions of downlink data); generate labels based on the received downlink transmission, wherein, when the configuration information comprises the first receiver function training allocation type, the labels are generated using the received data allocations, and wherein, when the configuration information comprises the second receiver function training allocation type, the labels are generated using the received PRD allocations, the labels are generated using both the received data allocations and the received PRD allocations (0012 & 0085, Training data in the joint training process includes RF resource grids, and labels for the training data; A computing system of the RU 102 can be configured to perform L1 functions; a computing system of the DU 104 can be configured to perform L1 and/or L2 functions; and a computing system of the CU 106 can be configured to perform L2 and/or L3 functions. Combinations of two or more of these and/or other computing systems can be referred to collectively as a computing system); train an artificial intelligence / machine learning (AI/ML) model based on the labels; (0012, Training data in the joint training process includes RF resource grids, and labels for the training data include ground-truth inferred bits corresponding to the RF resource grids or ground-truth recovered data corresponding to the RF resource grids.) associated with the AI/ML model (0012, the xApp 922 is configured to output messages to the L2 layer 906 of the DU (e.g., using the E2 interface) to control scheduling of resources in spectral and/or spatial dimensions, e.g., which can control operations of the downlink scheduling component 916. These messages can, for example, dictate the avoidance of certain time and/or frequency intervals which are known to have interference or which should be avoided for other reasons. As another example, the messages can include spatial information about interferers or other locations in space which should be avoided or suppressed during spatial processing or towards which it is desired to minimize or maximize the radiation of various energy within the RAN system.)
O’Shea fails to teach:
and wherein, when the configuration information comprises the third receiver function training allocation type; and send a status message to a network, the status message comprising an indication of whether training is complete;
However, Levitsky teaches:
and wherein, when the configuration information comprises the third receiver function training allocation type (0182, the transmitting device may select a third DMRS configuration for the first CDM group based on the first DMRS configuration selected by the wireless device and network scheduling criteria or constraint); and send a status message to a network, the status message comprising an indication of whether training is complete (0141 & 0189, the transmitting device may indicate the specific DMRS configuration from the activated list for each TB or CDM group to the wireless device via a field in a downlink control message, such as DCI. Additionally or alternatively, the base station 605 may transmit a message that directs the UE 615 to use a CSF reporting format that supports indicating a DMRS configuration for each TB or each CDM group).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of O’Shea which teaches a wireless unit to receive function training configurations and allocation types with the teaching of Levitsky which teaches third receiver function training allocation type and sending messages to a network in order to communicate data between devices (Levitsky: 0142, a downlink control message).
O’Shea in view of Levitsky fails to teach:
pseudo-random data (PRD) allocations;
However, CARSELLO teaches:
pseudo-random data (PRD) allocations (0005, Hopping is based on a defined pseudo random sequence based upon the device address of the Master);
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of O’Shea which teaches a wireless unit to receive function training configurations and allocation types with the teaching of CARSELLO which teaches pseudo-random data allocation in order to communicate data between devices (CARSELLO: 0005, a “Master” device, which initiates an exchange of data, and a “Slave” device which responds to the Master).
In regards to claim 2, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 1.
O’Shea further teaches:
wherein the processor is configured to: send a request for online training of an artificial intelligence/machine learning (AI/ML)-based joint receiver function; and report a capability associated with the WTRU, the capability associated with label generation (0174, Training and/or retraining based on sparse data can reduce complexity in training (e.g., reducing computational resources consumed and/or increasing training speed), to facilitate easier deployment and/or online tuning of neural networks such as networks 310, 316, and 410 in cases where wireless signals incorporate pilot signals, reference signals, or sounding signals).
In regards to claim 3, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 1.
O’Shea further teaches:
wherein the configuration information comprises the first receiver function training allocation type, and wherein the processor is configured to generate the labels by re-encoding decoded bits associated with the data allocations (0084, The RAN architecture 100 can be functionally divided into L1, L2, and L3 layers. L1, sometimes referred to as the “real-time” or “physical” layer, involves radio frequency (RF) reception and transmission, e.g., between user devices (e.g., 3GPP user equipment (UE) and the RU 102, along with low-level signal processing such as modulation, encoding, or filtering, among others).
In regards to claim 4, O’Shea further teaches the WTRU of claim 3.
O’Shea in view of Levitsky fails to teach:
wherein the processor is configured to determine if there is an error in a cyclic redundancy check (CRC), and wherein the processor is configured to generate the labels based on there being no error in the CRC.
However, CARSELLO teaches:
wherein the processor is configured to determine if there is an error in a cyclic redundancy check (CRC), and wherein the processor is configured to generate the labels based on there being no error in the CRC (0011, used to inform the source of a successful transfer of payload data with cyclic redundancy check (CRC)).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of O’Shea which teaches a wireless unit to receive function training configurations and allocation types with the teaching of CARSELLO which teaches cyclic redundancy check (CRC) in order to error check between devices (CARSELLO: 0067, to increase the likelihood that data is received successfully).
In regards to claim 5, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 1.
O’Shea teaches:
wherein the configuration information comprises the second receiver function training allocation type, and wherein the processor is configured to generate the labels with a PRD generator (0043 & 0012, The neural receiver includes one or more machine learning models; Training data in the joint training process includes RF resource grids, and labels for the training data).
In regards to claim 6, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 5.
O’Shea further teaches:
and generate the labels with the PRD generator based on the seed associated with the PRD allocations (0012, Training data in the joint training process includes RF resource grids, and labels for the training data).
O’Shea in view of Levitsky to teach:
wherein the processor is configured to: receive a seed associated with the PRD allocations from the network;
However, CARSELLO teaches:
wherein the processor is configured to: receive a seed associated with the PRD allocations from the network (0019, using an a priori pseudorandom sequence in block 820, based upon the 8-bit UAP. Hence the whitening starts at one of 64 possible starting locations or “seeds”);
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of O’Shea which teaches a wireless unit to receive function training configurations and allocation types with the teaching of CARSELLO which teaches receive a seed associated with the PRD in order to communicate data between devices (CARSELLO: 0019).
In regards to claim 7, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 1.
O’Shea in view of Levitsky fails to teach:
wherein the configuration information comprises the third receiver function training allocation type, and wherein the processor is configured to: receive a seed associated with the PRD allocations from the network; and generate the labels based on the seed associated with the PRD allocations and by re-encoding decoded bits associated with the data allocations.
However, CARSELLO teaches:
wherein the configuration information comprises the third receiver function training allocation type, and wherein the processor is configured to: receive a seed associated with the PRD allocations from the network; and generate the labels based on the seed associated with the PRD allocations and by re-encoding decoded bits associated with the data allocations (0019 & 0073, using an a priori pseudorandom sequence in block 820, based upon the 8-bit UAP. Hence the whitening starts at one of 64 possible starting locations or “seeds;” a sync word re-encoding into 64 bits can be performed).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of O’Shea which teaches a wireless unit to receive function training configurations and allocation types with the teaching of CARSELLO which teaches pseudo-random data and re-encoding bits in order to communicate data between devices (CARSELLO: 0005, a “Master” device, which initiates an exchange of data, and a “Slave” device which responds to the Master).
In regards to claim 8, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 1.
O’Shea further teaches:
wherein the processor is configured to: train an artificial intelligence/machine learning (AI/ML)-based joint receiver function based on the label (0012, Training data in the joint training process includes RF resource grids, and labels for the training data); associated with the AI/ML model (0012, the xApp 922 is configured to output messages to the L2 layer 906 of the DU (e.g., using the E2 interface) to control scheduling of resources in spectral and/or spatial dimensions, e.g., which can control operations of the downlink scheduling component 916. These messages can, for example, dictate the avoidance of certain time and/or frequency intervals which are known to have interference or which should be avoided for other reasons. As another example, the messages can include spatial information about interferers or other locations in space which should be avoided or suppressed during spatial processing or towards which it is desired to minimize or maximize the radiation of various energy within the RAN system.)
O’Shea in view of Levitsky fails to teach:
and transmit the indication that training is complete to the network based on the training of the AI/ML-based joint receiver function.
However, CARSELLO teaches:
and transmit the indication that training is complete to the network based on the training of the AI/ML-based joint receiver function (0141, the transmitting device may indicate the specific DMRS configuration from the activated list for each TB or CDM group to the wireless device via a field in a downlink control message, such as DCI.).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of O’Shea which teaches a wireless unit to receive function training configurations and allocation types with the teaching of CARSELLO which teaches sending messages to a network in order to communicate data between devices (CARSELLO: 0005, a “Master” device, which initiates an exchange of data, and a “Slave” device which responds to the Master).
In regards to claim 9, O’Shea in view of Levitsky in view of CARSELLO teaches the WTRU of claim 1.
O’Shea further teaches:
wherein the data allocations comprise data resource elements and the PRD allocations comprise PRD resource elements (0012, labels for the training data include ground-truth inferred bits corresponding to the RF resource grids or ground-truth recovered data corresponding to the RF resource grids).
In regards to claim 10, O’Shea teaches the WTRU of claim 9.
O’Shea further teaches:
the WTRU further comprising memory, wherein the processor is configured to store one or more of the data resource elements, the PRD resource elements, or the labels in the memory (0196, The processor 1602 can process instructions for execution within the computing device 1600, including instructions stored in the memory 1604 or on the storage device 1606 to display graphical information for a GUI on an external input/output device).
In regards to claim 11, the claim corresponds to claim 1 as analyzed accordingly.
In regards to claim 12, teaches the method of claim 11 the claim corresponds to claim 2 as analyzed accordingly.
In regards to claim 13, teaches the method of claim 11 the claim corresponds to claim 3 as analyzed accordingly.
In regards to claim 14, teaches the method of claim 13 the claim corresponds to claim 4 as analyzed accordingly.
In regards to claim 15, teaches the method of claim 11 the claim corresponds to claim 5 as analyzed accordingly.
In regards to claim 16, teaches the method of claim 15 the claim corresponds to claim 6 as analyzed accordingly.
In regards to claim 17, teaches the method of claim 11 the claim corresponds to claim 7 as analyzed accordingly.
In regards to claim 18, teaches the method of claim 11 the claim corresponds to claim 8 as analyzed accordingly.
In regards to claim 19, teaches the method of claim 11 the claim corresponds to claim 9 as analyzed accordingly.
In regards to claim 20, teaches the method of claim 19 the claim corresponds to claim 10 as analyzed accordingly.
Prior Art Made of Record
The prior art mode of record and not relied upon is considered pertinent to
Applicant’s disclosure:
Gupta (US 2020/0349435 A1): A deep neural network may be trained on the data of one or more entities, also know as Alices. An outside computing entity, also known as a Bob, may assist in these computations, without receiving access to Alices' data. Data privacy may be preserved by employing a “split” neural network. The network may comprise an Alice part and a Bob part. The Alice part may comprise at least three neural layers, and the Bob part may comprise at least two neural layers.
Response to Arguments
Applicant's arguments filed 02/06/2026 regarding the prior art rejections of Claims 1 – 20 have been fully considered, but they are not persuasive.
The Remarks argue that:
Claims 1-20 stand rejected under 35 U.S.C. § 103 as being allegedly unpatentable over
US 2023/0342590 ("O'Shea"), in view of US 2021/0376898 ("Levitsky"), and in further view of US 2020/0112395 ("Carsello"). Applicant respectfully disagrees and traverses.
O'Shea, considered alone or in combination with Levitsky or Carsello, does not teach or suggest the subject matter that claim 1 requires. Claim 1 requires a WTRU configured to "generate labels based on the received downlink transmission", "train an artificial intelligence / machine learning (AI/MIL) model based on the generated labels...and send a status message to a network... comprising an indication of whether training associated with the AI/ML model is complete," as recited by claim 1.
O'Shea's training is performed at the network side (e.g., at a training component in the RIC or DU), not at the WTRU. O'Shea discloses a network configured to generate labels based on, for example, uplink data (e.g., O'Shea, para. [0116] reciting "training data can be labeled with ... uplink data 322[,]"). In O'Shea, a user equipment (UE) is in communication with the network RAN architecture (O'Shea, paragraph [0085] reciting "radio frequency (RF) reception and transmission, e.g., between user devices (e.g., 3GPP user equipment (UE) and the RU 102..."). The RAN architecture includes, for example, a RAN intelligent controller (RIC) and a radio unit (RU) (e.g., O'Shea, para. [0080] reciting "[t]he RAN architecture 100 includes a radio unit (RU) 102, a RAN Intelligent Controller (MC) 112 ..."). As such, the RU and the computing systems of the RU in O'Shea reside at the network side. Rather, claim 1 requires a downlink transmission, which is a transmission sent from a network node to (e.g., RAN architecture of O'Shea), for example, a user equipment. Therefore, the network being configured to generate labels based on uplink data in O'Shea does not equate to a WTRU configured to "generate labels based on the received downlink transmission." (O'Shea, para. [0085]) (claim 1). As such, O'Shea does not teach or suggest the subject matter that claim 1 requires.
The Office Action concedes that O'Shea fails to teach "wherein, when the configuration information comprises the third receiver function training allocation type; and send a status message to a network, the status message comprising an indication of whether training is complete;" (Office Action, p. 3). The Office Action relies on Levitsky to cure the deficiencies of O'Shea. Applicant respectfully disagrees.
Levitsky's training is for DMRS configuration selection mappings, not for training AI/MlL-based joint receiver functions at the WTRU (e.g., see Levitsky, para. [0188] reciting "the UE 615 may perform link quality characteristic training to determine a mapping between a first link quality characteristic (e.g., a CSI-RS SINK) and a second link quality characteristic (e.g., a DMRS SINR)."). Levitsky does not teach or suggest configuration information comprising an indication of receiver function training allocation types associated with user data allocations, PRD allocations, or both, nor does it teach generating labels based on such allocation types or training an AI/ML model based on the generated labels. Therefore, Levitsky does not cure the deficiencies of O'Shea and Carsello.
Carsello does not cure the deficiencies of O'Shea and Levitsky. Carsello discloses subject matter with respect to blind decoding of data packets (Carsello, para. [0002] reciting "[t]he present disclosure relates to wireless communications and in particular to a method and system for classifying Classic Bluetooth packets and determining the most likely upper address parts (UAPs) and packet data units (PDUs)..."). Although Carsello discloses "[h]opping is based on a defined pseudo random sequence based upon the device address of the Master[,]" the pseudo random sequence of Carsello does not equate to the PRD allocations of claim 1. For example, Carsello discloses a "bit streaming processing 800 for encoding the 54-bit Classic Bluetooth Packet Header... The 18-bit information bit field is then whitened using an a priori pseudorandom sequence block 820..." (Carsello, para. [0019]). The hopping of Carsello is with respect to "each device [that] hops to an RF channel once per packet..." (Carsello, para. [0005]).
A person of skill in the art would not combine Carsello with O'Shea or Levitsky to arrive at the subject matter that claim 1 requires. Merely describing pseudo random sequence(s) with respect to hopping does not equate to a WTRU configured to "generate labels based on the received downlink transmission, wherein..., when the configuration information comprises the third receiver function training allocation type, the labels are generated using both the received data allocations and the received PRD allocations..." (claim 1). And, Carsello remains silent with respect to a WTRU configured to "train an artificial intelligence / machine learning (AI/ML) model based on the generated labels." (claim 1). Therefore, Carsello does not cure the deficiencies of O'Shea nor Levitsky.
Accordingly, claims 1 is patentable over the cited references. Claim 11 is amended to recite similar subject matter and is also patentable over the cited references. For at least the reason of depending on a respective patentable independent claim, the dependent claims are also patentable over the cited references. As such, Applicant respectfully requests that the rejections to claims 1-20 under 35 U.S.C. § 103 be reconsidered and withdrawn.
Conclusion
Applicant's arguments filed 02/06/2026 regarding the prior art rejections of Claims 1 – 20 have been fully considered, but they are not persuasive.
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.
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/V.P./Examiner, Art Unit 2111
/GUERRIER MERANT/Primary Examiner, Art Unit 2111 5/1/2026
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