Details
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Claim 1 recites receiving information comprising an identifier and data information and generating dataset based on the identifier such that data information associated with the same identifier has the same physical meaning. These limitations recite the abstract idea of collecting and organizing/classifying information according to an identifier, which constitutes a mental process involving observation, evolution and organization of information that can be practically performed in human mind or using pen and paper. See MPEP $2016.04(a)(2).
The claim additionally recites that the receiving and dataset generation are performed by a first device receiving information from a second device, that the information related to device communication and comprises beam information and the dataset is used for AI model processing.
These additional limitations do not integrate the judicial exception into a practical application. The recitation of receiving information from another device amounts to data gathering for use in performing the abstract information-organizing process. Restricting the information to wireless communication/ beam information merely limits the abstract idea to a particular technological environment. Further, stating that the resulting dataset is used for AI model training, inference, or monitoring does not recite a particular improvement to the operation of an AI model or to wireless communication technology, but merely identifies an intended technological use for the resulting dataset.
Considered individually and as ordered combination, the additional elements do not impose a meaningful limitation on the abstract information collection and organization and do not provide an inventive concept sufficient to amount to significantly more that the judicial exception. Accordingly, claim 1 is directed to patent-ineligible subject matter.
Claims 2-10 recite additional limitations. Considered individually and as ordered combination, the additional limitations of claims 2-10 do not impose a meaningful limitation on the abstract information collection and organization and do not provide an inventive concept sufficient to amount to significantly more that the judicial exception.
Claims 2 and 3 merely perform additional information analysis or predicting using an AI model.
Claims 4-10 mainly adds identify particular wireless information to be collected, identify the devices from which the information is obtained, specify measurement/reporting mechanisms, specify data-collection parameters, specify capability information, or identify particular wireless parameters forming the collected data.
These limitations do not require a specific technological mechanism that improves operation of an AI model, wireless device or communication network. Accordingly, the claims as a whole are still directed to patent-ineligible subject matter.
Claims 11-20 are rejected under the same rationale.
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.
Claims 1-2, 4-6, 8-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaya et al (Pub. No.: US 2022/0190883 A1) in view of Jeon et al (Pub. No.: US 2022/0287104 A1).
As per claim 1, Kaya discloses a dataset generation method, comprising: - receiving, by a first device (Kaya, base station, Fig 1 item 134), first information (Kaya, beam measurement report, Fig 1 item 134) from a second device (Kaya, User device, Fig 1 item 131) (Kaya, paragraph 0034-0035, wherein “For example, the serving beams may be updated based on UE measurements, reporting and beam recovery procedures as the UE moves or a propagation environment changes”, “A UE may perform a signal measurement (e.g., measure a reference signal received power (RSRP) or other signal measurement) for one or more received SSB beams/SSB resources, and then may determine or select a best or a highest power SSB beam (or a best set of N beams) and the associated SSB resource. There may be a different random access preamble associated with each SSB resource/SSB beam. The UE may then transmit to the BS a random access preamble associated with the selected SSB resource, as part of a random access procedure, in order to identify the best (or UE selected) SSB beam. Thus, a UE may indicate to the BS a best SSB beam (including a best BS transmit beam) based on the random access preamble transmitted to the BS. The UE may also determine a best UE receive beam for this best SSB transmit beam. Thus, an initial beam pair may be used by the BS and UE to perform a random access procedure, in order to establish a connection between the UE and BS”), wherein the first information comprises a first identifier and data information (Kaya, paragraph 0038, wherein “Thus, a UE may measure a signal parameter (e.g., RSRP, or other signal parameters) of a reference signal received on each of a plurality of these resources, where each resource is associated with a different beam. Thus, by reporting (e.g., in a measurement report) a RSRP (or other signal parameter) and a beam identifier or resource identifier for one or more of the resources, this may identify both the measured RSRP (or other signal parameter) and the associated beam to the BS” wherein the RSRP can be the data information and the beam identifier can be the first identifier), the data information being information obtained by processing information related to device communication (Kaya, paragraph 0034-0035, wherein “For example, the serving beams may be updated based on UE measurements, reporting and beam recovery procedures as the UE moves or a propagation environment changes”, “A UE may perform a signal measurement (e.g., measure a reference signal received power (RSRP) or other signal measurement) for one or more received SSB beams/SSB resources, and then may determine or select a best or a highest power SSB beam (or a best set of N beams) and the associated SSB resource. There may be a different random access preamble associated with each SSB resource/SSB beam. The UE may then transmit to the BS a random access preamble associated with the selected SSB resource, as part of a random access procedure, in order to identify the best (or UE selected) SSB beam. Thus, a UE may indicate to the BS a best SSB beam (including a best BS transmit beam) based on the random access preamble transmitted to the BS. The UE may also determine a best UE receive beam for this best SSB transmit beam. Thus, an initial beam pair may be used by the BS and UE to perform a random access procedure, in order to establish a connection between the UE and BS”) (Kaya, paragraph 0038, wherein “Thus, a UE may measure a signal parameter (e.g., RSRP, or other signal parameters) of a reference signal received on each of a plurality of these resources, where each resource is associated with a different beam. Thus, by reporting (e.g., in a measurement report) a RSRP (or other signal parameter) and a beam identifier or resource identifier for one or more of the resources, this may identify both the measured RSRP (or other signal parameter) and the associated beam to the BS”), and (Kaya, paragraph 0047, wherein “These past beam sequences for various UEs may thus be input to train a beam sequence model (e.g., which may be implemented as a neural network)”; Paragraph 0053-0054, wherein “In an example embodiment, the beam sequence model may include a neural network, the method further including: training the beam sequence model based on a past beam sequence for one or more user equipments (UEs). In an example embodiment, the beam sequence model may include a neural network, the method further including: performing, by the base station, the following for one or more past beam sequences for one or more user equipments (UEs): determining a past beam sequence for a first user equipment (UE); determining a first portion of the past beam sequence for the first UE as an input to the beam sequence model during training; determining a second portion, subsequent in time to the first portion, of the past beam sequence for the first UE as a correct output of the beam sequence model during training; determining a predicted future beam sequence output, during training, from the beam sequence model based on the first portion of the past beam sequence as an input to the beam sequence model; determining an error of the beam sequence model based on a comparison between the correct output of the beam sequence model during training and the predicted future sequence output by the beam sequence model during training; and adjusting one or more weights of the beam sequence model to reduce the error”; wherein the data including the training data and the predicted future beam sequence can be the generated dataset as claimed), wherein the dataset comprises data information associated with the first identifier (Kaya, paragraph 0047, wherein “These past beam sequences for various UEs may thus be input to train a beam sequence model (e.g., which may be implemented as a neural network)”; Paragraph 0053-0054, wherein “In an example embodiment, the beam sequence model may include a neural network, the method further including: training the beam sequence model based on a past beam sequence for one or more user equipments (UEs). In an example embodiment, the beam sequence model may include a neural network, the method further including: performing, by the base station, the following for one or more past beam sequences for one or more user equipments (UEs): determining a past beam sequence for a first user equipment (UE); determining a first portion of the past beam sequence for the first UE as an input to the beam sequence model during training; determining a second portion, subsequent in time to the first portion, of the past beam sequence for the first UE as a correct output of the beam sequence model during training; determining a predicted future beam sequence output, during training, from the beam sequence model based on the first portion of the past beam sequence as an input to the beam sequence model; determining an error of the beam sequence model based on a comparison between the correct output of the beam sequence model during training and the predicted future sequence output by the beam sequence model during training; and adjusting one or more weights of the beam sequence model to reduce the error”), the data information associated with the same first identifier has the same physical meaning (Kaya, paragraph 0047, wherein “These past beam sequences for various UEs may thus be input to train a beam sequence model (e.g., which may be implemented as a neural network)”; Paragraph 0053-0054, wherein “In an example embodiment, the beam sequence model may include a neural network, the method further including: training the beam sequence model based on a past beam sequence for one or more user equipments (UEs). In an example embodiment, the beam sequence model may include a neural network, the method further including: performing, by the base station, the following for one or more past beam sequences for one or more user equipments (UEs): determining a past beam sequence for a first user equipment (UE); determining a first portion of the past beam sequence for the first UE as an input to the beam sequence model during training; determining a second portion, subsequent in time to the first portion, of the past beam sequence for the first UE as a correct output of the beam sequence model during training; determining a predicted future beam sequence output, during training, from the beam sequence model based on the first portion of the past beam sequence as an input to the beam sequence model; determining an error of the beam sequence model based on a comparison between the correct output of the beam sequence model during training and the predicted future sequence output by the beam sequence model during training; and adjusting one or more weights of the beam sequence model to reduce the error”, the dataset is used for artificial intelligence (AI) model processing (Kaya, paragraph 0045, wherein “Thus, according to an example embodiment, a BS (e.g., gNB, a TRP or a radio access network (RAN) node) or other network node may use or employ an artificial intelligence (AI) neural network (which may be referred to as a neural network, a neural network model, an AI neural network model, an AI model, a machine learning model or algorithm, or other term) to implement a beam sequence model. The beam sequence model, e.g., which may be implemented as a neural network, or other model, may be trained, e.g., based on a past beam sequence for one or more UEs”), and the AI model processing comprises at least one of model inference of AI models, model training of AI models, and model monitoring of AI models (Kaya, paragraph 0047, wherein “At 430, step 3 may include Inference Mode For Prediction. For example, in this step, a past beam sequence for a UE may be input to the beam sequence model, e.g., to determine a predicted future beam sequence for the UE (e.g., predict a best k refined beams, and/or predict the TRP/BS, to best serve the UE/user over a next several hundred ms)”).Kaya does not explicitly show: - the first identifier being used to indicate the target processing manner. However, Jeon discloses wherein the first identifier being used to indicate the target processing manner (Jeon, paragraph 0095-0097, wherein “Logging into the Web GUI”, wherein “In one embodiment, the configuration information can include whether ML/AI techniques for certain operation/use case is enabled or disabled. One or multiple operations/use cases can be predefined. For example, there can be N predefined operations, with index 1, 2, . . . , N corresponding to one operation such as “UL channel prediction”, “DL channel estimation”, “handover”, etc., respectively. The configuration can indicate the indexes of the operations which are enabled, or there can be a Boolean parameter to enable or disable the ML/AI approach for each operation”).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate Kaya with Jeon so that the UE processed beam related information is identified according to Jeon’s predefined operation mode index, thereby indicting the target processing manner as claimed because this would help improve the performance of the system.
As per claim 2, claim 1 is incorporated and Kaya further discloses wherein after the generating, by the first device, a dataset based on the first identifier, the method further comprises: performing at least one of the following according to a processed AI model: beam prediction, channel prediction, channel quality prediction, mobility management, or quality of experience (QoE) prediction (Kaya, paragraph 0045, wherein “Thus, according to an example embodiment, a BS (e.g., gNB, a TRP or a radio access network (RAN) node) or other network node may use or employ an artificial intelligence (AI) neural network (which may be referred to as a neural network, a neural network model, an AI neural network model, an AI model, a machine learning model or algorithm, or other term) to implement a beam sequence model. The beam sequence model, e.g., which may be implemented as a neural network, or other model, may be trained, e.g., based on a past beam sequence for one or more UEs”);
As per claim 4, claim 1 is incorporated and Kaya further discloses wherein the information related to device communication comprises beam information of the second device, the first information is associated with a reference signal resource (Kaya, paragraph 0038, wherein “FIG. 2 is a diagram illustrating a different resource and an associated beam for each of a plurality of beams according to an example embodiment. In this illustrative example, a CSI-RS (channel state information-reference signal) signal is transmitted via different CSI-RS time-frequency resources and via associated CSI-RS beam. Thus, each CSI-RS beam is associated with a different CSI-RS resource. For example, beam 212 is associated with CSI-RS1 resource; beam 214 is associated with CSI-RS2 resource; beam 216 is associated with CSI-RS3 resource; beam 218 is associated with CSI-R41 resource, . . . and beam 220 is associated with CSI-RSn resource. This is merely an illustrative example, and any number of beams may be provided”), the dataset further comprises channel quality information obtained from channel measurement based on the beam information of the second device (Kaya, paragraph 0038-0040, wherein “BS may configure one or more parameters of the beam measurement reports to be provided by the UE to the BS. For example, the BS may send a measurement report configuration, or simply report configuration (which may be provided by BS to the UE as control information) indicating or updating one or more parameters or aspects of the beam measurement report to be provided by the UE to the BS. For example, the report configuration may indicate or update various parameters, such as a set of resources (e.g., a set of CSI-RS resources, or a set of SSB resources) to be measured, a number of resources/beams to be reported (e.g., configuring the UE to report a RSRP for best (strongest) two beams/resources, or the best 4 beams/resources), a frequency or timing of the measurement reports, and other parameters. Based on this report configuration, the UE may measure the set of resources (e.g., set of SSB or CSI-RS resources, with each resource associated with a beam) and report a signal measurement (e.g., RSRP or other signal measurements) for one or more resources/beams (e.g., report RSRP for the two or four strongest resources/beams) to the BS”), and the channel quality information is associated with the reference signal resource (Kaya, paragraph 0041, wherein “According to an example embodiment, CSI (channel state information) may include Channel Quality Indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), SS/PBCH Block Resource indicator (SSBRI), layer indicator (LI), rank indicator (RI) and/or L1-RSRP (reference signal received power). The CSI-RS resources may be periodic, semi-persistent, or aperiodic, for example”);
As per claim 5, claim 4 is incorporated and Kaya further discloses wherein the first device is a terminal, the second device is a network-side device, and the first information is sent through a measurement configuration; or the first device is a network-side device, the second device is a terminal, and the first information is sent through a measurement report (Kaya, paragraph 0039-0040, 0067, wherein “A BS may configure one or more parameters of the beam measurement reports to be provided by the UE to the BS. For example, the BS may send a measurement report configuration, or simply report configuration (which may be provided by BS to the UE as control information) indicating or updating one or more parameters or aspects of the beam measurement report to be provided by the UE to the BS. For example, the report configuration may indicate or update various parameters, such as a set of resources (e.g., a set of CSI-RS resources, or a set of SSB resources) to be measured, a number of resources/beams to be reported (e.g., configuring the UE to report a RSRP for best (strongest) two beams/resources, or the best 4 beams/resources), a frequency or timing of the measurement reports, and other parameters. Based on this report configuration, the UE may measure the set of resources (e.g., set of SSB or CSI-RS resources, with each resource associated with a beam) and report a signal measurement (e.g., RSRP or other signal measurements) for one or more resources/beams (e.g., report RSRP for the two or four strongest resources/beams) to the BS. For example, the UE may measure and report, every 10 ms, the RSRP of the two (or four, or other number of) refined beams (e.g., CSI-RS beams) having the highest measured RSRP. The BS may then, for example, switch its receive beam (to receive information or signals from the UE) and/or its transmit beam (to transmit information or signals to the UE) based on the information indicated in this measurement report (e.g., BS may switch to the CSI-RS beam having the highest RSRP)”);
As per claim 6, claim 1 is incorporated and Kaya further discloses wherein before the receiving, by the first device, first information from a second device, the method further comprises: sending, by the first device, second information to the second device, the second information comprising at least one of first indication information and second indication information; wherein the first indication information is used to indicate a data collection requirement, and the second indication information is used to indicate a data processing capability of the first device for the first information (Kaya, paragraph 0039, wherein “A BS may configure one or more parameters of the beam measurement reports to be provided by the UE to the BS. For example, the BS may send a measurement report configuration, or simply report configuration (which may be provided by BS to the UE as control information) indicating or updating one or more parameters or aspects of the beam measurement report to be provided by the UE to the BS. For example, the report configuration may indicate or update various parameters, such as a set of resources (e.g., a set of CSI-RS resources, or a set of SSB resources) to be measured, a number of resources/beams to be reported (e.g., configuring the UE to report a RSRP for best (strongest) two beams/resources, or the best 4 beams/resources), a frequency or timing of the measurement reports, and other parameters. Based on this report configuration, the UE may measure the set of resources (e.g., set of SSB or CSI-RS resources, with each resource associated with a beam) and report a signal measurement (e.g., RSRP or other signal measurements) for one or more resources/beams (e.g., report RSRP for the two or four strongest resources/beams) to the BS. For example, the UE may measure and report, every 10 ms, the RSRP of the two (or four, or other number of) refined beams (e.g., CSI-RS beams) having the highest measured RSRP. The BS may then, for example, switch its receive beam (to receive information or signals from the UE) and/or its transmit beam (to transmit information or signals to the UE) based on the information indicated in this measurement report (e.g., BS may switch to the CSI-RS beam having the highest RSRP)”);
As per claim 8, claim 6 is incorporated and Kaya further discloses wherein the data collection requirement comprises at least one of the following: a data collection object; or the number of data collections; wherein the number of data collections is consistent with the number of reference signal resources (Kaya, paragraph 0039, 0043, 0057, wherein “A BS may configure one or more parameters of the beam measurement reports to be provided by the UE to the BS. For example, the BS may send a measurement report configuration, or simply report configuration (which may be provided by BS to the UE as control information) indicating or updating one or more parameters or aspects of the beam measurement report to be provided by the UE to the BS. For example, the report configuration may indicate or update various parameters, such as a set of resources (e.g., a set of CSI-RS resources, or a set of SSB resources) to be measured, a number of resources/beams to be reported (e.g., configuring the UE to report a RSRP for best (strongest) two beams/resources, or the best 4 beams/resources), a frequency or timing of the measurement reports, and other parameters. Based on this report configuration, the UE may measure the set of resources (e.g., set of SSB or CSI-RS resources, with each resource associated with a beam) and report a signal measurement (e.g., RSRP or other signal measurements) for one or more resources/beams (e.g., report RSRP for the two or four strongest resources/beams) to the BS. For example, the UE may measure and report, every 10 ms, the RSRP of the two (or four, or other number of) refined beams (e.g., CSI-RS beams) having the highest measured RSRP. The BS may then, for example, switch its receive beam (to receive information or signals from the UE) and/or its transmit beam (to transmit information or signals to the UE) based on the information indicated in this measurement report (e.g., BS may switch to the CSI-RS beam having the highest RSRP)”);
As per claim 9, claim 8 is incorporated and Jeon further discloses wherein the information related to device communication comprises information about the data collection object (Kaya, paragraph 0038, wherein “FIG. 2 is a diagram illustrating a different resource and an associated beam for each of a plurality of beams according to an example embodiment. In this illustrative example, a CSI-RS (channel state information-reference signal) signal is transmitted via different CSI-RS time-frequency resources and via associated CSI-RS beam. Thus, each CSI-RS beam is associated with a different CSI-RS resource. For example, beam 212 is associated with CSI-RS1 resource; beam 214 is associated with CSI-RS2 resource; beam 216 is associated with CSI-RS3 resource; beam 218 is associated with CSI-R41 resource, . . . and beam 220 is associated with CSI-RSn resource. This is merely an illustrative example, and any number of beams may be provided”);
As per claim 10, claim 8 is incorporated and Jeon further discloses wherein the data collection object comprises at least one of the following: a beam pointing; a 3 dB beamwidth; a beam gain; an antenna pattern; a terminal moving speed; a terminal location; a base station location; cell line-of-sight (LOS) distribution; cell non-line-of-sight (NLOS) distribution; or an antenna configuration parameter (Jeon, paragraph 0104, wherein “For example, the set of states can include UE location, satellite location, UE trajectory, and/or satellite trajectory for DL channel estimation, or include UE location, satellite location, UE trajectory, satellite trajectory, and/or estimated DL channel for UL channel prediction, or include location, satellite location, UE trajectory, satellite trajectory, estimated DL channel, measured signal to interference plus noise ratio (SINK), reference signal received power (RSRP) and/or reference signal received quality (RSRQ), current connected cell, and/or cell deployment for handover operation, etc”);
Claims 11-15 and 17-20 are rejected under the same rationale as claims 1-2, 4-6, 8-10.
Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kaya et al (Pub. No.: US 2022/0190883 A1) in view of Jeon et al (Pub. No.: US 2022/0287104 A1) and Chen et al (Pub. No.: US 2019/372644 A1).
As per claim 3, claim 1 is incorporated and Kaya and Jeon do not explicitly disclose determining, by the first device, beam quality based on an AI model and the dataset. However, Chen discloses determining, by the first device, beam quality based on an AI model and the dataset (Chen, paragraph 0009 “receive, from a central controller (CC) via a base station (BS), information of a first beam that is used in a measurement report; and transmit, to the CC via the BS, the measurement report including a measurement result of the first beam. The first beam for the BS is selected, by the CC, from a set of beams in a candidate beam pool including predetermined candidate beams, the set of beams being allocated to the BS; consecutive measurement results corresponding to the set of beams in the candidate beam pool along with the measurement result of the first beam are preprocessed by the CC; beam scores for the first beam based on the measurement result of the first beam are calculated by the CC; and a second beam based on the beam scores is selected by the CC, the second beam being determined as a beam including a highest score among the set of beams in the candidate beam pool”)).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate Chen with Kaya and Jeon to achieve the claimed limitations because it would have improved the system by providing beam optimization using machine learning technology.
Claim 16 is rejected under the same rationale as claim 3.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kaya et al (Pub. No.: US 2022/0190883 A1) in view of Jeon et al (Pub. No.: US 2022/0287104 A1) and HONG et al (WO 2021/243619 “The English translation is used for the mapping”).
As per claim 7, claim 6 is incorporated and Kaya and Jeon do not explicitly disclose wherein the data processing capability of the first device for the first information comprises at least one of the following: whether to support generating a dataset using the first information; a maximum quantity of the data information supported; or maximum and minimum numerical values supported for processing. However, HONG discloses wherein the data processing capability of the first device for the first information comprises at least one of the following: whether to support generating a dataset using the first information; a maximum quantity of the data information supported; or maximum and minimum numerical values supported for processing (HONG, page 8 “In an embodiment, the computing capability parameters of the UE's processor include: The maximum calculation rate and/or the minimum calculation rate supported by the UE's processor. The UE can send the maximum calculation rate and/or the minimum calculation rate, and the base station determines the upper and lower limits of the AI capability of the UE, so that AI services suitable for the UE can be allocated”).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate HONG with Kaya and Jeon to achieve the claimed limitations because it would have improved the system by allowing collaboration between terminal-side AI and the cloud-side AI.
Conclusion
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/HAMZA N ALGIBHAH/Primary Examiner, Art Unit 2457
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