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 .
DETAILED OFFICE ACTION
This action is responsive to the communication received August 9th, 2024. Claims 1-20 have been entered and are presented for examination.
Application 18/799,274 is a Continuation of PCT/CN2023/075442 02/10/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 9th, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticpated by Yerramalli et al. (US 2021/0321221).
Regarding claims 1, 19-20, Yerramalli et al. discloses an information processing method (see Figure 10 [message flow between base station and mobile device]), comprising: obtaining, by a communication device, first information related to configuration information of a target artificial intelligence (AI) model (see Figure 10 and paragraph 0096 [ the base station 502 may determine a NN to use and provide the corresponding NN information (e.g., model/algorithm) to the UE 105 in one or more NN information messages 1010. In an example, the UE 105 may be configured with a plurality of local NN information and the base station 502 may provide an index to indicate which local NN information to use in the NN information messages 1010]), wherein the first information comprises measurement-related information (paragraph 0098 [the base station 502 may generate and transmit PRS signals 1102. The UE 105 may be configured to update NN weights in the local NN information using the ground truth of the previously stored conventional algorithm.]) and/or at least one candidate data processing policy (Limitation Not Selected); and determining, by the communication device, input data of the target AI model (paragraphs 0094, 0098 [the base station 502 may generate and transmit PRS signals 1102. The UE 105 may be configured to update NN weights in the local NN information using the ground truth of the previously stored conventional algorithm; CIR, SNR, CFR]) or a target data processing policy (Limitation Not Selected) based on the measurement-related information and/or each candidate data processing policy (Limitation Not Selected); wherein the target data processing policy is used to indicate a preprocessing policy for the measurement-related information or a preprocessing policy for the input data of the target AI model (MPEP 2111.04 [Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed]).
Regarding claim 2, Yerramalli et al. discloses all the recited subject matter in claim 1 and further discloses wherein the method further comprises: obtaining, by the communication device, the configuration information of the target AI model (see Figure 10 and paragraph 0096 [ the base station 502 may determine a NN to use and provide the corresponding NN information (e.g., model/algorithm) to the UE 105 in one or more NN information messages 1010. In an example, the UE 105 may be configured with a plurality of local NN information and the base station 502 may provide an index to indicate which local NN information to use in the NN information messages 1010]).
Regarding claim 3, Yerramalli et al. discloses all the recited subject matter in claim 1 and further wherein the configuration information of the target AI model comprises at least one of the following: model identifier (ID) information (paragraph 0096 [an index to indicate which local NN information to use in the NN information messages 1010]); model structure information; model type information; model parameter information; model input information; model output information; model inference process; or optimizer state information.
Regarding claim 4, Yerramalli et al. discloses all the recited subject matter in claim 1 and further wherein the input data of the target AI model comprises at least one of the following: first channel impulse response (CIR) information, wherein a length of the first CIR information is N1, and N1 is a positive integer (paragraph 0086 [The oversampled CIR may be generated by zero-padding the CFR to the right length at stage 706 and then performing a large point Inverse Fast-Fourier Transform (IFFT) at stage 708. In an embodiment, the process may optionally perform one or more shift, scaling and truncation operations to reduce the NN input complexity.]); a first CIR matrix, wherein the first CIR matrix has N2N3 dimensions and a translation parameter M, and N2, N3, and M are all positive integers; path-related information of N4 paths, wherein N4 is a positive integer; or long term CIR information.
Regarding claim 5, Yerramalli et al. discloses all the recited subject matter in claim 1.
However, claim 5, wherein the target data processing policy comprises at least one of the following: an AI model input format; a CIR information truncation length; a number of rows and/or number of columns of a CIR matrix; CIR information translation; path-related information of N5 paths, wherein N5 is a positive integer; a normalization policy; a long term smoothing method; or a short term smoothing method, is directed to non-selected portions of claim 1.
Regarding claim 6, Yerramalli et al. discloses all the recited subject matter in claim 1.
However, claim 6, wherein the candidate data processing policy comprises at least one of the following: path-related information; a characteristic of CIR information; a normalization policy; a long term indication; a short term indication; or CIR information averaged over L measurement results, wherein L is a positive integer, is directed to non-selected portions of claim 1.
Regarding claim 7, Yerramalli et al. discloses all the recited subject matter in claim 4.
However, claim 7, wherein the path-related information comprises at least one of the following: a number of paths; path characteristic information; or a path selection criterion, is directed to non-selected portions of claim 4.
Regarding claim 8, Yerramalli et al. discloses all the recited subject matter in claim 7.
However, claim 8, wherein the path characteristic information comprises at least one of the following: time information; energy information; or angle information; and/or the path selection criterion comprises at least one of the following: a path with energy greater than a first threshold among multiple paths, wherein the first threshold is a product value of energy of a path with maximum energy and a first value; or paths ranking in top N6 positions by energy in multiple paths, wherein N6 is a positive integer, is directed to non-selected portions of claims 4 and 7.
Regarding claim 9, Yerramalli et al. discloses all the recited subject matter in claim 6.
However, claim 9, wherein the characteristic of CIR information comprises at least one of the following: a CIR information truncation length; a number of rows of a CIR matrix; a number of columns of a CIR matrix; or a CIR translation parameter, is directed to non-selected portions of claim 6.
Regarding claim 10, Yerramalli et al. discloses all the recited subject matter in claim 5.
However, claim 10, wherein the normalization policy comprises at least one of the following: a time normalization policy; an energy normalization policy; indication information for indicating whether normalization is performed; or a normalization coefficient, is directed to non-selected portions of claims 1 and 5.
Regarding claim 11, Yerramalli et al. discloses all the recited subject matter in claim 1 and further discloses wherein the measurement-related information comprises at least one of the following: signal measurement information (paragraph 050 [RSRP]); location information; an error value; CIR information; or power delay profile (PDP) information; and wherein the signal measurement information comprises at least one of the following: a reference signal time difference (RSTD) measurement result; a round-trip time (RTT) measurement result; an angle of arrival (AOA) measurement result; an angle of departure (AOD) measurement result; reference signal received power (RSRP) (paragraph 050 [RSRP]); measurement information of multiple paths; or line-of-sight (LOS) indication information.
Regarding claim 12, Yerramalli et al. discloses all the recited subject matter in claim 4 and further discloses wherein the CIR comprises at least one of the following: a time domain channel impulse response (paragraph 0086 [the CIR is contained within a few time-domain samples]); a time domain cross-correlation vector or matrix; a time domain auto-correlation vector or matrix; a frequency domain channel response; a frequency domain cross-correlation vector or matrix; a frequency domain auto-correlation vector or matrix; a frequency domain subcarrier phase vector or matrix; or a frequency domain subcarrier phase difference vector or matrix.
Regarding claim 13, Yerramalli et al. discloses all the recited subject matter in claim 1 and further discloses wherein the communication device obtaining the measurement-related information comprises: obtaining, by the communication device, the measurement-related information based on a target mode (paragraph 0075 [different techniques may be used to determine position of an entity such as the UE 105. For example, known position-determination techniques include OTDOA (also called TDOA and including UL-TDOA and DL-TDOA)]) or a target device; wherein the target mode comprises at least one of the following: observed time difference of arrival (OTDOA) (paragraph 0075 [different techniques may be used to determine position of an entity such as the UE 105. For example, known position-determination techniques include OTDOA (also called TDOA and including UL-TDOA and DL-TDOA)]); global navigation satellite system (GNSS); downlink time difference of arrival (TDOA); uplink time difference of arrival (TDOA); bluetooth AoA; bluetooth AoD; or RTT; and the target device comprises at least one of the following: bluetooth; a sensor; or wireless high-fidelity (WiFi) (paragraph 0040 [the UE 105 may support wireless communication using one or more Radio Access Technologies (RATs) IEEE 802.11 WiFi (also referred to as Wi-Fi), Bluetooth® (BT]).
Regarding claim 14, Yerramalli et al. discloses all the recited subject matter in claim 3.
However, claim 14, wherein the model structure information comprises at least one of the following: any one or a combination of a fully connected neural network, a convolutional neural network, a recurrent neural network, and a residual network; a number of hidden layers; a connection mode between an input layer and a hidden layer; a connection mode between a plurality of hidden layers; a connection mode between a hidden layer and an output layer; or a number of neurons in each layer.
Regarding claim 15, Yerramalli et al. discloses all the recited subject matter in claim 3.
However, claim 15, wherein the model type information comprises at least one of the following: a fully connected model; a hybrid model; an unsupervised model; or a supervised model, is directed to non-selected portions of claim 3.
Regarding claim 16, Yerramalli et al. discloses all the recited subject matter in claim 3.
However, claim 16, wherein the model parameter information comprises at least one of the following: application documentation of a model; descriptive parameter information of a model; hyperparameter information of a model; initial parameter information of a model; or a weight of a model, is directed to non-selected portions of claim 3.
Regarding claim 17, Yerramalli et al. discloses all the recited subject matter in claim 1 and further discloses wherein the configuration information of the target AI model comprises at least one of the following: list information of a neural network, wherein the list information comprises at least one of the following: a neuron type of each neural network; or a neuron weight and/or bias of each neural network (paragraph 0099 [weights and bias matrices associated with the neurons in a neural network]); a type and/or location of an activated network element; hyperparameter information; or loss function information.
Regarding claim 18, Yerramalli et al. discloses all the recited subject matter in claim 1 and further discloses wherein the method further comprises: receiving, by the communication device, update information of the configuration information of the target AI model (paragraph 0098 [The base station 502, or other associated network servers such as the LMF 120, may be configured to update NN weights using the ground truth from the previously stored conventional algorithm. The retraining procedure may be used to adapt the NN 800 to an environment, RF filters on the base station 502 and/or the UE 105, or other RF distortions that may impact the performance of the NN 800; The information given by the PRS and SRS signals may be used for online retraining of NN weights in the NN 800. For example, the base station 502 may generate and transmit PRS signals 1102. The UE 105 may be configured to update NN weights in the local NN information using the ground truth of the previously stored conventional algorithm.]) and/or update information of the first information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER T WYLLIE whose telephone number is (571)270-3937. The examiner can normally be reached 4pm-11:30pm.
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/CHRISTOPHER T WYLLIE/ Examiner, Art Unit 2465
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