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 .
Information Disclosure Statement
The information disclosure statement submitted on 07/07/25 has been considered by the examiner and made of record in the application file.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,3,4,17,18,19,20 are rejected under 35 U.S.C. 103 as being unpatentable over Artemenko et al. (DE 102020203703, hereinafter Artemenko) in view of Ryden (US 20230262448)
Regarding claim 1, Artemenko discloses a positioning method based on an artificial intelligence (AI) model, comprising:
obtaining, by a first communication device, first information associated with AI model-related information (Page 3: Lines 28-29; The device receives measured values associated with the localization process; Page 2: Lines 42-46; The processing module determines uncertainty associated with the first localization model during positioning; The received measured values and the determined uncertainty estimates correspond to the claimed first information because they are information associated with the machine-trained first localization model);
determining, by the first communication device, target information based on the first information, wherein the target information comprises at least one of the following: a target AI model, validity information of the AI model-related information, or feedback information obtained by performing positioning based on the target AI model (Page 2: Lines 42-46; The system determines whether the uncertainty of the localization model exceeds a threshold to determine whether the localization model remains sufficiently accurate during operation; Page 5: Lines 54-56; The processing module evaluates whether the localization model maintains sufficient positioning accuracy before allowing the model to update; The determination of whether the localization model maintains sufficient positioning accuracy based on the previously obtained measured values and uncertainty estimates corresponds to the target information, specifically validity information of the AI model-related information because the reference determines whether the machined-trained localization model remains suitable for performing positioning; The remaining limitations have been given no patentable weight due to the optional language “or”).
Artemenko does not disclose the first information indicates a valid application range of the AI model-related information, and the AI model-related information comprises at least one of the following: the AI model, an AI model parameter, an input of the AI model, or an output of the AI model.
Ryden, however, discloses information indicates a valid application range of the AI model-related information, and the AI model-related information comprises at least one of the following: the AI model, an AI model parameter, an input of the AI model, or an output of the AI model (Par. 5:Lines 1-3; Each ML model is valid only within a particular geographical area; Par. 47: Lines 16-21; The wireless device uses ML models valid for its current cells that may not be valid for different cells; Par. 91 and Par. 92; The ML models and its parameters are communicated to the wireless device; The area-specific, cell-specific, and circumstance-specific validity corresponds to the information indicating a valid application range because a ML model is only valid in a particular area. The ML models and parameters correspond to the AI model-related information).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko to associate the machine-trained localization model with information indicating a valid application range of the AI model, such as area-specific, cell-specific, or operating circumstance-specific validity, and to communicate the AI model and its associated model parameters as taught by Ryden, in order to ensure that an AI model appropriate for the current operating environment is utilized, thereby improving positioning accuracy by avoiding the use of AI models outside of their valid application range.
Regarding claim 3 as applied to claim 1, Artemenko does not disclose wherein the first information comprises at least one of the following: cell information, region information, valid time information, scenario information, or a signal-to-interference-plus-noise ration (SINR) range.
Ryden, however, discloses wherein the first information comprises at least one of the following: cell information (Par. 45: Lines 16-18; The radio environment of the UE includes the location of the UE; Par. 27: Lines 5-12; The location may comprise cell identities; Par. 47: Lines 6-14; A ML model corresponding to a particular cell is selected for use when the wireless device is associated with that cell; The remaining limitations were given no patentable weight due to the optional language “or”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko such that the first information comprises cell information, as taught by Ryden, in order to associate the AI model with the wireless device's current radio environment and utilize an AI model appropriate for the applicable cell, thereby improving the accuracy and reliability of AI-based positioning.
Regarding claim 4 as applied to claim 1, Artemenko does not disclose wherein the cell information comprises at least one of the following:
identification information of one or more cells;
identification information of one or more base stations;
identification information of one or more transmission reception points (TRPs);
cell list information; or
cell frequency-domain range information;
the region information comprises at least one of the following:
region identification information; distance range information; or reference point information corresponding to the distance range;
the valid time information comprises at least one of the following:
timer duration;
or a timer start time; or the scenario information comprises at least one of the following:
a line of sight (LOS) scenario; a non-line-of-sight (NLOS) scenario; a complex scenario; an indoor scenario; or an outdoor scenario.
Ryden, however, discloses wherein the cell information comprises at least one of the following: identification information of one or more cells (Par. 45: Lines 16-18; The radio environment of the UE includes the location of the UE; Par. 27: Lines 5-12; The location may comprise cell identities; The remaining limitations were given no patentable weight due to the optional language “at least one of”. Region and scenario information were given no patentable weight in claim 3 and therefore were given no patentable weight in claim 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko such that the cell information comprises identification information of one or more cells, as taught by Ryden, in order to identify the applicable cell associated with the wireless device and provide the AI model with cell-specific information for selecting and applying the appropriate positioning model, thereby improving the accuracy and reliability of AI-based positioning.
Regarding claim 17 as applied to claim 1, Artemenko discloses wherein the validity information may comprise at least one of the following: validity indication information (Page 2: Lines 42-46; The system determines whether the uncertainty of the localization model exceeds a threshold to determine whether the localization model remains sufficiently accurate during operation; Page 5: Lines 54-56; The processing module evaluates whether the localization model maintains sufficient positioning accuracy before allowing the model to update; The determination of whether the localization model remains sufficiently accurate corresponds to the claimed validity indication because it indicates whether the model is suitable; The remaining limitations were given no patentable weight due to the optional language “or”), indicating whether the AI model-related information is valid; a validity degree; a validity class; a cause of invalidity; reliability indication information, indicating whether a positioning result obtained based on the target AI model is reliable; a reliability degree; or a reliability rating.
Regarding claim 18, Artemenko does not disclose receiving, by a second communication device, target information sent by a first communication device, wherein the target information comprises at least one of the following: a target AI model, validity information of AI model-related information, or feedback information obtained by performing positioning based on the target AI model.
Ryden, however, discloses receiving, by a second communication device, target information sent by a first communication device, wherein the target information comprises at least one of the following: a target AI model (Par. 8; A node selects an ML model from a plurality of candidate ML models and causes transmission of the selected ML model to the wireless device; Par. 47: Lines 12-15; A RAN node (first communication device) transmits the selected ML model to the UE (second communication device)).
The remaining limitations have been addressed by claim 1.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko to transmit the selected AI model to a second communication device, as taught by Ryden, in order to provide the second communication device with the appropriate AI model for positioning based on the applicable operating conditions, thereby enabling the second communication device to perform AI-based positioning using the appropriate model.
Regarding claim 19, the rejection of claim 1 addresses the limitations presented in claim 19. Therefore, the limitations of claim 19 have been addressed. A device capable of performing the recited functions necessarily includes a processor and a memory.
Regarding claim 20 as applied to claim 18, a device capable of performing the recited functions necessarily includes a processor and a memory.
Claims 2,5,10 are rejected under 35 U.S.C. 103 as being unpatentable over Artemenko et al. (DE 102020203703, hereinafter Artemenko) in view of Ryden (US 20230262448) in further view of Li et al. (US 20210329416, hereinafter Li)
Regarding claim 2 as applied to claim 1, Artemenko in view of Ryden discloses determining, by the first communication device, target information based on the first information (as detailed in the rejection of claim 1) but does not disclose obtaining, by the first communication device, second information of a target terminal, wherein the second information indicates positioning-related information obtained by the target terminal; and determining, by the first communication device, target information based on the first information comprises: determining, by the first communication device, the target information based on the first information and the second information.
Li, however, discloses obtaining, by a communication device, second information of a target terminal, wherein the second information indicates positioning-related information obtained by the target terminal (Par. 56: Lines 14-18; The terminal devices provide positioning-related measurements and beamforming information to the network; Par. 76: Lines 1-6; The location server obtains positioning measurements reported by other terminals; Par. 79: Lines 20-22; Multiple terminals provide measurement reports (MRs) containing positioning related information for another terminal); and determining, by the first communication device, the target information based on the second information (Par. 80: Lines 1-9; The location server determines positioning information using measurement information received from the terminal devices).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden to further obtain positioning-related information of a target terminal, such as measurement reports and beamforming information, as taught by Li, and to determine the target information based on both the previously obtained first information and the obtained second information, in order to improve positioning accuracy by utilizing additional positioning information from the target terminal when evaluating and performing AI-based positioning.
Regarding claim 5 as applied to claim 2, Artemenko in view of Ryden does not disclose wherein the second information comprises at least one of the following:
position information of the target terminal, cell information, region information, timer information, scenario information, or an SINR measured by the target terminal, wherein
the cell information is at least one piece of information of a serving cell of the target terminal, a reference cell, or a cell with the strongest reference signal received power (RSRP), and the at least one piece of information comprises: identification information and frequency-domain information;
the region information is region identification information of the target terminal; and the scenario information is information of a scenario in which the target terminal is located.
LI, however, discloses information comprising at least one of the following: position information of the target terminal (Par. 56: Lines 14-18; The terminal devices provide positioning-related measurements and beamforming information to the network; Par. 74: Lines 13-17; The location server determines the position of the terminal device; Par. 80: Lines 1-4; Measurement reports include positioning information used to determine the terminal position; The remaining limitations were given no patentable weight due to the optional language “at least one”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Artemenko in view of Ryden to utilize position information of the target terminal as the second information, as taught by Li, in order to improve the accuracy of AI-based positioning by using additional positioning information obtained from the target terminal.
Regarding claim 10 as applied to claim 2, claim 10 is given no patentable weight because it further limits an optional alternative (“feedback information”) of claim 1 that was not relied upon in the rejection.
Claims 6,7,8,9,11,13,16 are rejected under 35 U.S.C. 103 as being unpatentable over Artemenko et al. (DE 102020203703, hereinafter Artemenko) in view of Ryden (US 20230262448) in further view of Li et al. (US 20210329416, hereinafter Li) in further view of Vankayala et al. (US 20210126738, hereinafter Vankayala).
Regarding claim 6 as applied to claim 2, Artemenko in view of Ryden in further view of Li discloses determining, by the first communication device, the target information based on the first information and the second information (as detailed in the rejection of claim 2), but does not disclose determining, by the first communication device, the target information based on a value of a parameter in the second information and a range of a corresponding parameter in the first information.
Vankayala, however, discloses determining, by the first communication device, the target information based on a value of a parameter in the second information and a range of a corresponding parameter in the first information (Par. 30: Lines 37-47; The base station determines whether to combine information by comparing a measured SINR value with a predefined SINR threshold. The predefined SINR threshold used for the determination is generated by a machine learning model. The machine-learning-derived SINR threshold is periodically updated for subsequent determinations).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden and Li to determine the target information based on a value of a parameter in the second information and a range of a corresponding parameter in the first information, as taught by Vankayala, in order to evaluate whether measured positioning-related parameters satisfy machine-learning-derived operating criteria before performing the determination, thereby improving the accuracy and reliability of AI-based positioning decisions.
Regarding claim 7 as applied to claim 6, Artemenko in view of Ryden in further view of Li does not disclose wherein in a case that the value of the parameter in the second information falls within the range of the corresponding parameter in the first information, the AI model-related information is valid.
Vankayala, however, discloses wherein in a case that the value of the parameter in the second information falls within the range of the corresponding parameter in the first information, the AI model-related information is valid (Par. 30: Lines 37-46; The base station performs the machine-learning-based determination when the measured SINR values satisfies the predefined SINR threshold. The predefined SINR threshold used for the determination is generated by the machine learning model; Because the machine-learning-derived threshold is applied only when the measured SINR value satisfies the threshold, the machine-learning-derived information is applicable under those conditions, corresponding to the claimed AI model-related information being valid).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Artemenko in view of Ryden, Li, and Vankayala such that the AI model-related information is treated as valid when the value of the parameter in the second information falls within the range of the corresponding parameter in the first information, in order to ensure that AI-based positioning determinations are made only when the measured parameter satisfies the machine-learning-derived operating criteria, thereby improving positioning accuracy.
Regarding claim 8 as applied to claim 6, Artemenko in view of Ryden in further view of Li in further view of Vankalaya discloses wherein in a case that the value of the parameter in the second information falls within a first range of the corresponding parameter in the first information, the AI model-related information is valid (as detailed in the rejection of claim 7).
Ryden further discloses the target AI model is an AI model corresponding to the first range of the first information (Par. 168; A node selects one ML model from a plurality of candidate ML models based on information describing the wireless device and its operating environment; Par. 47: Lines 6-14; A ML model corresponding to a particular cell is selected for use when the wireless device is associated with that cell; The ML model is selected according to the particular cell/radio environment which corresponds to the first range of the first information);
wherein the method further comprises:
receiving, by the first communication device, a plurality of pre-configured AI models and/or AI model parameters, and first information corresponding to the AI models and/or the AI model parameters (Par. 95: Lines 1-3; The wireless device is provided with a plurality of preconfigured ML models; Par. 95: Lines 8-13; The network identifies which preconfigured ML model the wireless device is to use; Par. 91: Lines 1-2; The wireless device receives ML model parameters).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden, Li, and Vankayala such that, when the value of the parameter in the second information falls within a first range of the corresponding parameter in the first information, the target AI model is an AI model corresponding to the first range of the first information, and to receive a plurality of pre-configured AI models and/or AI model parameters and first information corresponding thereto, as taught by Ryden, in order to select and utilize the AI model appropriate for the current operating conditions, thereby improving the accuracy and reliability of AI-based positioning while reducing unnecessary model updates and signaling overhead.
Regarding claim 9 as applied to claim 8, claim 9 is given no patentable weight because it further limits an optional alternative (“feedback information”) of claim 1 that was not relied upon in the rejection.
Regarding claim 11 as applied to claim 2, Artemenko in view of Ryden in further view of Li discloses obtaining, by the first communication device, second information of a target terminal, wherein the second information indicates positioning-related information obtained by the target terminal, and determining, by the first communication device, the target information based on the first information and the second information (as detailed in the rejection of claim 2), but does not explicitly disclose that the second information comprises second measurement information obtained by the target terminal, wherein, in a case that the second measurement information is measurement information obtained through one measurement, the target information is determined based on a value of a parameter in the second measurement information and a range of a corresponding parameter in the first information.
Vankayala, however, discloses wherein the second information comprises second measurement information obtained by the target terminal, and in a case that the second measurement information is measurement information obtained through one measurement, determining, by the first communication device, the target information based on a value of a parameter in the second measurement information and a range of a corresponding parameter in the first information (Par. 30: Lines 37-47; The base station determines whether to combine information by comparing a measured SINR value with a predefined SINR threshold. The predefined SINR threshold used for the determination is generated by a machine learning model. The measured SINR corresponds to the claimed second measurement information obtained through one measurement, while the measured SINR value corresponds to the claimed value of a parameter in the second measurement information, and the machine-learning-derived predefined SINR threshold corresponds to the claimed range of a corresponding parameter in the first information, because the threshold defines the acceptable operating range against which the measured SINR value is evaluated).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden and Li to utilize second measurement information obtained by the target terminal and, in a case that the second measurement information is obtained through one measurement, determine the target information based on a value of a parameter in the second measurement information and a range of a corresponding parameter in the first information, as taught by Vankayala, in order to evaluate measured positioning-related parameters against machine-learning-derived operating criteria, thereby improving the accuracy and reliability of AI-based positioning determinations.
Regarding claim 13 as applied to claim 11, claim 13 is given no patentable weight because it further limits an optional alternative (“feedback information”) of claim 1 that was not relied upon in the rejection.
Regarding claim 16 as applied to claim 13, claim 16 is given no patentable weight because it depends on claim 13 which was given no patentable weight.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Artemenko et al. (DE 102020203703, hereinafter Artemenko) in view of Ryden (US 20230262448) in further view of Li et al. (US 20210329416, hereinafter Li) in further view of Vankayala et al. (US 20210126738, hereinafter Vankayala) in further view of Wang et al. (WO 2022010685, hereinafter Wang)
Regarding claim 12 as applied to claim 11, Artmenko in view of Ryden in further view of Li in further view of Vankalaya discloses second measurement information obtained by the target terminal used to determine target information (as detailed in the rejection of claim 11).
Artmenko in view of Ryden in further view of Li in further view of Vankalaya does not disclose wherein the second measurement information comprises at least one of the following:
a signal-to-interference-plus-noise ratio (SINR) range, a noise value, an NLOS-introduced absolute time value, a delay spread value, an angle spread value, an SINR mean and variance, a noise mean and variance, an NLOS-introduced absolute time mean and variance, a delay spread mean and variance, or an angle spread mean and variance, wherein
the SINR range and the SINR mean and variance are derived from an SINR of at least one piece of information;
the noise value and the noise mean and variance are derived from a noise value of at least one piece of information; and
the at least one piece of information comprises: a measurement channel, a measurement signal, or first measurement information;
wherein
the first measurement information comprises at least one of the following:
signal measurement information; position information; an error value; channel impulse response (CIR) information; or power delay profile (PDP) information;
wherein the signal measurement information comprises at least one of the following:
a reference signal time difference (RSTD) measurement result, a round trip delay measurement result, an angle of arrival (AOA) measurement result, an angle of departure (AOD) measurement result, a reference signal received power (RSRP), multipath measurement information, or line of sight (LOS) indication information; and
the multipath measurement information comprises at least one of the following:
a power of a first path, a time delay of a first path, a time of arrival (TOA) of a first path, a reference signal time difference (RSTD) of a first path, an antenna subcarrier phase difference of a first path, an antenna subcarrier phase of a first path, a power of a multipath, a time delay of a multipath, a TOA of a multipath, an RSTD of a multipath, an antenna subcarrier phase difference of a multipath, or an antenna subcarrier phase of a multipath.
Wang, however, discloses wherein the second measurement information comprises at least one of the following:
a delay spread value (Par. 64: Lines 1-5; The UE reports observed signal of link quality parameters as updated ML information. The UE reports observed signal quality parameters including a channel delay spread; The remaining limitations have been given no patentable weight due to the optional language “at least one of”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden, Li, and Vankayala such that the second measurement information comprises a delay spread value, as taught by Wang, in order to provide the AI model with additional UE-observed channel characteristics for use in AI-based positioning, thereby improving the accuracy and reliability of the positioning determination.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Artemenko et al. (DE 102020203703, hereinafter Artemenko) in view of Ryden (US 20230262448) in further view of Li et al. (US 20210329416, hereinafter Li) in further view of Vankayala et al. (US 20210126738, hereinafter Vankayala) in further view of Chen et al. (CN 111428817, hereinafter Chen)
Regarding claim 14 as applied to claim 11, Artemenko in view of Ryden in further view of Li in further view of Vankalaya disclose first information comprising a range of a first measurement value (as detailed in the rejection of claim 11).
Artemenko in view of Ryden in further view of Li in further view of Vankalaya does not disclose wherein that the first information is obtained based on a test set and a validation set of the AI model comprises at least one of the following cases:
the first information is characteristic information obtained based on input data of the test set and the validation set of the AI model;
the first information is characteristic information obtained based on output data of the test set and the validation set of the AI model;
or the first information is characteristic information obtained based on input data and output data of the test set and the validation set of the AI model.
Chen, however, discloses wherein that the first information is obtained based on a test set and a validation set of the AI model comprises at least one of the following cases:
the first information is characteristic information obtained based on input data of the test set and the validation set of the AI model (Page 3: Line 13; After training, the AI model is tested and verified using a test and validation set; Page 3: Lines 3-4; The AI model is trained using input data that forms the basis if the trained model; The trained AI model contains characteristic information obtained from the input data that is tested and validated using the test and validation sets; The remaining limitations were given no patentable weight due to the optional language “or”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden, Li, Vankayala, and Chen such that the first information is obtained based on a test set and a validation set of the AI model and comprises characteristic information obtained based on input data of the test set and the validation set, in order to utilize AI model information that has been tested and validated prior to deployment, thereby improving the reliability, robustness, and accuracy of AI-based positioning determinations.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Artemenko et al. (DE 102020203703, hereinafter Artemenko) in view of Ryden (US 20230262448) in further view of Li et al. (US 20210329416, hereinafter Li) in further view of Vankayala et al. (US 20210126738, hereinafter Vankayala) in further view of Kaya et al. (CN 113994598, hereinafter Kaya)
Regarding claim 15 as applied to claim 11, Artemenko in view of Ryden in further view of Li in further view of Vankayala does not disclose wherein the second measurement information is characteristic information obtained based on the first measurement information; and/or the second measurement information is characteristic information obtained based on the output of the AI model.
Kaya, however, discloses wherein the second measurement information is characteristic information obtained based on the first measurement information (Page 12: Lines 29-31; The AI algorithm extracts features from previously obtained measurement information. The extracted features are used as information for subsequent AI processing; The disclosed extracted features correspond to the claimed characteristic information, because they are features derived from previously obtained measurement information. The previously obtained signal measurements and beam information correspond to the claimed first measurement information, while the extracted features correspond to the claimed second measurement information because they are generated based on those measurements. The remaining limitations have been given no patentable weight due to the optional language "and/or").
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the positioning method of Artemenko in view of Ryden, Li, and Vankayala such that the second measurement information comprises characteristic information obtained based on the first measurement information, as taught by Kaya, in order to provide the AI model with measurement-derived characteristic information for use in AI-based positioning, thereby improving the robustness, accuracy, and reliability of the positioning determination.
Conclusion
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/FABIAN BOTELLO/Examiner, Art Unit 2648
/WESLEY L KIM/Supervisory Patent Examiner, Art Unit 2648
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