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 .
Response to Arguments
The rejection with respect to 35 UC 112 has been withdrawn.
Applicant's arguments filed on 06/04/2026 regarding 35 USC 101 has been withdrawn.
Applicant’s arguments with respect to amended claim(s) have been considered but are moot in view of new ground of rejection because of newly added limitations into currently amended claims. Response to the amendment is as below.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
Claim(s) 1, 3, 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah et al. (US2024/0340136)(hereafter Hirzallah) in view of Song (US 2024/0171950)(hereafter Song).
Regarding claim 1, Hirzallah discloses a method, comprising:
transmitting, by a training entity, a request for an assistance data to a data collection entity (see, para [0089], The wireless device may transmit such assistance information in response to a request to provide assistance information for training a positioning model);
receiving, by the training entity, the assistance data from the data collection entity ([0089] A wireless device may transmit assistance information to an LMF or a training entity along with measurements and/or labels for training a positioning model, LMF is training entity receiving assistance data);
performing, by the training entity, a model training on a positioning model based on the assistance data (see, para [0089], assistance data for training a positioning model); and
determining, by the training entity, a position information by the positioning model (see, para [0155], Fig. 13, LMF, 1310, calculate the position of the first wireless device based on positioning model or assistance information).
Hirzallah does not explicitly disclose wherein the request comprises a data augmentation indicating a data augmentation method.
However, in same field of endeavor, Song teaches, [0092], A data augmentation request message may include a value corresponding to at least one of attributes included in the <data Augmentation> resource. For example, the <data Augmentation> resource may include at least one of an augmentation type attributes, an augmentation type parameter attribute, a source resource attribute, and a target resource attribute. See, also, claim 7, wherein the request message further includes an indicator indicating the augmentation policy.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Song with the Hirzallah, as a whole, so as to configure the request for an assistance data indicating the augmentation policy, the motivation is to expanding learning data to train a model using augmented data.
Regarding claim 3, Hirzallah further discloses the method, wherein the training entity comprises at least one of a positioning reference unit (PRU), a user equipment (UE), a base station, a location management function (LMF), a server and a network node (see, para [0089], [0155], Fig. 13, LMF).
Regarding claim 8, Hirzallah further discloses the method, further comprising: obtaining, by the training entity, a data input sample; and transmitting, by the training entity, the data input sample to the data collection entity, wherein the assistance data is associated to the data input sample (see, para [0089], The wireless device may transmit such assistance information in response to a request to provide assistance information for training a positioning model, [0089] A wireless device may transmit assistance information to an LMF or a training entity along with measurements and/or labels for training a positioning model, LMF is training entity receiving assistance data).
Regarding claim 10, Hirzallah further discloses the method, wherein the position information comprises an estimated location of the training entity or a soft information related to the estimated location (see, para [0029], soft information, para [0097], estimated location of the wireless device).
Claim(s) 2, 4, 5, 6, 9 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah and Song and further in view of Feki et al (US 2026/0012399)(hereafter Feki).
Regarding claim 2, the combined teachings do not discloses the method, wherein the assistance data comprises one or more of: a set of delay profiles of channels; a set of labels labelling each delay profile; a set of quality indicators of the delay profiles and the labels; a set of augmented training data; a set of data augmentation indicators for the augmented training data; a set of parameters or timing shift values associated with the augmented training data; an actual dataset size; and statistical information associated with a data augmentation method.
However, in same field of endeavor, Feki teaches para [0007], receiving, from a second device, a configuration of data augmentation for training a positioning model at the first device; determining a set of data augmentation parameters based on the configuration of data augmentation; obtaining data based on a data augmentation procedure and the set of data augmentation parameters, the data comprising measured data of the first device and augmented data; and training the positioning model based on a combination of the measured data and the augmented data.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Feki with the Hirzallah and Song, as a whole, so as to train the positioning model based on augmented training data , the motivation is to enhance the accuracy of the positioning.
Regarding claim 4, the combined teachings further disclose the method, wherein the data augmentation indicator indicates a type of data augmentation method (see, Song, [0092], A data augmentation request message may include a value corresponding to at least one of attributes included in the <data Augmentation> resource. For example, the <data Augmentation> resource may include at least one of an augmentation type attributes, an augmentation type parameter attribute, a source resource attribute, and a target resource attribute. See, also, claim 7, wherein the request message further includes an indicator indicating the augmentation policy) wherein the request comprises one or more of a data augmentation indicator to indicate a type of data augmentation needed, a preferred dataset size, and statistical information associated with a data augmentation method (Feki, para [0088], the assistance request may indicate a proportion of available data with regard to a target data size used in training a positioning model. The term “positioning model” used herein can refer to a processing model or an AI/ML model which is used for positioning a device).
Regarding claim 5, the combined teachings do not explicitly disclose the method further comprising: performing, by the training entity, a data augmentation to generate an augmented training data based on the assistance data, wherein the model training is performed based on the augmented training data. However, in same field of endeavor, Feki teaches in para [0007], receiving, from a second device, a configuration of data augmentation for training a positioning model at the first device; determining a set of data augmentation parameters based on the configuration of data augmentation; obtaining data based on a data augmentation procedure and the set of data augmentation parameters, the data comprising measured data of the first device and augmented data; and training the positioning model based on a combination of the measured data and the augmented data. Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Feki with the Hirzallah and Song, as a whole, so as to train the positioning model based on augmented training data , the motivation is to enhance the accuracy of the positioning.
Regarding claim 6, the combined teachings further disclose the method wherein the data augmentation is performed by at least one of a jittering augmentation method, a timing shift augmentation method, an artificial intelligence (AI) augmentation method and a machine learning (ML) augmentation method (see, Feki, Fig. 3, data augmentation for model training).
Regarding claim 9, the combined teachings further discloses the method of Claim 8, further comprising: requesting, by the training entity, at least one label for the data input sample (see, Feki, para [0085], raw labelled data which are input sample); and performing, by the training entity, a data augmentation to generate an augmented training data based on the at least one label and the data input sample (see, para [0085], it proposes a method and a procedure which targets data augmentation for AI/ML-based positioning with AI/ML inference running at the UE side. For example, it proposes a method which allows to increase the amount of raw labelled data: radio measurements and corresponding geographical position. The additional signaling may ensure efficient data augmentation operation at UE with network assistance).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah, Song and Feki and further in view of Kanazawa et al. (US2025/0086760) (hereafter Kanazawa).
Regarding claim 7, the combined teachings do not explicitly disclose the method, wherein the AI augmentation method comprises a Conditional Variational Autoencoding (CVAE) method.
However, in same field of endeavor, Kanazawa teaches in para [0123], The inpainting models and/or the occlusion models disclosed herein can include conditional variational autoencoders (CVAE) for processing input data and the blended data in order to generate augmented data.
Therefore, it would have been obvious to one of ordinary skilled in the art to combine the teachings of Kanazawa with the Hirzallah, Song and Feki, as a whole, so as to use the conditional variation autoencoding to generate the augmented data.
12. Claim(s) 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hirzallah and Song and further in view of Feki et al (US 2026/0012399) (hereafter Feki).
Regarding claim 11, Hirzallah discloses a method, comprising:
receiving, by a data collection entity, a request for an assistance data from a training entity (see, para [0089], The wireless device may transmit such assistance information in response to a request to provide assistance information for training a positioning model);
generating, by the data collection entity, the assistance data according to the request ([0089] A wireless device may transmit assistance information to an LMF or a training entity along with measurements and/or labels for training a positioning model, LMF is training entity receiving assistance data); and
transmitting, by the data collection entity, the assistance data to the training entity (see, para [0089], assistance data for training a positioning model),
Hirzallah does not explicitly disclose wherein the request comprises a data augmentation indicating a data augmentation method.
However, in same field of endeavor, Song teaches, [0092], A data augmentation request message may include a value corresponding to at least one of attributes included in the <data Augmentation> resource. For example, the <data Augmentation> resource may include at least one of an augmentation type attributes, an augmentation type parameter attribute, a source resource attribute, and a target resource attribute. See, also, claim 7, wherein the request message further includes an indicator indicating the augmentation policy.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Song with the Hirzallah, as a whole, so as to configure the request for an assistance data indicating the augmentation policy, the motivation is to expanding learning data to train a model using augmented data.
But, the combined teachings do not explicitly disclose wherein the assistance data is generated by performing a data augmentation to train a positioning model, wherein the assistance data comprises at least one of: a set of delay profiles of channels; a set of labels labelling each delay profile; a set of quality indicators of the delay profiles and the labels; a set of augmented training data; a set of data augmentation indicators for the augmented training data; a set of parameters or timing shift values associated with the augmented training data; an actual dataset size; and statistical information associated with a data augmentation method.
However, in same field of endeavor, Feki teaches para [0007], receiving, from a second device, a configuration of data augmentation for training a positioning model at the first device; determining a set of data augmentation parameters based on the configuration of data augmentation; obtaining data based on a data augmentation procedure and the set of data augmentation parameters, the data comprising measured data of the first device and augmented data; and training the positioning model based on a combination of the measured data and the augmented data. see, para [0005], augmented data for training a positioning model, [0062], model training with data augmentation, [0052], data augmentation to increase amount of training data);
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Feki with the Hirzallah and Song, as a whole, so as to train the positioning model based on augmented training data , the motivation is to enhance the accuracy of the positioning.
Regarding claim 12, Claim is rejected for the same reason claim 3 is rejected.
Regarding claim 13, Claim is rejected for the same reason claim 5 is rejected.
Regarding claim 14, claims is rejected for the same reason claim 6 is rejected.
Regarding claim 15, the combined teachings further discloses the method, further comprising: obtaining, by the data collection entity, a data input sample; and adding, by data collection entity, at least one label for the data input sample, wherein the assistance data is associated to the data input sample and the at least one label (Hirzallah, para [0030], The described techniques enable a wireless device to report measurements to a network entity, receive labeling assistance for training a positioning model, and exchange assistance information with the network entity to improve the accuracy of the positioning model, para [0058], he positioning model configuration component 199 may transmit a set of labels and/or assistance information to the UE for training the positioning model at the UE ).
13. Claim(s) 16- 19 and 20 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Feki et al. (US2026/0012399)(hereafter Feki) in view of Dekel et al. (US 2024/0419382)(hereafter Dekel).
Regarding claim 16, Feki discloses a method, comprising: obtaining, by a processor of an apparatus, a data input sample (see, para [0005]);
performing, by the processor, a data augmentation to generate an augmented training data based on the data input sample (see, para [0005], augmented data for training a positioning model, [0062], model training with data augmentation, [0052], data augmentation to increase amount of training data);
performing, by the processor, a model training on a positioning model based on the augmented training data (see, para [0050], [0051], model training], [0061], see, Fig. 3, the model training using data augmentation, 300, see, fig. 7, the determine augmented data, 7015 and train the positioning model, 7025 using such augmented data); and
determining, by the processor, a position information of the apparatus by the positioning model (see, para [0155], Fig. 13, LMF, 1310, calculate the position of the first wireless device based on positioning model or assistance information).
But, Feki does not disclose data augmentation is performed by one or more of a jittering augmentation method and a timing shift augmentation method.
However, in same field of endeavor, Dekel teaches in para [0087], train the object detector on a single scale using jitter-augmentation integrated landmark localization information through joint multi-task learning to improve the performance and accuracy of end-to-end object detection.
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to combine the teachings of Dekel with the Feki, as a whole, so as to use the jitter augmentation method to perform data augmentation, the motivation is to improve the accuracy of the localization.
Regarding claim 17, Feki further discloses the method, wherein the apparatus comprises at least one of a positioning reference unit (PRU), a user equipment (UE), a base station, a location management function (LMF), a server and a network node (see, para [0047], Fig. 8, Fig. 9).
Regarding claim 18, Feki further discloses the method, wherein the data input sample comprises at least one of a channel impulse response (CIR), a power delay profile (PDP) and a reference signal received power (RSRP) (see, para [0071], the radio measurement may indicate a reference signal received power (RSRP) measured by the device 430. Alternatively, or in addition, the radio measurement may indicate channel state information. In some other example embodiments, the radio measurement may indicate a channel response, for example, a channel impulse response (CIR)).
Regarding claim 20, Feki further discloses the method, wherein the position information comprises an estimated location of the apparatus or a soft information related to the estimated location (see, para [0081], The augmented data 520 can be estimated for the locations. [0082], The device 410 trains 4040 the positioning model based on a combination of the measured data and the augmented data. For example, the combination of the measured data and the augmented data may indicate data at a certain position is measured and data at another certain position is interpolated (i.e., augmented)).
Conclusion
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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/DHAVAL V PATEL/Primary Examiner, Art Unit 2631
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