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 ACTION
This action is issued in response to Applicants amendments filed July 8, 2026.
Claims 1-13 and 15-21 are pending.
Claim 21 is added and claim 14 is cancelled.
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 1-13 and 15-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an Abstract Idea without significantly more.
Claims 1-11 and 21 are method claims and directed to the process category of patentable subject matter.
Claims 12-13 and 15-19 are method claims and directed to the process category of patentable subject matter.
Claim 20 is a device claim and directed to the machine category of patentable subject matter.
Although claims 1-13 and 15-21 fall under at least one of the four statutory categories, it should be determined whether the claims recite a judicial exception.
The following claim limitations are drawn to abstract idea recitations (struck-through limitations have been identified as additional elements and will be discussed in later sections):
A method for acquiring label information, comprising:
[a] determining,
[b] determining,
The claims fall within the “Mental Processes” grouping of abstract ideas; since the claims are simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind.
With regard to limitation [a], a human may perform mental processing of determinations for “determining” a correction parameter. A human may mentally observe a parameter for correction. This claim limitation has been identified as a recitation of a mental process.
With regard to limitation [b], a human may perform mental processing of determinations for “determining” label information and dataset data. A human may mentally observe and associate label information with data from a dataset. This claim limitation has been identified as a recitation of a mental process.
The examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. The limitations of “determining”; fall within the “Mental Processes” grouping of abstract ideas because this recites a mentally performable process of observing and determining information, such as label information and dataset data for a correction parameter. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all”. Specifically, the limitations as discussed above, as claimed, is a process that covers performance of the limitations in the mind, or with pen and paper, but for the recitation of generic computer components (i.e., processor, memory, device, etc.) because a user can mentally, or with pen and paper, perform the steps as discussed above. See Digitech (organizing and manipulating information through mathematical correlations), Electric Power Group (collecting information, analyzing it, and displaying certain results of the collection and analysis).
This judicial exception is not integrated into a practical application. The claim(s) includes additional elements which fall within the mental processing of information. In particular, the limitations of a network device; a transceiver for communication with a network device; memory stores a computer program executable on the process; a processor; have been identified as recitations of generic computing functions. In particular, the claims only recite additional elements (i.e., processor, memory, device, etc.) that are recited at a high-level of generality (e.g., as a generic computer or as a generic processor performing a generic computer function), such that it amounts to no more than mere instructions to apply the exception using generic computer components. See 2106.05(d) (II). The claim limitations further discuss training a model; however, the model for training in the claim limitation is a basic element for learning the data associations. Accordingly, the additional element(s) do not integrate the abstract idea into a practical application because it does not impose meaningful limits on practicing the abstract idea. The claims as a whole do not appear to integrate the mental process into a practical application and is thus directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component, or are merely drawn to insignificant extra-solution activity. Mere instructions to apply an exception using a generic computer component or insignificant extra-solution is not significantly more than the judicial exception.
The dependent claims, 2-11, 13, 15-19, and 20 depend on a rejected parent claim and do not cure its deficiencies. Similar to the above discussion, each of the dependent claims are drawn to an abstract idea within the “Mental Processes” grouping of abstract ideas. The claims are drawn to subject matter that covers performance of the claimed limitations in the mind, or with pen and paper, but for the recitation of generic computer components as discussed above. The claims are not integrated into a practical application. The claims only recite additional elements that is/are recited at a high-level of generality (e.g., as a generic computer or as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component or are merely drawn to insignificant extra-solution activity.
The claim elements considered individually or in combination do not result in a new or improved method for acquiring label information.
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.
Claim(s) 1-13 and 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asgarieh (U.S. Patent Application No. 2022/0067443) in view of Bao (CN 116782118).
Regarding Claim 1, Asgarieh discloses a method for acquiring label information, comprising:
determining, by a first network device1, a correction parameter (par [0032-0034], [0042-0043], Asgarieh – labeling via data source(s) can be corrected via learning; wherein learning correct label prediction for data from source 3, which is used for collaborating with the label decoders to allow learning model parameters for decoding data from source 1 and source 3 and target as well as models parameters) reported by a first terminal device using a global positioning system (GPS) (par [0046-0047], Asgarieh – the user device can correspond to a mobile device including a tablet, smart phone, global positioning system (GPS) receiver, and wearable computing device; wherein the a user device can also correspond to a computer with user interface elements that implement a personal computer or other type of workstation or terminal device); and
determining, by the first network device, label information corresponding to each data in a data set based on the correction parameter (par [0029-0030], Asgarieh - data from target source may correspond to a small data set yet with the most consistent/correct set of training data. With data from such sources, traditional multi-label classification solutions may lead to incorrect/incomplete predictions for samples as the small training data set from the target source is generally not sufficient for training deep and more accurate models. To overcome such shortcomings, the multi-task dual loop learning framework incorporate mechanism to allow learning, while training the traditional encoder and decoders, of mapping data to correct labels by leveraging the although small yet with consistent/correct set of training data from target to bootstrap the capability of correcting inconsistent and noisy labels… par [0045]), wherein the data set and the label information corresponding to each data in the data set are used to train a first model (par [0028-0030], Asgarieh – presented is a framework for meta-learning or learning-to-learn and is general and model agnostic, which is a multi-task dual loop learning scheme. Not only encoder/decoder models can be trained and optimized, the label decoders or models that learn-to-learn for producing correct labels can be trained smoothly using target task data… allow learning, while training the traditional encoder and decoders, of mapping data to correct labels by leveraging the although small yet with consistent/correct set of training data from target source to bootstrap the capability of correcting inconsistent and noisy labels from other unreliable data sources… also see par [0005-0006] and [0039], Asgarieh).
While Asgarieh discusses all of the elements as found above; however, Asgarieh is not as detailed as the examiner would like.
On the other hand, Bao discloses reported by a first terminal device, wherein the first terminal device is a positioning reference unit (PRU) (pgs.4-5, Bao - the present application provides a location management entity for receiving and transmitting data and performing operations of obtaining the first positioning information of the user equipment UE; obtaining the correlation ID of the positioning reference device PRU, wherein the PRU is related to the UE; determining the second positioning information of the PRU according to the PRU Correlation ID; and correcting the first locating information according to the second locating information.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bao’s positioning related device teachings into the Asgarieh system. A skilled artisan would have been motivated to combine in order to improve location accuracy and deliver appropriate information based on the reported data.
Regarding Claim 2, the combination of Asgarieh in view of Bao, disclose the method of claim 1, wherein, the correction parameter is determined based on an error value between actual information and estimated information of the first terminal device (par [0033], Asgarieh - target loss is directed to data from target source and is determined based on a label from target and a label predicted by decoder based on data from the target. In the first loop, the label decoders are assumed correct so that their predicted labels based on data from the respective sources are used as actual ground truth in learning the parameters of the encoder and decoders… par [0035], [0039], Asgarieh - the label decoder learns its model parameters based on discrepancy between a predicted label from the decoder using the updated encoder/decoder parameters and an actual ground truth label from the target data set. As discussed herein, the model parameters for the label decoders are learned by minimizing the target loss… par [0042], [0044], Asgarieh - the label decoder is assumed to be correct and is used to predict a ground truth (not actual) label that is used for updating the encoder and decoder parameters based on the source loss. In the outer loop, the label decoder learns and updates its label predictor parameters stored in by minimizing the target loss determined based on an actual ground truth label retrieved from target source).
Regarding Claim 3, the combination of Asgarieh in view of Bao, disclose the method of claim 2, wherein a number of the first terminal device is more than one, the correction parameter is determined based on an average value of error values between actual information and estimated information of the plurality of the first terminal devices (par [0033], Asgarieh - target loss is directed to data from target source and is determined based on a label from target and a label predicted by decoder based on data from the target. In the first loop, the label decoders are assumed correct so that their predicted labels based on data from the respective sources are used as actual ground truth in learning the parameters of the encoder and decoders… par [0035], [0039], Asgarieh - the label decoder learns its model parameters based on discrepancy between a predicted label from the decoder using the updated encoder/decoder parameters and an actual ground truth label from the target data set. As discussed herein, the model parameters for the label decoders are learned by minimizing the target loss… par [0042], [0044], Asgarieh - the label decoder is assumed to be correct and is used to predict a ground truth (not actual) label that is used for updating the encoder and decoder parameters based on the source loss. In the outer loop, the label decoder learns and updates its label predictor parameters stored in by minimizing the target loss determined based on an actual ground truth label retrieved from target source)2.
Claims 12 and 13 contain similar subject matter as claim 1 above; and are rejected under the same rationale with the addition of transmitting request information to a first network device by a second terminal device, wherein the request information is used to request one or more of the correction parameter (Bao – a request is received for obtaining the PRU correlation ID… FIG. 1 is a schematic diagram of an application scenario according to an embodiment of the present application. As shown in FIG. 1, the present embodiment provides a positioning system, which comprises a positioning server 110, a positioning management entity 120, a UE 130 and a PRU 140. The positioning server 110 may be, but not limited to, an entity device such as AMF, the positioning management entity 120 may be, but not limited to, an entity device such as LMF, the PRU 140 may be a device for loading the positioning module, and the PRU 140 may be, for example, a mobile terminal, a vehicle, a base station or the like. The location server 110 stores information such as location and performance of each PRU 140, which is defined as PRU information. When the location server 110 receives the location request from the UE 130, the PRU associated with the UE 130 is determined based on the stored PRU information, and the PRU 140 associated with the UE 130 refers to that the UE and the PRU are in the same cell).
Claim 20 contains similar subject matter as claims 1 and 12 above; and is rejected under the same rationale with the addition of par [0046] of Asgarieh for the disclosure of the claimed processor, memory, and transceiver for communication; and the addition of transmitting request information to a first network device, wherein the request information is used to request one or more of the correction parameter (Bao – a request is received for obtaining the PRU correlation ID… FIG. 1 is a schematic diagram of an application scenario according to an embodiment of the present application. As shown in FIG. 1, the present embodiment provides a positioning system, which comprises a positioning server 110, a positioning management entity 120, a UE 130 and a PRU 140. The positioning server 110 may be, but not limited to, an entity device such as AMF, the positioning management entity 120 may be, but not limited to, an entity device such as LMF, the PRU 140 may be a device for loading the positioning module, and the PRU 140 may be, for example, a mobile terminal, a vehicle, a base station or the like. The location server 110 stores information such as location and performance of each PRU 140, which is defined as PRU information. When the location server 110 receives the location request from the UE 130, the PRU associated with the UE 130 is determined based on the stored PRU information, and the PRU 140 associated with the UE 130 refers to that the UE and the PRU are in the same cell).
Claim 21 contains similar subject matter of claim 1; and is rejected under the same rationale.
Response to Arguments
Applicant’s arguments filed 07/08/2026, with respect to the arguments for claims 12 and 16 have been fully considered and are persuasive. The 102 rejection has been withdrawn.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHELCIE L DAYE whose telephone number is (571) 272-3891. The examiner can normally be reached on Monday-Friday 7:30-4:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Chelcie Daye
Patent Examiner
Technology Center 2100
September 19, 2026
/CHELCIE L DAYE/Primary Examiner, Art Unit 2161
1 Examiner Notes: See Asgarieh at par [0046] and [0048].
2 Examiner Notes: For details about multiple devices see par [0046], [0048].
