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 .
Claims 1, 3-6, 8-12, 14-16, and 21-26 are pending for examination. Claims 1, 12, and 21 are independent.
Response to Amendment
The office action is responsive to the amendments filed on 08/29/2025. As directed by the amendments claims 1, 8, 12, and 21-23 are amended. Claims 2, 7, 13, and 17-20 are cancelled.
Examiner Interview Summary
Examiner proposed an Examiners amendment to put all claims in condition for allowance. Applicant rejected the proposed amendments.
Response to Arguments
Applicant's arguments filed 08/29/2025 have been fully considered but they are not fully persuasive.
Applicant arguments regarding 35 U.S.C. § 101:
Examiner response: Applicant’s arguments, see page 9 of remarks, filed 08/29/2025, with respect to claims 1, 3-6, 8-12, 14-16, and 21-26 have been fully considered and are persuasive. The 35 U.S.C. § 101 rejection has been withdrawn.
Applicant arguments regarding 35 U.S.C. § 103:
In the rejection of now-canceled claim 2, the Office Action stated that "Ledvina discloses clustering methods and calibration data with contextual information," relying on paragraphs 103 and 137 and FIG. 13 of Ledvina.
The cited portion of Ledvina mentions that "[a] machine learning (e.g., clustering, classification, or deep learning) approach to this problem is particularly valuable when the transceivers for the car and key fob ranging are RF-based as opposed to LF-based." (Ledvina, paragraph [0103].)
Nevertheless, this is the only mention of "clustering" in Ledvina. Nowhere does Ledvina disclose or suggest to "identify data clusters in the calibration data according to the contextual information to produce clustering information," to "purge outlier data elements that are outliers with respect to the identified data clusters" and, in that context, to further "train a machine-learning model according to the calibration data, as purged, using the wireless data, the contextual information, and the clustering information as inputs and the ground truth data as output." Indeed, no clustering is performed in Ledvina in advance of and as an input to model training.
Desmond is also relied upon in the rejection of dependent claim 2. As cited in the Office Action, Desmond states: […]
Thus, Desmond discusses clustering using "a density clustering algorithm" of the vectors themselves, not based on "contextual information with respect to the mobile devices." Thus, Desmond taken in alleged combinate with the other references still fails to disclose or suggest at least to "identify data clusters in the calibration data according to the contextual information to produce clustering information," to "purge outlier data elements that are outliers with respect to the identified data clusters" and, in that context, to further "train a machine-learning model according to the calibration data, as purged, using the wireless data, the contextual information and the clustering information as inputs and the ground truth data as output."
For at least these reasons, the rejection of independent claim 1 should be reconsidered and withdrawn.
Examiner response: Examiner respectfully disagrees, Ledvina discloses the machine learning model can be a clustering method in para 0103. Para 0105-0108, Para 0119 and Fig 10-11, Ledvina describes training the machine learning model with RF/LF data (i.e., calibration/wireless data), ancillary data (e.g., GPS data) (i.e., contextual information), and truth data (i.e., ground truth). Desmond is relied on to explicitly disclose clustering operations such as identifying clusters, purging data (e.g. removing outliers), and training a model as purged. The combination discloses training a model with the specific data described in Ledvina and the clustering operations explicitly discloses by Desmond.
Claim Objections
Claims 1, 12, and 21 objected to because of the following informalities: Claim 1 line 21 recites "the RSSI information", instead of "(RSSI) wireless data". Claims 12, and 21 recite similar limitations.. Appropriate correction is required.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter for claims 21-26:
Ledvina et al. (US 20180234797 A1) teaches training a machine learning model with LF and RF distance information from a vehicle and mobile information for passive entry functions.
Smith et al. (US 20200196098 A1) teaches using UWB as truth data to facilitate training a machine learning model.
Chen et al. (US 2020/0219338 A1) teaches a calibration data server with a plurality of vehicles.
Desmond et al. (US 20210174196 A1) teaches clustering operations for training a machine learning model
None of these references taken alone or in combination with the prior art of record disclose the same steps for clustering and training machine learning models according to a device model of the mobile device across a plurality of vehicles to perform location-based phone-as-key functions, in combination with the remaining elements and features of the claimed invention. It is for these reasons that the applicants’ claims 21-26 defines over the prior art of record.
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.
Claim(s) 1, 3, 5, 8-12, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ledvina et al. (US 20180234797 A1, hereinafter "Ledvina") in view of Smith et al. (US 20200196098 A1, hereinafter "Smith"), Chen et al. (US 2020/0219338 A1, hereinafter "Chen"), and Desmond et al. (US 20210174196 A1, hereinafter "Desmond").
Regarding Claim 1
Ledvina discloses: A system for implementing a machine-learning localization scheme ([Fig 13] discloses a system for implementing the machine learning method.) comprising:
receive calibration data from the ([Para 0070-0079 and Fig. 6-7] describes receiving LF and RF distance information from a vehicle (i.e. calibration data).), the calibration data including received signal strength indication (RSSI) wireless data indicative of locations of mobile devices within the ([Para 0030-0032, 0073, 0102-0103 and Fig. 9] describes using the LF/RF signals (i.e. wireless data) to determine regions for a key fob (i.e. locations of a mobile device) within the vehicle. [Para 0006, 0068, 0070-0073 and Fig 7] describe a LF antenna providing RSSI data.), ([Para 0105-0108, Para 0119, and Fig 10] describes truth data (i.e., ground truth data) corresponding the mobile device being moved within a region.), and contextual information with respect to the mobile devices ([Para 0072, Para 0137, Fig 7, and Fig 13(1300)] describes an orientation of the mobile device, GPS location of the mobile device, and a power status indicator for the mobile device. Examiner interprets orientation, GPS location, and power states as all being contextual information with respect to the mobile device.);
([Para 0103, Para 0137, and Fig 13(1300)], Ledvina discloses machine learning including a clustering method and inputs such as calibration data with contextual information. Examiner interprets a clustering machine learning model as producing clustering information.);
train a machine-learning model according to the calibration data,using the wireless data, the contextual information, as inputs and the ground truth data as output ([Para 0105-0108, Para 0119 and Fig 10-11] describes training the machine learning model with RF/LF data (i.e., calibration/wireless data), ancillary data (e.g., GPS data) (i.e., contextual information), and truth data (i.e., ground truth). Examiner interprets truth data as corresponding to a correct classification (i.e., correct output). [Para 0103] discloses the machine learning as a clustering method.); and
responsive to an error rate for the machine-learning model being within an error target, provide the machine-learning model to the
utilize the machine-learning model to determine locations of the mobile devices using the RSSI information without use of UWB ToF data ([Para 0107-0108 and Para 0118] describe a machine learning model that classifies location of mobile devices (i.e. determine locations) using distance measurements from a LF antenna (i.e. RSSI information). [Para 0105] states “As examples, the distance measurements can be RF, LF, or both” Examiner interprets using LF measurements (i.e., RSSI information (see para 0006 and Fig7(705))) without using RF (i.e., UWB (see para 0041 and 0097)) as disclosing the limitation. [Para 0072-0074] also describes using LF system (i.e. RSSI information) exclusively when in close proximity to the vehicle. This discloses a scenario where LF is used exclusively without RF (i.e., without UWB).), and
perform one or more location-based phone-as-a-key functions based on the determined locations of the mobile devices ([Para 0091-0092 and Fig 8(840)] describes performing location-based functions (e.g. enable start buttons).).
Ledvina does not explicitly disclose: ultra-wide band (UWB) time of flight (ToF) data to use as ground truth;
However, Smith discloses in the same field of endeavor: ultra-wide band (UWB) time of flight (ToF) data to use as ground truth([Para 0114] describe using UWB as truth data (i.e., ground truth) to facilitate training 210 (i.e., machine learning model see para 0075). [Para 0029-0030] further describes truth data.); determine locations of the mobile devices using the RSSI information without use of UWB ToF data ([Para 0064] “A BTLE-only remote device 20 may be operable to process such maps but without UWB communications characteristics to refine RSSI-only range estimates. […] the locator 210 may generate location information based on BTLE communication characteristics without the UWB communication characteristics.”);
Ledvina and Smith are both analogous art to the present invention because both are from the same field of endeavor directed to locating mobile devices.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the System/Method for Enhanced Automotive Passive Entry disclosed by Ledvina with the method for Real-time location disclosed by Smith. One of ordinary skill in the art would have been motivated to make this modification in order to determine a location provided in the truth data within a degree of confidence (Para 0026, Smith).
Ledvina in view of Smith does not explicitly disclose: a plurality of vehicles; and a calibration data server, programmed to; wherein each vehicle of the plurality of vehicles is configured to utilize the machine-learning model;
However, Chen discloses in the same field of endeavor: a plurality of vehicles ([Para 0023, 0035, 0049-0051 and Fig 5(502)] Para 0023 states “motion command learning and distribution server 180 receives motion data and associated motion commands from a plurality of vehicles (e.g. , vehicles 502-1 through 502-N illustrated in FIG. 5), and may optionally also receive user, key fob, or other identifiers.”); and a calibration data server, programmed to ([Para 0023, 0034-0036, 0049-0051, Fig 1 and Fig 5] describes a distribution server (i.e. calibration data server).); wherein each vehicle of the plurality of vehicles is configured to utilize the machine-learning model; ([Para 0012, 0022-0023, 0035-0036, Fig 2(228), and Fig 5] describe vehicles that can perform machine learning processes (i.e. utilize the machine-learning) for motion commands.)
Ledvina, Smith, and Chen are analogous art to the present invention because they are from the same field of endeavor directed to vehicles and machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the System/Method for Enhanced Automotive Passive Entry disclosed by Ledvina with the method for Real-time location disclosed by Smith with the System for Key Fob commands disclosed by Chen. One of ordinary skill in the art would have been motivated to make this modification in order to transmit refined commands to vehicles (Para 0023, Chen).
Ledvina in view of Smith and Chen does not explicitly discloses: identify data clusters in the calibration data according to the contextual information to produce clustering information; purge outlier data elements that are outliers with respect to the identified data clusters; train a machine-learning model according to the using , and the clustering information as inputs and the ground truth data as output ;
However, Desmond discloses in the same field of endeavor: identify data clusters in the ([Para 0014 and Fig 5-6] “The method also includes clustering the plurality of vector representations into one or more clusters using a density clustering algorithm. The method also includes analyzing a vector space that includes the one or more clusters to identify at least one vector representation corresponding to an outlier data input and/or a mislabeled data input.” Examiner interprets clusters as having context.); purge outlier data elements that are outliers with respect to the identified data clusters ([Para 0014, 0035, and Fig 5-6] “The method also includes analyzing a vector space that includes the one or more clusters to identify at least one vector representation corresponding to an outlier data input and/or a mislabeled data input. The method also includes forming a new plurality of data inputs having associated labels by removing the outlier data input from the plurality of data inputs in response to identifying an outlier data input and relabeling the data input to have an associated label of a classification type of the predominant classification type of the vector representations in the same cluster as the vector representation corresponding to the mislabeled data input in response to identifying a mislabeled data input. ”); train a machine-learning model according to the ([Para 0035] “automatically by the processing system 100 ( e.g. , removing outliers from data used for training , relabeling an anomalous data point determined to be mislabeled , etc.) to create an improved set of data inputs and automatically retrain the model using the improved set of data inputs”), using , and the clustering information as inputs and the ground truth data as output; ([Para 0014, 0035, 0056, 0066-0068, and Fig 5-6] describe analyzing clusters, data inputs, and ground truth for training.)
Ledvina, Smith, Chen and Desmond are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the System/Method for Enhanced Automotive Passive Entry disclosed by Ledvina with the method for Real-time location disclosed by Smith with the System for Key Fob commands disclosed by Chen with the method for Ground Truth Quality for Machine Leaning models taught by Desmond. One of ordinary skill in the art would have been motivated to make this modification in order to develop more accurate machine learning models while reducing outlier data (Para 0014, Desmond).
Regarding Claim 12
Ledvina in view of Smith, Chen, and Desmond discloses: A method for implementing a machine-learning localization scheme comprising: (Claim 12 is a method claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 3
Ledvina in view of Smith, Chen, and Desmond discloses: The system of claim 1, wherein the calibration data server is further programmed to test the machine-learning model using test data to determine the error rate for the machine-learning model ([Para 0030-0031], Smith describes training/testing a machine learning algorithm with a validation data set (i.e. test data) to determine a degree of confidence (i.e. error rate).)
Regarding Claim 5
Ledvina in view of Smith, Chen, and Desmond discloses: The system of claim 3, wherein the calibration data server is further programmed to determine the error rate for the machine-learning model as being within the error target responsive to the machine-learning model achieving correct results data ([Para 0030], Smith describes training a machine learning model to be accurate to within the established degree of confidence (i.e. within error target).) in at least a predefined percentage of the test ([Para 0046], Desmond “separating the plurality of data inputs into training data and test data and successively training and testing the data to generate vector representations for each data input of the test data. For example, in some embodiments, the ground truth data may be split into five groups, each representing 20% of the total ground truth data and the model can be trained using 4 groups of data and then used to test and generate vector representations of the remaining group of data. The model can then be further trained using a different set of 4 groups and test and generate vector representations for the remaining 5th group of test data, and so on until each combination of 4 groups of data is used to train the model and each 5th group is used as test data to generate vector representations of each data input.”).
Regarding Claim 8
Ledvina in view of Smith, Chen, and Desmond discloses: The system of claim 1, wherein the ground truth data further includes BLUETOOTH Low Energy (BLE) distance measurement data. ([Para,0029-0030, 0075-0076, and 0114], Smith describe training with truth data including BLE. Examiner interprets truth data as high accuracy.).
Regarding Claim 9
Ledvina in view of Smith, Chen, and Desmond discloses: The system of claim 1, wherein the ground truth data includes is one or more of UWB phasing data ([Para 0114], Smith) or Wi-Fi ToF data ([Para 0044, 0068-0070, 0082-0082, 0114, and 0129], Ledvina).
Regarding Claim 10
Ledvina in view of Smith, Chen, and Desmond discloses: The system of claim 1, wherein the contextual information includes one or more of operating system versions of the mobile devices or battery levels of the mobile devices ([Para 0137 and Fig 13(1300)], Ledvina describes a power status indicator for the mobile device.).
Regarding Claim 11
Ledvina in view of Smith, Chen, and Desmond discloses: The system of claim 1, wherein the contextual information includes antenna characteristics defining offsets with respect to signal strengths for the mobile devices ([Para 0071, 0112], Ledvina describes signal strengths for the mobile device.).
Regarding Claim 14
(Claim 14 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Regarding Claim 15
(Claim 15 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ledvina et al. (US 20180234797 A1, hereinafter "Ledvina") in view of Smith et al. (US 20200196098 A1, hereinafter "Smith"), Chen et al. (US 2020/0219338 A1, hereinafter "Chen"), and Ma et al. (US 20190145784 A1, hereinafter "Ma").
Regarding Claim 4
Ledvina in view of Smith and Chen discloses: The system of claim 3,
Ledvina in view of Smith and Chen does not explicitly disclose: wherein the test data is a subset of the
However, Ma discloses in the same field of endeavor: wherein the test data is a subset of the ([Para 0111] describes splitting data into non-overlapping testing (i.e. test subset excluded from training.) and training sets.)
Ledvina, Smith, Chen and Ma are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the System/Method for Enhanced Automotive Passive Entry disclosed by Ledvina with the method for Real-time location disclosed by Smith with the System for Key Fob commands disclosed by Chen with the method for Vehicle Localization taught by Ma. One of ordinary skill in the art would have been motivated to make this modification in order to avoid overfitting data (Para 0111, Ma).
Claim(s) 6, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ledvina et al. (US 20180234797 A1, hereinafter "Ledvina") in view of Smith et al. (US 20200196098 A1, hereinafter "Smith"), Chen et al. (US 2020/0219338 A1, hereinafter "Chen") and Sascha et al. (EP 3806007 A1, hereinafter "Sascha").
Regarding Claim 6
Ledvina in view of Smith and Chen discloses: The system of claim 1, wherein the calibration data server is further programmed to: determining of the locations of the mobile devices ([Para 0112 and Para 0114], Ledvina disclose region decisions of mobile devices.);
Ledvina in view of Smith and Chen does not explicitly disclose: send the machine-learning model to a test subset of the plurality of vehicles; and determine the error rate to be within the error target responsive to receipt, from the test subset of the plurality of vehicles, of test information indicative of the machine-learning model performing more accurately at
However, Sascha discloses in the same field of endeavor: send the machine-learning model to a test subset of the plurality of vehicles ([Para 0039, 0042, Para 0057, Fig 1-2] describes a machine learning model that assigns a subset of assignments to vehicles (i.e., to a subset of vehicles that can perform the assignment) to evaluate said assignment (i.e., testing)); and determine the error rate to be within the error target responsive to receipt, from the test subset of the plurality of vehicles, of test information indicative of the machine-learning model performing more accurately at ([Para 0057-0059] describes evaluating the subset of assessments using an objective function (i.e. error rate) and provided the favorable results.).
Ledvina, Smith, Chen and Sascha are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the System/Method for Enhanced Automotive Passive Entry disclosed by Ledvina with the method for Real-time location disclosed by Smith with the System for Key Fob commands disclosed by Chen with the method for Assigning Vehicles Task Provided by a Machine Learning Model taught by Sascha. One of ordinary skill in the art would have been motivated to make this modification in order to perform a selection criterion based on the favorability of the respective assignment (Para 0008, Sascha).
Regarding Claim 16
(Claim 16 recites analogous limitations to claim 6 and therefore is rejected on the same ground as claim 6.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rosen (US 20150054639 A1) describes a mobile phone detection system (Abstract).
THIS ACTION IS MADE FINAL. 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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/TEWODROS E MENGISTU/Examiner, Art Unit 2127