DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
Applicant's arguments filed 10/8/2025 have been fully considered but they are not persuasive.
On pages 8-9 of the Applicant’s Response, Applicant argues Shim and Campos fail to teach or suggest “storing the implicit data as part of a training data set, and performing additional training on the machine learning model, using the trained data set, to determine a motion state, wherein the motion state is used to determine a future visit state”. Further, “Campos uses a model to determine user behavior. On the other hand, claim 1 recites training a machine learning model to determine a motion state, not a behavior…”
The Examiner respectfully disagrees with the Applicant. Shim teaches storing the implicit data as training data set to perform training on the model to fine tune the model (see previously cited in the Office Action - see at least [0108], [0115]-[0117], [0125]-[0127], where at least some data are used as training data to fine-tune the model to improve the chance to rank correct places as visited places) and only lack the specific of teaching “using the trained data set, to determine a motion state, wherein the motion sate is used to determine a visit state”, which Applicant disagrees Campos teaches stated above. However, Campos does disclose the claimed feature as discussed in previous Office Action where at least the location information and various sensor data are used to determine the user state, which determine motion state of the user, given its BRI and is used to determine state of visit, among other data and results, frequently visited locations and the likes (see at least [0084]-[0086]). Given its current forms, the limitations argued, such as the motion state and visit state are claimed in a very broad and nonspecific ways that the combination of the teachings of Shim and Campos read on the claimed language. Therefore, claims 1-20 are properly rejected.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1-4, 6, 13, 14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shim et al. (US 2013/0226857 A1) in view of Campos et al. (US 2016/0260024 A1).
In regard to claim 1, Shim et al. discloses a computer storage medium comprising computer executable instruction that, when executed by at least one processor, cause the at least one processor to perform operations (see [0040] a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices, analytics server 122 in Fig. 1 and [0022] which discloses multi-processor system , microprocessor and [0024] data processor ) (database 124 in Fig. 1 and [0040] coupled to at least one of the one or more processors, the memory comprising computer executable instructions that, when executed by the at least one processor ( see [0040] a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices, and claim 9) comprising:
receiving aggregated sensor data for a mobile device, wherein the sensor data comprises at least movement data for the mobile device and network data for a venue (see at least [0041] discloses the clustering and analysis module 202 takes raw location data and sensor data as input and detects when a user has visited a place, and how a user transitions from one place to another place, [0016] discloses the inference pipeline system can be coupled to a data collection system which collects and validates location data from a mobile device. Collected user information includes location data such as latitude, longitude, or altitude determinations, sensor data from, for example, compass/bearing data, accelerometer or gyroscope measurements, and other information that can be used to help identify a user's location and activity. [0119], [0146] discloses data collected are aggregated for identifying locations) (see also [0026], [0027], [0033], [0121]-[0123] for similar reasonings);
processing, using a plurality of models, the aggregated sensor data to generate a feature set of implicit data (see [0033] wherein sensor data reading are inputting to generate probabilities that a user has visited a place, and [0034] discloses continuous tracking utilizing different techniques to predict the location of the user, [0035] shows different technique using location reading ordered by time stamp passes to a temporal clustering algorithm that produces a list of location clusters, wherein each cluster consists of a number of location reading that are chronologically continuous and geographically close to each other. The existence of cluster indicates that the user discloses location readings was relatively stationary during a certain period of time), then [0036] disclose after a cluster is identified from a user's location readings, the place database is queried for places nearby the cluster's centroid. This search uses a large radius in hope to mitigate the noise in location data and cover all the candidate places the user may be located. A feature generation process examines each candidate place and extracts features that characterize the place. The inference model takes the features of each candidate place and generates the probabilities of each candidate being the correct place, [0037] discloses to tune this inference model, a "ground truth," or process to confirm or more accurately determine place location, is created that includes multiple mappings from location readings to places. A machine learning module uses this ground truth as training and testing data set to fine tune the model. [0038] discloses generating the set of features from the aggregated data through the numbers of components, which are models, [0041], [0042], [0046]-[0048], [0057], where the models generates and classifies the movements and data relate to the location history recorded) (see also [0058]-[0097] for similar reasonings),
(Examiner notes that the original filed specification of the present application defines implicit data as not explicit data such as check-in data and the passive visit detection relies on passive collection of data from various sensors ([0012] of the original specification of the present application), which implies implicit data, can be other collected data that are not explicit, as defined. Shim uses various types of data including implicit data such as device movements and signals.)
wherein the feature set comprises a feature vector representing the movement data, the aggregated sensor data, and additional data associated with one or more candidate venues ([0038] discloses a number of features from the aggregated data are extracted using the components/models; [0041] discloses clustering and analysis module that are further aggregated into locations and movements data; [0046]-[0048] discloses component classify the movements, data associated with the locations and aggregated sensor data from the data);
Examiner notes that the feature vector is merely described as a set of data that represent various information within the data associated with the locations/venues visited ([0023]-[0025] of the original specification of the present application).
evaluating the feature set using a machine-learning model trained implicit data to detect a vehicle to determine whether a visit event has occurred (see at least [0015], [0018], where the inference models trained are used to recognize the places visited by a user, [0032]-[0037], [0058]-[0097], [0099], [0110], [0125], [0134], where the trained model results in the determination of a visit event) based upon implicit data comprising device signals ([0018], [0041], [0052], [0057], [0087], [0121], [0147], where various implicit data including device signals collected are used to make the determinations);
storing the implicit data as part of a training data set (see [0097]-[0098], [0111], where implicit data for training data set are stored in database); and perform additional training on the machine learning model, using the trained data set (see at least [0108], [0115]-[0117], [0125]-[0127], where at least some data are used as training data to fine-tune the model to improve the chance to rank correct places as visited places).
Shim et al. does not explicitly disclose using the trained data set, to determine a motion state, wherein the motion sate is used to determine a visit state. Campos et al. in the same field of the art discloses using the trained data set, to determine a motion state, wherein the motion state is used to determine a visit state (see [0034]-[0036], [0084]-[0086], teaches using the trained data to determine motion states of the user associate with visit states). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Shim et al. with the disclosure of Campos et al. to use the trained data set, to determine a motion state, wherein the motion state is used to determine a visit state, to improve the predictability and accuracy in determining possible site visits and activities.
In regard to claim 2, Shim et al. discloses detecting, by the mobile device, one or more events corresponding to the sensor data, wherein the one or more events correspond to at least one of movement events, purchase events, information delivery events and venue check-in events; and correlating the one or more events to the one or more features (see at least [0026], [0087], [0099]-[0102], [0116]-[0123]).
In regard to claim 3, Shim et al. discloses wherein the network data comprises one or more Wi-Fi signal strengths for the venue ((see at least [0026], [0087], [0099]-[0102], [0116]-[0123]).
In regard to claim 4, Shim et al. discloses wherein processing the aggregated sensor data comprises:
parsing the aggregated sensor data to identify the one or more Wi-Fi signal strengths (see at least [0026], [0087], [0099]-[0102], [0116]-[0123]); using a statistical model to evaluate the identified one or more Wi-Fi signal strengths (see at least [0026], [0087], [0099]-[0102], [0116]-[0123]); and
generating, by the statistical model, at least a portion of the feature set (see at least [0026], [0087], [0099]-[0102], [0116]-[0123])
In regard to claim 6, Shim et al. discloses observing a second set of network signals at a second time; and comparing the first set of network signals to the second set of network signals to determine an estimated distance traveled by the mobile device between the first time and the second time (see at least [0026], [0087], [0099]-[0102], [0116]-[0123]).
In regard to claim 13, Shim et al. discloses wherein the system further comprises a user interface operable to receive user input comprising at least one of a data label and training data (see at least [0026], [0087], [0099]-[0102], [0116]-[0123]).
In regard to claim 14, Shim et al. discloses a computer storage medium comprising computer executable instruction that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
aggregating, at a mobile device, sensor data, wherein the sensor data comprises location data for the mobile device, movement data for the mobile device, and network data for a venue (see at least [0041], [0016], [0026], [0027], [0033], [0121]-[0123], [0119], [0146] discloses data collected are aggregated for identifying locations);
processing, using a plurality of models, the sensor data in real time to generate a feature set for the sensor data, wherein the feature set for the sensor data includes implicit data ([0038] discloses generating the set of features from the aggregated data through the numbers of components, which are models, [0041], [0042], [0046]-[0048], [0057], where the models generates and classifies the movements and data relate to the location history recorded, see at least [0033]-[0037], [0058]-[0097]);
(Examiner notes that the original filed specification of the present application defines implicit data as not explicit data such as check-in data and the passive visit detection relies on passive collection of data from various sensors ([0012] of the original specification of the present application), which implies implicit data, can be other collected data that are not explicit, as defined. Shim uses various types of data including implicit data such as device movements and signals.)
wherein the feature set comprises a feature vector representing the movement data, the aggregated sensor data, and additional data associated with one or more candidate venues ([0038] discloses a number of features from the aggregated data are extracted using the components/models; [0041] discloses clustering and analysis module that are further aggregated into locations and movements data; [0046]-[0048] discloses component classify the movements, data associated with the locations and aggregated sensor data from the data);
Examiner notes that the feature vector is merely described as a set of data that represent various information within the data associated with the locations/venues visited ([0023]-[0025] of the original specification of the present application).
evaluate the feature set using a machine-learning model trained on implicit data to determine a visit state of the mobile device (see at least [0033]-[0037], [0058]-[0097], [0099], [0110], [0125]) based upon implicit data comprising device signals ([0018], [0041], [0052], [0057], [0087], [0121], [0147], where various implicit data including device signals collected are used to make the determinations);
storing the implicit data as part of a training data set (see [0097]-[0098], [0111], where implicit data for training data set are stored in database); and perform additional training on the machine learning model, using the trained data set (see at least [0108], [0115]-[0117], [0125]-[0127], where at least some data are used as training data to fine-tune the model to improve the chance to rank correct places as visited places)
Shim et al. does not explicitly disclose using the trained data set, to determine a motion state, wherein the motion state is used to determine a visit state. Campos et al. in the same field of the art discloses using the trained data set, to determine a motion state, wherein the motion sate is used to determine a visit state (see [0034]-[0036], [0084]-[0086], teaches using the trained data to determine motion states of the user associate with visit states). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Shim et al. with the disclosure of Campos et al. to use the trained data set, to determine a motion state, wherein the motion state is used to determine a visit state, to improve the predictability and accuracy in determining possible site visits and activities.
In regard to claim 17, Shim et al. discloses a computer storage medium comprising computer executable instruction that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
receiving, at a computing device, sensor data, wherein the sensor data comprises location data for a mobile device, movement data for the mobile device, and network data for a venue (see at least [0041], [0016], [0026], [0027], [0033], [0121]-[0123]);
processing the sensor data to generate a feature set for the sensor data (see at least [0033]-[0037], [0058]-[0097]);
wherein the feature set comprises a feature vector representing the movement data, the aggregated sensor data, and additional data associated with one or more candidate venues ([0038] discloses a number of features from the aggregated data are extracted using the components/models; [0041] discloses clustering and analysis module that are further aggregated into locations and movements data; [0046]-[0048] discloses component classify the movements, data associated with the locations and aggregated sensor data from the data);
Examiner notes that the feature vector is merely described as a set of data that represent various information within the data associated with the locations/venues visited ([0023]-[0025] of the original specification of the present application).
train a predictive model, using the feature set, to detect visit states of the mobile device (see at least [0032]-[0037], where machine learning uses the extracted data for training and testing to fine tune the model, [0058]-[0097], where the data is used to train and validate the inference models downstream, the predictive model, [0099], [0110], [0125], [0127], where the training data is used to predict location of a user).
Shim et al. does not explicitly disclose using the trained data set based on implicit data, to detect a motion state, wherein the motion state is used to determine one or more visit states. Campos et al. in the same field of the art discloses using the trained data set, to determine a motion state, wherein the motion state is used to determine one or more visit states (see [0034]-[0036], [0084]-[0086], teaches using the trained data to determine motion states of the user associate with visit states). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Shim et al. with the disclosure of Campos et al. to use the trained data set based on implicit data, to detect a motion state, wherein the motion state is used to determine one or more visit states, to improve the predictability and accuracy in determining possible site visits and activities.
12. Claim 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shim et al. (US 2013/0226857 A1) and Campos et al. (US 2016/0260024 A1) as applied to claim 4 above, and further in view of Lee Jung Su et al. (KR 2014-0000566 A).
In regard to claim 5, Shim et al. the limitations of claim 4 but does not explicitly disclose wherein evaluating the one or more Wi-Fi signal strengths comprises using the Wi-Fi signal strengths to determine a distance between the mobile device and the venue.
Lee Jung Su et al. discloses wherein evaluating the one or more Wi-Fi signal strengths comprises using the Wi-Fi signal strengths to determine a distance between the mobile device and the venue (see at least claims 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Shim et al. with the disclosure of Lee Jung Su et al. because such modification would provide a location-based service by inferring a location meaningful for a user.
13. Claim 7-12, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shim et al. (US 2013/0226857 A1) and Campos et al. (US 2016/0260024 A1) as applied to claim 1 above, and further in view of Milton et al. (US 2016/0019465 A1).
In regard to claims 7-9, Shim et al. meets the limitations of claim 1 but does not particularly disclose the limitations of claims 7-9.
Milton et al. discloses wherein evaluating the feature set comprises providing the feature set to a predictive model operable to detect a visit state of the mobile device for one or more venues (see Milton et al. [0027]); wherein the predictive model is an HMM operable to determine features, in the feature sets, relevant to detecting a visit event (see Milton et al. [0027]); wherein the features determined using the HMM are used as an initialization point for an EM algorithm operable to evaluate a set of unlabeled data (see Milton et al. [0027]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Shim et al. with the feature evaluation of Milton et al. because such modification would enable the merchant to analyze mobile-device location histories to characterize consumer behavior.
In regard to claims 10-11, the combination of Shim et al. and Milton et al. discloses wherein the predictive model is implemented on the mobile device, and the predictive model is operable to detect visit states in real time (see Milton et al. [0027]); wherein the predictive model is implemented on a remote device operable to train the predictive model using labeled data and perform big data analysis (see Milton et al. [0027]).
In regard to claim 12, the combination of Shim et al. and Milton et al. discloses wherein the method further comprises generating a set of observations for the aggregated sensor data, wherein the set of observations are indicative of a correlation between one or more features of the aggregated sensor data and a visit state (see Milton et al. claim 1).
In regard to claim 18, the combination of Shim et al. and Milton et al. discloses wherein the sensor data comprises a set of labeled data (see Milton et al. claim 1), and wherein the predictive model uses the set of labeled data to determine correlations between the set of labeled data and the one or more visit states (see Milton et al. [0032]).
In regard to claims 19-20, the combination of Shim et al. and Milton et al. discloses wherein the determined correlations are stored in one or more data stores for use with subsequent training of the predictive model (see Milton et al. claim 1); wherein an instance of the trained predictive model is transmitted to the mobile device. (see Milton et al. claim 1).
14. Claims 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shim et al. (US 2013/0226857 A1) and Campos et al. (US 2016/0260024 A1) as applied to claim 14 above and further in view of Lee Jung Su et al. (KR 2014-0000566 A).
In regard to claim 15, Shim et al. discloses wherein the sensor data comprises telemetry data (see at least [0016], [0027], [0035]).
The combination of Shim et al. and Campos et al. does not explicitly disclose wherein evaluating the feature set comprises comparing sensor data for the first time period to sensor data for the second time period.
Lee Jung Su et al. discloses the above limitation (see at least claim 17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Shim et al. and Campos et al. with the disclosure of Lee Jung Su et al. because such modification would provide a location-based service by inferring a location meaningful for a user.
In regard to claim 16, Shim et al., as modified, discloses wherein evaluating the feature set further comprises correlating features in the feature set to one or more events, wherein the one or more events correspond to at least one of movement events, purchase events, information delivery events and venue check-in events (see [0079]-[0082], [0099], [0103], [0117], such as movements, purchase events, and location check-in events).
Conclusion
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2016/0162016 A1 Lee et al. discloses using machine learning model to determine the motion state and current state of the user device.
US 2017/0241786 A1 Ohira et al. discloses estimating the motion and state of the mobile device based on learned model.
US 2017/0193797 A1 Gschwind et al. discloses a tracking system of a user based on electronic device noise profiles determining using a smart training device the motion states and visit states of the user while the user navigate the environments.
US 2018/0249435 A1 Yu et al. discloses a system recognizing user activities includes places visited using implicit data.
US 2013/0254227 A1 Shim et al. discloses a data collection to validate location data system for determining visit event occurrences based on extracted feature set and train the models using the collected/aggregated data for downstream to improve accurate prediction.
US 2014/0248911 A1 Rouda, Jr. discloses a mobile system that identify feature vectors having movement vectors, sensor data and candidate venues data.
US 2016/0345163 A1 Monaghan et al. discloses a location services devices identifying movement vectors for every device and venue data providing to visitors associate with the devices.
US Patent No 10,078,852 discloses location information identifies a plurality of different locations at which each of the user devices was located. From the location information, a plurality of chains of locations visited by each of a plurality of users are extracted. The online system generates one or more location pairs based on the chain of locations, where each location pair includes a first location and a second location to which there is a high probability a user will travel if the user is located at the first location. The location pairs are used for a variety of applications, such as for advertising to users based on locations and for providing insights into the movements of users.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sze-Hon Kong whose telephone number is (571)270-1503. The examiner can normally be reached 9 AM-5 PM Mon-Fri.
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/SZE-HON KONG/Primary Examiner, Art Unit 3657