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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/9/2026 has been entered.
Response to Arguments
Applicant's arguments filed 4/13/2026 have been fully considered but they are not persuasive.
Applicant argues about “consistency of the data source” without claiming consistency. Remarks 14. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “consistency of the data source”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The “based on data synchronization mechanism” does not require complete copies or consistent data across the off-line and on-line data stores. Applicant repeats this style of argument with “consistency of the calculation processes”. Remarks 15. The unified script language is not described in the specification, it’s not a term of art, and it doesn’t require “consistency”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 2, 4, 6, 8, 10-15, 17, 19, 22-24 and 27-29 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant claims a unified script language. This is not a term of art, and it is not described in the specification.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 6, 8, 10-15, 17, 19, 22-24 and 27-29 are rejected under 35 U.S.C. 102(a)(1) as being described by US20200059753A1 to Liang et al.
Liang teaches claims 1, 14, 27, 28 and 29. A method for assisting online prediction using a machine learning model, comprising:
setting an online data storage system and an offline data storage system, wherein the online data storage system is configured to store at least part of data used for feature calculation in an online environment, and the offline data storage system is configured to store at least part of data used for feature calculation in an offline environment, and the feature calculation in the online environment and the feature calculation in the offline environment are performed based on a unified script language; (Liang fig. 1 online system 120, offline system 110 and feature engineering module in system 110. This isn’t a term of art and applicant hasn’t defined unified script language to mean something other than just any programming language. Liang is computer executed so it uses a unified script language.)
storing data into both of the online data storage system and the offline data storage system based on a data synchronization mechanism; (Liang fig. 1 systems 110/120 both have databases, databases have data stored in them. Liang para 70 “off-line prediction subsystem 110 configured to generate off-line location predictions, which are stored in a prediction library for querying by the document server, and an on-line prediction subsystem 120 configured to generate on-line (or real-time) location predictions, which are provided to the document server in real-time” Liang para 72 “mechanism are also put in place such that when any of the one or more links are clicked, or when the mobile user download an app or make a purchase from a linked webpage, a signal is also sent from the mobile device to the document server in the background either directly or indirectly so that the document server can keep track of the clicks/calls or secondary actions made in response to the impression.” The document server is the data synchronization mechanism.)
acquiring at least part of the data required by online feature calculation from the online data storage system, in response to an online prediction request; (Liang para 46 “The on-line prediction subsystem 120 further includes an on-line prediction module configured to build a set of features for a particular mobile device, in response to receiving in real-time a request associated with the particular mobile device…”)
wherein the online data storage system and the offline data storage system are configured to store multiple types of data, and the multiple types of data comprise one or more of static feature data, statistical feature data and real-time data, (Liang abs “off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events…. The system further comprises an on-line prediction subsystem configured to construct a feature vector using the off-line prediction results and recently detected location events….” The location events include the static data in Liang para 58, statistical data in Liang para 77 and real-time data in Liang para 74.)
the static feature data docs not change or does not change frequently, Liang para 58 “A defined area according to certain embodiments can be a static circle around a business location, e.g. a fence obtained using offline index databases such as InfoUSA…”)
the statistical feature data is obtained from data within a predetermined period of time, (Liang para 78 “aggregated location events associated with each triggered geo-block (e.g., GBx) or brand (e.g., Bx) includes… average length of stay per visit, etc….”)
the real-time data is generated in real time. (Liang para 74 “the off-line data manager is configured to extract a set of mobile device data corresponding to location events in each of a plurality of time periods from entries in the request log having time stamps in the corresponding time period.”)
Liang teaches claims 2 and 15. The method according to claim 1, further comprising:
setting a data acquisition manner corresponding to each type of data respectively; (Liang para 58, 78 and 74 teach using different manners to acquire data, see below.)
acquiring each type of data using the data acquisition manner corresponding to the type of data, (Liang para 58, 78 and 74 teach using different manners to acquire data, see below.)
wherein the data acquisition manner corresponding to the static feature data is acquiring the static feature data periodically, (Liang para 58 “A defined area according to certain embodiments can be a static circle around a business location, e.g. a fence obtained using offline index databases such as InfoUSA…” Liang para 70 “the information server is a computer server, e.g., a web server, backed by a database server that information sponsors use to periodically update the content thereof and may store information documents.”)
the data acquisition manner corresponding to the statistical feature data is performing statistic on data within a predetermined period of time to obtain the statistical feature data, (Liang para 77 “The off-line data manager further includes… an aggregator configured to aggregate the location events …” Liang para 78 “aggregated location events associated with each triggered geo-block (e.g., GBx) or brand (e.g., Bx) includes… average length of stay per visit, etc….”)
the data acquisition manner corresponding to the real-time data is acquiring data generated in real time. (Liang para 74 “the off-line data manager is configured to extract a set of mobile device data corresponding to location events in each of a plurality of time periods from entries in the request log having time stamps in the corresponding time period.”)
Liang teaches claims 4 and 17. The method according to claim 2, wherein the static feature data is stored in a static feature data source, and (Liang para 58 “A defined area according to certain embodiments can be a static circle around a business location, e.g. a fence obtained using offline index databases such as InfoUSA…”)
the storing data into both of the online data storage system and the offline data storage system comprises:
sending the static feature data in the static feature data source to the online data storage system, which sends the static feature data to the offline data storage system; or (Applicant claims in the alternative, so this claim does not need to be taught for the whole claim to be taught.)
sending the static feature data in the static feature data source to the offline data storage system, which sends the static feature data to the online data storage system; or (Liang fig. 1 shows mobile data storage in offline system 110, being transferred to online system 110.)
sending the static feature data in the static feature data source to each of the online data storage system and the offline data storage system. (The above sections show that the document server, Fig. 1, sends this data to both online and offline systems.)
Liang teaches claims 6 and 19. The method according to claim 2, wherein the data within the predetermined period of time is stored in a statistical feature data source, and (Liang para 77 “The off-line data manager further includes… an aggregator configured to aggregate the location events …” Liang para 78 “aggregated location events associated with each triggered geo-block (e.g., GBx) or brand (e.g., Bx) includes… average length of stay per visit, etc….”)
the storing data into both of the online data storage system and the offline data storage system comprises:
sending the data within the predetermined period of time in the statistical feature data source to the offline data storage system, performing statistic on the data within the predetermined period of time by an offline feature calculation module to obtain the statistical feature data; (Liang para 77 “The off-line data manager further includes… an aggregator configured to aggregate the location events …” Liang para 78 “aggregated location events associated with each triggered geo-block (e.g., GBx) or brand (e.g., Bx) includes… average length of stay per visit, etc….”)
storing the statistical feature data to the offline data storage system, and sending the statistical feature data to the online data storage system by the offline data storage system. (The above sections show that the document server, Fig. 1, sends this data to both online and offline systems.)
Liang teaches claim 8. The method according to claim 2, wherein the real-time data is stored in a real-time data source, and
the storing data into both of the online data storage system and the offline data storage system comprises: (Liang para 74 “the off-line data manager is configured to extract a set of mobile device data corresponding to location events in each of a plurality of time periods from entries in the request log having time stamps in the corresponding time period.”)
sending the real-time data in the real-time data source to the online data storage system, which sends the real-time data to the offline data storage system; or (Applicant claims in the alternative, so this claim does not need to be taught for the whole claim to be taught.)
sending the real-time data in the real-time data source to the offline data storage system, which sends the real-time data to the online data storage system; or (Liang fig. 1 shows mobile data storage in offline system 110, being transferred to online system 110.)
sending the real-time data in the real-time data source to each of the online data storage system and the offline data storage system. (The above sections show that the document server, Fig. 1, sends this data to both online and offline systems.)
Liang teaches claims 9 and 22. The method according to claim 1, further comprising:
processing the data acquired using a first processing script to obtain an online estimation sample; and (First script is the prediction model in Liang fig. 1 120.)
performing a prediction on the online estimation sample using an online prediction service based on the machine learning model to obtain an online prediction result. (Liang abs “generate on-line prediction results by applying the on-line prediction model to the feature vector.”)
Liang teaches claims 10 and 23. The method according to claim 9, wherein the online prediction request comprises partial feature data (Liang para 108 “prediction time frames…”) required by a prediction on a target object, (Liang para 108 “set of real-time prediction features are constructed from data corresponding to location events in the prediction time period PTPr, and from the plurality of off-line prediction results associated with the particular mobile device and corresponding to the off-line prediction time frames PTF1, PTF2, PTF3,” The partial data are the prediction time period.) the data acquired comprises static feature data, statistical feature data and real-time data, the static feature data does not change or does not change frequently, the statistical feature data is obtained from data within a predetermined period of time by a predetermined statistical manner, the real-time data is generated in real time, and (Liang para 54, 77 and 78.)
the processing the data acquired using the first processing script comprises:
performing real-time feature calculation on the real-time data using the first processing script to obtain real-time feature data; (Liang para 108 “the set of real-time prediction features are constructed from data corresponding to location events in the prediction time period PTPr…”)
performing a calculation on or splicing the real-time feature data, the static feature data, the statistical feature data and the partial feature data comprised in the online prediction request to obtain the online estimation sample. (Liang para 109 “apply the real-time prediction model to the set of real-time prediction features to obtain prediction results for the particular mobile device…”)
Liang teaches claim 11. The method according to claim 9, further comprising:
acquiring an online feedback result on the online prediction request; (Liang para 173 “the geo-block-based targeting module 3610 also monitors feedbacks indicating whether the document associated with the one or more information campaigns has been delivered to (or impressed opon) the related mobile device and provides the feedback to the real-time pacing estimation module 3630.”
splicing the online feedback result and feature data obtained by processing data from the offline data storage system using a second processing script to obtain a training sample, wherein the second processing script and the first processing script are obtained by translation based on a same script; (Liang para 106 “The rolling database is configured to store short-term rolling data of the feedbacks and the processed requests.” The feedback is taught as separate from other feature data, so the feedback is spliced from other features.)
training the machine learning model using the training sample. (Liang para 105 “the training module is further configured to employ machine learning approaches to train a real-time prediction model using the real-time prediction training feature space…” Feedback is included in the data to make the training feature space, see Liang para 81 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data corresponding to the training time period TTP,…”)
Liang teaches claim 12. The method according to claim 11, wherein the online prediction request comprises partial feature data required (Liang para 108 “prediction time frames…”) by a prediction on a target object, (Liang para 108 “set of real-time prediction features are constructed from data corresponding to location events in the prediction time period PTPr, and from the plurality of off-line prediction results associated with the particular mobile device and corresponding to the off-line prediction time frames PTF1, PTF2, PTF3,” The partial data are the prediction time period.) the data acquired from the offline data storage system comprises static feature data, statistical feature data and real-time data, the static feature data does not change or does not change frequently, the statistical feature data is obtained from data within a predetermined period of time by a predetermined statistical manner, the real-time data is generated in real time and stored in the offline data storage system, and (Liang para 54, 77 and 78.)
the processing the data from the offline data storage system using the second processing script comprising:
performing offline feature calculation on the real-time data using the second processing script to obtain real-time feature data; and (Liang para 81 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data corresponding to the training time period TTP…”)
performing a calculation on or splicing the online feedback result, the real-time feature data, the static feature data, the statistical feature data and the partial feature data comprised in the online prediction request to obtain the training sample. (Liang para 91 “the set of features for the mobile device may include other features, such as mobility features and feedback features.” Liang para 81 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data corresponding to the training time period TTP…” The feedback is taught as separate from other feature data, so the feedback is spliced from other features.)
Liang teaches claim 13. The method according to claim 12, further comprising:
verifying data acquired from the online prediction request. (Liang para 131 “The location module outputs verified or derived mobile device location in the form of, for example, latitude/longitude (lat/long), which is then processed by a block Lookup module.”)
Liang teaches claim 24. The device according to claim 22, further comprising:
a processor, configured to acquire an online feedback result on the online prediction request; and (Liang para 72 “The document server provides data of such feedback events (i.e., impressions, clicks/calls, and secondary actions) to buffer 2, which buffers and outputs the data to a feedback log.”)
an offline feature calculation module, configured to splice the online feedback result and feature data obtained by processing data from the offline data storage system (Liang para 91 “the set of features for the mobile device may include other features, such as mobility features and feedback features.” Liang para 81 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data corresponding to the training time period TTP…” The feedback is taught as separate from other feature data, so the feedback is spliced from other features.) using a second processing script to obtain a training sample, (Liang para 91 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data…” This second script is a second script because it is in the offline system, not the online system.) wherein the second processing script and the first processing script are obtained by translation based on a same script; and (Liang para 107 teaches that the real-time prediction module has a first script for extracting features, “The real-time prediction module is configured to, in response to a processed request output by the front-end server, construct a set of real-time prediction features from relevant data in the rolling database…” Liang para 91 teaches the second feature engineering module. They both look at time frames, “real-time prediction time frame” and “off-line prediction time frames…” Liang 107-108. In this way, the feature extractors/engineering modules are translations of the same script. Also translating a script doesn’t really mean anything and it’s not defined in the specification.)
wherein the processor is further configured to train the machine learning model using the training sample, (Liang fig. 1 model training module in off-line system 110.)
wherein the online prediction request comprises partial feature data required (Liang para 108 “prediction time frames…”) by a prediction on a target object, (Liang para 108 “set of real-time prediction features are constructed from data corresponding to location events in the prediction time period PTPr, and from the plurality of off-line prediction results associated with the particular mobile device and corresponding to the off-line prediction time frames PTF1, PTF2, PTF3,” The partial data are the prediction time period.) the data acquired from the offline data storage system comprises static feature data, statistical feature data and real-time data, the static feature data does not change or does not change frequently, the statistical feature data is obtained from data within a predetermined period of time by a predetermined statistical manner, the real-time data is generated in real time and stored in the offline data storage system, and (Liang para 54, 77 and 78.)
the offline feature calculation module is configured to perform offline feature calculation on the real-time data using the second processing script to obtain real-time feature data, (Liang para 81 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data corresponding to the training time period TTP…”) and perform a calculation on or splice the online feedback result, the real-time feature data, the static feature data, the statistical feature data and the partial feature data comprised in the online prediction request to obtain the training sample. (Liang para 91 “the set of features for the mobile device may include other features, such as mobility features and feedback features.” Liang para 81 “the feature engineering module is configured to construct a training feature space for the location group using at least the mobile device data corresponding to the training time period TTP…” The feedback is taught as separate from other feature data, so the feedback is spliced from other features.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Austin Hicks whose telephone number is (571)270-3377. The examiner can normally be reached Monday - Thursday 8-4 PST.
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, Mariela Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AUSTIN HICKS/ Primary Examiner, Art Unit 2142
Prosecution Insights