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
Claims 1-20 have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/10/25 has been entered.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are moot in view of the new ground(s) of rejection. On 12/10/25, Applicant amended the independent claims. Applicant’s remarks address these amended features. See the new rejection with the new citations and motivation that address this new feature.
Claim Rejections - 35 USC § 103
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.
Claims 1-5, 7-12, 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Grosso (20180068350) in view of Gutierrez (20220215141).
Claims 1, 8, 15. Grosso discloses a system comprising:
one or more processors configured by executable instructions to perform operations including (Figs. 1, 2):
receiving, by the one or more processors, from a plurality of data sources associated with a plurality of service providers, data related to user interactions with information provided by the service providers to a plurality of users ([2, 6]);
determining, by the one or more processors, based at least on the received data, users associated with the user interactions with the information [2, 6];
determining, by the one or more processors, whether the users associated with the user interactions are new users or existing users based at least on accessing a user information data structure to compare user information of the users with user information in the user information data structure (actual and potential users [6], new customers at [21], customer lifetime value at [29, 62], new user at [83] and [86]);
using, by the one or more processors, a value-determining machine learning model to determine respective values of the received data associated with the new users (machine learning and new and existing customers at [29] and machine learning at [104]).
As noted in Applicant 12/10/25 remarks, Examiner notes description of the following feature at [44, 45] of Applicant Spec. Grosso does not explicitly disclose adjusting, by the one or more processors, at least one of the determined values of the received data based at least on applying a respective adjustment to change the least one value determined for the received data received from the data sources, wherein the respective adjustment to change the at least one value is based on empirically determined performance results determined for a corresponding respective data source of the plurality of data sources. However, Grosso discloses normalizing metrics [60, 127, 128] and adjusting settings [135, 230] and weighting combinations of test data [226]. And, Gutierrez discloses adjusting and modifying parameters and correlations of datasets and correlation parameters ([65, 84, 194], Fig. 4), adjusting multiple variables of different datasets ([67], Fig. 4), and adjusting and modifying and tuning datasets from different data sources [72, 150], and adjusting factors and agents/behaviors for models [95]. Also, note in Gutierrez a range of different datasets from different initial sources [67] and that there are multiple, different outside data sources [48] and [78] with selection of source data set and second source dataset and [163] with comparing multiple true source data sets. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Gutierrez different data sources and different datasets and adjusting values of the different datasets to Grosso’s adjusting settings and weighting and normalizing metrics. One would have been motivated to do this in order to enable better datasets for use in the system.
Grosso further discloses using a plurality of data synthetization techniques to generate synthetic data based at least in part on the adjusted data (see canned or simulated data at [63] and [101, 102]).
Grosso does not explicitly disclose using machine learning models to generate the synthetic data. However, Grosso discloses automatically generating the simulated data [63]. And, Grosso discloses using machine learning [104] for the processes of Grosso. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Grosso’s machine learning for processes to Grosso’s automatically generating simulated data. One would have been motivated to do this in order to better generate the data.
Grosso does not explicitly disclose each respective data synthetization machine learning model of the plurality of data synthetization machine learning models configured for generating synthetic data for a different respective group of one or more of the data sources from which corresponding adjusted data is received. However, Gutierrez discloses receiving an initial dataset and then generating a generating a new model for that dataset and then generating a new synthetic dataset (Figs. 3, 6; [63, 14, 77]). And, Gutierrez discloses source datasets in plural at [48] and different new datasets [49] and also First and second source data sets and selecting a source dataset [78] and groups of datasets that are used as a source data set [143, 151]. Hence, Gutierrez discloses multiple data sources that can get grouped into a dataset and that a dataset of many can get selected and that a different model is created for each selected dataset and that the model generated synthetic data for that selected dataset. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Gutierrez’s machine learning and synthetic data for datasets to Grosso’s machine learning. One would have been motivated to do this in order to better use machine learning.
Grosso further discloses determining an allocation of resources based at least in part on comparing the adjusted data and the synthetic data of the respective data sources of the plurality of data sources with the adjusted data and synthetic data of others of the respective data sources of the plurality of data sources ([58, 89, 90, 91]; note initial and optimal segmenting data at [150]; also for synthetic data see canned or simulated data at [63] and [101, 102]); and
sending at least one communication to allocate at least a portion of the resources based on determining the allocation of resources ([74] and maximizing at [58, 90]).
Claim 2, 9, 16. Grosso further discloses the system as recited in claim 1, the operations further comprising: using an allocation model for determining, at least in part, the allocation of resources, wherein the allocation model receives, as input, the adjusted data and the synthetic data of the respective data sources, and limits an amount of change in the allocation of resources based on one or more prior allocations of resources (see budget at [15]).
Claim 3, 10, 17. Grosso further discloses the system as recited in claim 1, the operations further comprising: using one or more bidder models to determine one or more bids for one or more of the service providers based at least in part on the determined allocation of resources; and communicating, to the one or more service providers, the one or more bids via the at least one communication (see bidding at [21]).
Claims 4, 11, 18. Grosso does not explicitly disclose training, using at least the adjusted data, the plurality of data synthetization machine learning models. However, Grosso discloses the machine learning and simulated/canned data above. And, Gutierrez further discloses synthetic data for training ([67], “[67]… a machine learning model may be trained in step 408 on the synthetic dataset generated”). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Gutierrez training and synthetic data to Grosso’s machine learning and simulated data. One would have been motivated to do this in order to better use machine learning.
Claim 5, 12, 19. Grosso further discloses the system as recited in claim 4, the operations further comprising: creating a plurality of validation data sets from a second portion of at least the adjusted data, each validation data set corresponding to a respective group of the different respective groups of one or more of the data sources (testing segments at [51] and testing at [63, 136] and valid results at [221]).
Grosso does not explicitly disclose creating a training data set from a first portion of at least the adjusted data; and validating the respective data synthetization machine learning models using the respective validation data set corresponding to the respective group to which the respective data synthetization machine learning model being validated corresponds. However, Grosso discloses the machine learning and simulated/canned data above. And, Gutierrez further discloses synthetic data for training [67] and validating (see compare and validate at Figs. 4, 6). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Gutierrez training and validation and synthetic data to Grosso’s machine learning and simulated data. One would have been motivated to do this in order to better use machine learning.
Claim 7, 14. Grosso further discloses the system as recited in claim 5, wherein the second portion of at least the adjusted data includes data received most recently within a past threshold period of time (see Grosso and 3 day moving average which is the most recent 3 days at [59]).
Claims 6, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grosso (20180068350) in view of Gutierrez (20220215141) in view of Zargahi (20200090001).
Claim 6, 13, 20. Grosso does not explicitly disclose the system as recited in claim 5, wherein the validating comprises determining an optimal amount of synthetic data to produce, respectively, for individual data sources of the respective groups of one or more data sources. However, Grosso discloses the machine learning and simulated/canned data above. And, Zargahi discloses machine learning and optimal amounts of synthetic data [58]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Zargahi machine learning and optimal synthetic data amounts to Grosso’s machine learning and simulated data. One would have been motivated to do this in order to better use machine learning (and Zargahi discloses generating simulated/synthetic data is resource intensive).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) Note the two allowed parents that the case is a CON of;
aa) Shen discloses lifetime value of customers and other features; Chen [123] customer lifetime value and Morgan disclose relevant features;
ab) Chauhan (20220108334) discloses synthetic data;
aa) note Chauhan (US 2022/0108334) in view of Wong (US 2017/0091813), and Tulloch (US 10,438,235) as prior art from the parent cases;
c)Goodsitt discloses limits on synthetic data making.
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/ARTHUR DURAN/Primary Examiner, Art Unit 3621 1/25/26