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 responds to Applicant’s Arguments/Remarks filed 05/13/2026. Claims 1, 18, and 20 have been amended. Claims 1-20 are now pending in this Application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-6, 9-18 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Allowable Subject Matter
Claims 7-8 and 19 objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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) 1-6, 10, 12, 16-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wittle (U.S. Pub No. 2018/0307760 A1), and in view of Edalur et al (U.S. Pub No. 2019/0121897 A1).
As per claim 1, Wittke discloses a method, comprising:
receiving, from a user, an input query related to user interactions with a platform for one or more users of the platform (par [0003, 0089, 0091] receive user input);
processing the input query to select a data source of a plurality of data sources to be used for responding to the input query, comprising (Par [0004]):
providing a model input comprising at least two or more characteristics of the plurality data source to a model to obtain an output from the model, wherein a plurality of data sources for the platform comprise the first data source and the one or more second data sources, and wherein the selected data source is one of the plurality of data sources for the platform, and wherein the plurality of data sources comprise at least an event data source and an aggregated data source (Par [0004-0007, 0026-0027]);
obtaining an output form the model, wherein the output comprises a likelihood that a first result corresponding to the input query obtained using a first data source has a higher accuracy than each of one or more second results corresponding to the input query obtained using one or more second data sources, (Par [0004-0007, 0026-0027]);
obtaining, using the selected data source, a result that describes user interactions with the platform and that is responsive to the input query (Par [0007]); and
providing the result to the user (par [0017]).
Wittke discloses user selecting source. However, Wittke does not explicitly disclose wherein each data source of the plurality of data sources stores data related to user interaction with the platform; selecting the data source to be used for responding to the input query based on the output from the model by selecting the data source based in on the likelihood that the first result corresponding to the input query obtained using the first data source has a higher accuracy than each of the one or more second result corresponding to the input query obtained using the one or more second data sources.
However, Edalur discloses wherein each data source of the plurality of data sources stores data related to user interaction with the platform; selecting the data source to be used for responding to the input query based on the output from the model by selecting the data source based in on the likelihood that the first result corresponding to the input query obtained using the first data source has a higher accuracy than each of the one or more second result corresponding to the input query obtained using the one or more second data sources (par [0011-0012, 0019-0021]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Edalur into the teachings of Wittke in order to lower production cost, improve reliability and enhanced safety (Par [0002]).
As per claim 2, Wittke discloses the method of claim 1, wherein the input query specifies one or more of: a type of interaction event for the user interactions, a time period for the user interactions, a geographic location for the user interactions, a count of the user interactions, or a frequency of the user interactions (Par [0086-0087, 0098]).
As per claim 3, Wittke discloses the method of claim 1, wherein the event data source comprises, for each interaction event of a plurality of interaction events, one or more features and corresponding feature values for the respective interaction event (Par [0087]).
As per claim 4, Wittke discloses the method of claim 3, wherein the one or more features comprise any one or more of: a geographic location for the respective interaction event, a time of the respective interaction event, a description of the respective interaction event, or an identifier for the respective interaction event (par [0087]).
As per claim 5, Wittke discloses the method of claim 1, wherein the aggregated data source comprises a plurality of aggregated results, wherein each of the aggregated results corresponds to a potential input query (Par [0012-0013]).
As per claim 6, Wittke discloses the method of claim 5, wherein at least one of the aggregated results is generated by aggregating corresponding feature values for two or more interaction events over at least one feature (Par [0017]).
As per claim 9, Wittke discloses the method of claim 1, wherein selecting the data source to be used for responding to the input query based on the output from the model comprises; determining that the likelihood meets a threshold likelihood; and in response, selecting the first data source to be used for responding to the input query (Par [0003-0004]).
As per claim 10, Wittke discloses the method of claim 1, wherein the first data source is the event data source and the one or more second data sources comprise the aggregated data source (Par [0012-0013]).
As per claim 12, Wittke discloses the method of claim 10, wherein the model comprises, for each combination of characteristic values for the two or more characteristics, a respective probability representing the likelihood that a first result corresponding to the input query obtained using the event data source has a higher accuracy than a second result corresponding to the input query obtained using the aggregated data source (Par [0005-0007, 0026-0027]).
As per claim 13, Wittke discloses the method of claim 1, wherein the model input further comprises any one or more of: one or more features of the input query, or one or more characteristics of the platform (Par [0005-0007, 0026-0027]).
As per claim 14, Wittke discloses the method of claim 13, wherein the model has been trained to output a likelihood that a first result corresponding to the input query obtained using the first data source has a higher accuracy than each of one or more second results corresponding to the input query obtained using one or more second data sources (Par [0005-0007, 0026-0027]).
As per claim 15, Wittke discloses the method of claim 14, wherein the model is a trained classifier that has been trained on training data comprising a plurality of training examples, each comprising at least a model input, and a ground-truth label identifying one of the plurality of data sources, wherein a first measured result corresponding to the input query obtained using the identified data source has a higher accuracy relative to one or more second measured results (Par [0005-0007, 0026-0027]).
As per claim 16, Wittke discloses the method of claim 1, wherein obtaining a result corresponding to the input query using the selected data source comprises: obtaining a query from the input query; and querying the selected data source using the query (Par [0005-0007, 0026-0027]).
As per claim 17, Wittke discloses the method of claim 1, wherein the plurality of data sources for the platform comprise data related to user interactions with the platform for a window of time (Par [0005-0007, 0026-0027]).
As per claim 18, Wittke discloses a system comprising:
one or more processors; and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processor to carry out operations comprising (Par [0088]):
receiving, from a user, an input query related to user interactions with a platform for one or more users of the platform (par [0003, 0089, 0091] receive user input);
processing the input query to select a data source of a plurality of data sources to be used for responding to the input query comprising (Par [0004]):
providing a model input comprising at least two or more characteristics of the plurality data source to a model to obtain an output from the model, wherein a plurality of data sources for the platform comprise the first data source and the one or more second data sources, and wherein the selected data source is one of the plurality of data sources for the platform, and wherein the plurality of data sources comprise at least an event data source and an aggregated data source (Par [0004-0007, 0026-0027]);
obtaining an output form the model, wherein the output comprises a likelihood that a first result corresponding to the input query obtained using a first data source has a higher accuracy than each of one or more second results corresponding to the input query obtained using one or more second data sources, (Par [0004-0007, 0026-0027]);
obtaining, using the selected data source, a result that describes user interactions with the platform and that is responsive to the input query (Par [0007]); and
providing the result to the user (par [0017]).
Wittke discloses user selecting source. However, Wittke does not explicitly disclose wherein each data source of the plurality of data sources stores data related to user interaction with the platform; selecting the data source to be used for responding to the input query based on the output from the model by selecting the data source based in on the likelihood that the first result corresponding to the input query obtained using the first data source has a higher accuracy than each of the one or more second result corresponding to the input query obtained using the one or more second data sources.
However, Edalur discloses wherein each data source of the plurality of data sources stores data related to user interaction with the platform; selecting the data source to be used for responding to the input query based on the output from the model by selecting the data source based in on the likelihood that the first result corresponding to the input query obtained using the first data source has a higher accuracy than each of the one or more second result corresponding to the input query obtained using the one or more second data sources (par [0011-0012, 0019-0021]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Edalur into the teachings of Wittke in order to lower production cost, improve reliability and enhanced safety (Par [0002]).
As per claim 20, Wittke discloses a computer readable storage medium carrying instructions that, when executed by one or more processors, cause the one or more processors to carry out operations comprising:
receiving, from a user, an input query related to user interactions with a platform for one or more users of the platform (par [0003, 0089, 0091] receive user input);
processing the input query to select a data source to be used for responding to the processing the input query to select a data source of a plurality of data sources to be used for responding to the input query comprising (Par [0004]):
providing a model input comprising at least two or more characteristics of the plurality data source to a model to obtain an output from the model, wherein a plurality of data sources for the platform comprise the first data source and the one or more second data sources, and wherein the selected data source is one of the plurality of data sources for the platform, and wherein the plurality of data sources comprise at least an event data source and an aggregated data source (Par [0004-0007, 0026-0027]);
obtaining an output form the model, wherein the output comprises a likelihood that a first result corresponding to the input query obtained using a first data source has a higher accuracy than each of one or more second results corresponding to the input query obtained using one or more second data sources, (Par [0004-0007, 0026-0027]);
obtaining, using the selected data source, a result that describes user interactions with the platform and that is responsive to the input query (Par [0007]); and
providing the result to the user (par [0017]).
Wittke discloses user selecting source. However, Wittke does not explicitly disclose wherein each data source of the plurality of data sources stores data related to user interaction with the platform; selecting the data source to be used for responding to the input query based on the output from the model by selecting the data source based in on the likelihood that the first result corresponding to the input query obtained using the first data source has a higher accuracy than each of the one or more second result corresponding to the input query obtained using the one or more second data sources.
However, Edalur discloses wherein each data source of the plurality of data sources stores data related to user interaction with the platform; selecting the data source to be used for responding to the input query based on the output from the model by selecting the data source based in on the likelihood that the first result corresponding to the input query obtained using the first data source has a higher accuracy than each of the one or more second result corresponding to the input query obtained using the one or more second data sources (par [0011-0012, 0019-0021]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Edalur into the teachings of Wittke in order to lower production cost, improve reliability and enhanced safety (Par [0002]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wittke (U.S. Pub No. 2018/0307760 A1), and Edalur et al (U.S. Pub No. 2019/01218976 A1), and further in view of NI et al (U.S. Pub No. 2023/0381542).
As per claim 11, Wittke discloses the method of claim 10, wherein the two or more characteristics of the plurality of data sources comprise for the event data source and a data loss percentage for the aggregated data source (Par [0026-0027]).
Wittke does not explicitly disclose a sampling ratio.
However, Ni discloses a sample ratio (Par [0173, 0176).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the feature as disclosed in Ni into the teaching of Wittke in order to improve the system (Par [0003]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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July 23, 2026
/THU N NGUYEN/Examiner, Art Unit 2154
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