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 .
Response to Amendment
The following Office Action is responsive to the amendments and remarks received on January 23, 2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
At step 1, independent claim 1 recites a method, independent claim 8 recites a medium, and independent claim 15 recites a system, each of which is a statutory category of invention.
At step 2A prong I, independent claim 1 (exemplary) is directed to an abstract idea. Claim 1 recites the following limitations directed to the abstract idea:
obtaining, from a mobile device, based on electronic location data captured by radio or GPS triangulation, measured impression counts (MI-counts) over a period of time associated with a site displaying content that gives rise to impressions, wherein each of the MI-counts is stamped with a corresponding time, and wherein at least some of the electronic location data is inaccurate;
calculating a plurality of metrics for a profile corresponding to a sub-range of the period of time based on MI-counts within the profile;
detecting at least one type of multiple different types of data characteristics exhibited in the profile based on the plurality of metrics, wherein each of the multiple different types of data characteristics indicates a respective type of data failure;
if one of the multiple types is detected, setting a replacement label to a first label indicating that all the MI-counts in a subset of the profile are replaced with corresponding impression counts (I-counts) estimated based on the forecasting TS model;
if a different one of the multiple types is detected, setting the replacement label to a second label indicating that none of the MI-counts in the subset of the profile are replaced with corresponding I-counts estimated based on the forecasting TS model;
generating, based on the replacement label, corrected MI-counts for the profile based on the MI-counts in the profile and the forecasting TS model in accordance with a hybrid correction operation; and
providing the corrected MI-counts for determining a level of viewership of the content at the site based on the corrected MI-counts thereby mitigating impact on ability of correctly determining the level of viewership due to the inaccuracy of the at least some of the electronic location data.
These limitations recite the abstract idea of Certain Methods of Organizing Human Activity, specifically as it pertains to advertising or marketing behaviors. These limitations determine content impression counts which are used for purposes of determining advertisement prices (see specification, paragraph 1). Further, the limitations of calculating, detecting, setting, generating, and providing can also be considered a Mental Process, as they are tasks that a human could carry out.
At step 2A prong II, the additional elements are:
establishing a forecasting time series (TS) model via machine learning based on a sequence of at least some of the MI-counts arranged in accordance with the respective corresponding times associated therewith.
This limitation does not serve to integrate the abstract idea into a practical application because it recites the use of machine learning at a high level of generality, such that they amount to no more than mere instructions to apply the exception using a computer. Establishing a model is a process that can be done independent of a computer, so reciting “via machine learning” does not demonstrate any technical solution to a technical problem, or show how the use of technology is purporting to improve the functioning of the generically recited technology.
The claim recites a mobile device and electronic location data captured by radio or GPS triangulation, but those elements are not a part of the claimed invention. Rather, the invention is directed to obtaining data, which happens to come from those devices, but the collection of the data from those devices is not a part of the claimed invention. Therefore, they are not considered as additional elements. However, even if they were to be considered additional elements, they would amount to generic computing components recited at a high level of generality, amounting to mere instructions to apply the exception using a computer.
At step 2B, the additional element of machine learning, as discussed above, is a generic computing component recited at a high level of generality, and does not amount to significantly more than the judicial exception.
Independent claims 8 and 15 follow the same analysis as above. Independent claim 8 further recites a machine readable and non-transitory medium, and claim 15 further recites a processor, both of which are generic computing components recited at a high level of generality, and do not serve to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, as discussed above.
Dependent claims 2-7, 9-14, and 16-20 only further limit the abstract idea and do not contain any additional elements, such that they are rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed January 23, 2026 have been fully considered but they are not persuasive. Applicant argues that claims 1-20 are not directed to an abstract idea because the claims use technology to correct the MI-counts. However, as explained above, the obtaining of pre-measured data is not a technical concept, as obtaining is part of the abstract idea. The claim overall is directed to correcting the MI-counts data for purposes of improving the understanding of overall impressions, which is then used to drive advertising prices. Because the claims are focused on correcting the data after it is obtained, the claims are directed to an abstract idea. The correction itself is not technical in nature, as it merely uses a model and different characteristics found in the data to adjust the data values. Applicant’s argument that the claims are related to computing technology because data is obtained from a mobile device that gets data through GPS triangulation, and uses machine learning to develop a model, does not take into account the level of technology being claimed, which is at the apply it level. Applicant argues that the claims cannot be directed to a mental process because a human cannot capture GPS data or establish a model via machine learning, but this conflates the abstract idea with the additional elements. It is not contended that a human can carry out the functions of the additional elements; however, a human can obtain data from a device, and a human can create a mathematical model independent of a computer.
Applicant further argues that the claims are integrated into a practical application because they solve technical problems related to inaccuracy of electronic location data. However, the sections of the specification pointed out by the applicant do not discuss technical problems, but rather discuss problems within the abstract idea of obtaining correct impression data to determine advertising pricing. None of the quotes from the specification discuss anything other than correction. There is no mention of technology in the problem being solved, and therefore the solution as claimed and disclosed in the specification is not a technical solution to a technical problem, but instead a solution to a problem identified within the abstract idea. The contention that the claims provide an improvement to the technology of electronic location data captured by radio or GPS triangulation is incorrect, because there is no improvement to the technology of the data capture using technological components itself. Rather, the improvement is in the processing of data, which is abstract. Further, there is no claimed improvement to the machine learning, as the machine learning is claimed at such a high level as to only be “apply it” using generic computing elements. If the specification or claims pointed to improvements to the machine learning then it might be considered a practical application, but merely using generic machine learning is not an improvement to the technology.
As for step 2B, applicant argues that the claims when considered as an ordered combination and as a whole are significantly more than the abstract idea, but fails to provide any evidence for this position. Applicant points to the facts of the Berkheimer case, but does not show how this analysis would apply to the facts of the instant claims. Even when considered as an ordered combination, receiving (obtaining) data is considered well-understood, routine and conventional activity per MPEP 2106.05(d), and nothing in the claim suggests that the ordered combination of the technology itself is non-conventional, non-generic, or provides a technical improvement in the art. Rather, the reason the claim is distinct from the prior art lies solely within the abstract idea (see previous reasons for allowance), and the ordered combination of technological elements does not provide any improvement in the art that would make these claims analogous to Berkheimer or Bascom.
Therefore, the arguments are not persuasive and the rejection of claims 1-20 is maintained.
Conclusion
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 ILANA L SPAR whose telephone number is (571)270-7537. The examiner can normally be reached 8-4 M-F.
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/ILANA L SPAR/ Supervisory Patent Examiner, Art Unit 3622
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