DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 1/7/2026, Applicant, on 5/7/2026, amended Claims 1, 8, and 15. Claims 1-20 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant states the Desjardins Decision and that the amended limitations are eligible as the Claims recite a technical improvement in the audience measurement field, and thus is eligible. Examiner disagrees as the claims are not directed to an improvement in any additional element, combination, a technology, or technological field, but rather recites claims directed at an abstraction, that of determining audience size and measurement of such. The use of a machine learning algorithm does not make the claim eligible, and this is utilization of current technologies such as a machine learning algorithm to perform the abstract limitations of the Claims. Further, this has nothing to do with Desjardins other than a generic link to a machine learning algorithm which is purported to improve computer functionality. The claims as a whole do not improve any claimed addition element, such as by generally linking the claim to an updated machine learning algorithm, computer system, medium, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they merely are receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies, such as artificial intelligence, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Arguments regarding 35 USC § 103 – The rejection is hereby removed for the reasons found in the “Allowable Subject Matter” section found below
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.
Alice - Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15 recite limitations for obtaining (i) first return path data associated with a plurality of media devices of panelist households and (ii) panel meter data associated with the plurality of media devices and obtained from meters of the panelist households (Collecting Information, an Observation, a Mental Process; a Fundamental Economic Process, i.e. marketing to specific groups; a Certain Method of Organizing Human Activity),classifying view segments of the first return path data based on whether the first return path data for respective ones of the view segments matches the panel meter data (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. marketing to specific groups; a Certain Method of Organizing Human Activity), based on a first set of features generated from the classified view segments, training a machine learning algorithm to output one or more on/off determinations for media devices wherein training the machine learning algorithm to output the one or more on/off determinations for the media devices comprises: modeling the machine learning algorithm with features generated from the classified view segments based on 1) the view segments matching the panel meter data being included in the modeling and 2) other view segments that do not match the panel meter data being removed from the modeling; and based on the modeling of the machine learning algorithm, selecting the first set of features generated from the classified view segments (Analyzing and Transmitting the Information, an Evaluation and Judgment, a Mental Process; a Fundamental Economic Process, i.e. marketing to specific groups; a Certain Method of Organizing Human Activity), obtaining second return path data associated with a media device of a non-panelist household, different from the panelist households (Collecting Information, an Observation, a Mental Process; a Fundamental Economic Process, i.e. marketing to specific groups; a Certain Method of Organizing Human Activity), and applying the second return path data to the machine learning algorithm trained based on the first set of features to output an on/off determination for the media device (Analyzing and Transmitting the Information, an Evaluation and Judgment, a Mental Process; a Fundamental Economic Process, i.e. marketing to specific groups; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of a Fundamental Economic Process, i.e. marketing to specific groups, but for the recitation of generic computer components. That is, other than reciting an audience measurement computing system, processor, media devices, and medium, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of a Fundamental Economic Process. For example, classifying view segments of the first return path data based on whether the first return path data for respective ones of the view segments matches the panel meter data encompasses a supervisor or data analyst tracking who is watching different shows and putting together groups which are similar, which is an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a Fundamental Economic Process, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The system, media devices, processor, and medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmitting steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0053]FIG. 7 is a block diagram of an example processor platform structured to execute the example computer readable instructions of FIGS. 3-4 to implement the example media device on/off detector 124 of FIGS. 1-2. The processor platform 700 can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPadTM), a personal digital assistant (PDA), an Internet appliance, or any other type of computing device.”
Which shows that any generic computer can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, processor, medium, etc., nor the receiving or transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-7, 9-14, and 16-20 contain the identified abstract ideas, further narrowing them, with the additional elements of an audience measurement entity, database, and televisions which are all highly generalized as per Applicant’s Specification when considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1-20 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejections.
The closest prior art of record are Sullivan (U.S. Publication No. 2017/006,4358), Time (NPL - Its-time-get-return-path-data-together – JUL 2016), and Reismann (U.S. Publication No. 2015/014,3395). Sullivan, a method and apparatus to estimate demographics of a household, teaches obtaining (i) first return path data associated with a plurality of media devices of panelist households and (ii) panel meter data associated with the plurality of media devices and obtained from meters of the panelist households, classifying view segments of the first return path data based on whether the first return path data for respective ones of the view segments matches the panel meter data, based on a first set of features generated from the classified view segments, training a machine learning algorithm to output estimated data, obtaining second return path data associated with a media device of a non-panelist household, different from the panelist households, applying the second return path data to the machine learning algorithm trained based on the first set of features to output estimated data such as tuning events where data associated with non-panelist households are used with the features and trainers for the machine learning algorithm, a machine learning algorithm which is trained with features from both first path (panelist) and second path (non-panelist) data, as well as tuning events such as turning the television or set-top box on or off, wherein the non- panelist households are households that do not include meters associated with an audience measurement entity, a database configured to store return path data and panel meter data, wherein obtaining the first return path data and the panel meter data comprises accessing the first return path data and the panel meter data from the database, wherein the first and second on/off determination for the media device associated with the second return path data indicate whether, for each of a plurality of viewing segments of the second return path data, the media device was in an on state or in an off state, wherein the plurality of media devices are televisions, wherein the first return path data is reported by set-top boxes connected to the plurality of media devices, wherein the plurality of media devices are media devices of panelist households, wherein the panel meter data is obtained from meters of the panelist households, but it does not explicitly call this return path data nor does it teach to output on/off determinations for media devices. Time, NPL for using return path data, teaches return path data being used to out determinations of on and off televisions as on pg. 2 and 3 where the system predicts whether a TV is on or off even if set-box is turned on or off, but neither explicitly states use of a machine learning algorithm with feature generated from a classified view segments and modeling based on the selecting of the first set of features. Reisman, a method and apparatus to estimate demographics of a household, teaches estimation of household data from panelist households, collecting information using a plethora of devices, using features or predictors and a machine learning algorithm to determine use of devices in a household, but does not teach , and is used to identify the condition and the tasks required, by using a support vector machine, decision tree, random forest, and other types of machine learning, but does not explicitly state use of a machine learning algorithm with feature generated from a classified view segments and modeling based on the selecting of the first set of features as claimed. None of the prior art explicitly teaches this use of a machine learning algorithm with feature generated from a classified view segments and modeling based on the selecting of the first set of features, along with the other limitations of the claims, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1, 8, and 15, and their dependents, are allowable over the prior art of record, and are objected to as provided above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 8/2/2026
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