DETAILED ACTION
Status of the Claims
This office action is submitted in response to the amendment filed on 3/2/26.
Examiner notes Applicant’s priority date of 9/13/23, which stems from provisional applications 63691171 and 63582359.
Examiner further notes the previous withdrawal of prior art on 12/3/25.
Claims 4 and 7-9 have been cancelled.
Claims 1-3, 5-6, 10-11, and 20 have been amended.
Claims 21-22 are new.
Therefore, claims 1-3, 5-6, and 10-20 are currently pending and have been examined.
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 .
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 1–3, 5–6, 10–22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 21, and 22 are each directed to an apparatus comprising an interface system and a first local control system. As such, the claims are directed to a machine, which is a recognized statutory category under 35 U.S.C. § 101. Accordingly, the claims satisfy Step 1 of the subject matter eligibility framework. MPEP § 2106.03.
Independent claims 1, 21, and 22, in part, describe an invention comprising: generating user engagement data corresponding to one or more people in a first preview environment, the user engagement data indicating estimated engagement with presented content of a content stream; determining, based on user preference data, whether to provide user engagement data, sensor data, or both, for further processing; and determining, responsive to user preference data, to withhold at least some user engagement data, sensor data, or both, from further processing. As such, the invention is directed to the abstract idea of collecting and analyzing audience engagement with content and applying user preferences to determine how that engagement data is used, which, pursuant to MPEP 2106.04(a), is aptly categorized as a method of organizing human activity (i.e., managing and controlling the collection and use of audience engagement information in a content preview environment). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional elements that are associated with the judicial exception, including: receiving sensor data from one or more sensors in the first preview environment via the interface system (claims 1, 21, and 22); outputting user engagement data, sensor data, or both, to a data aggregation device (claims 1, 21, and 22); receiving updated federated ML model data from the federated ML model (claims 1 and 22); providing updated local ML model data to the federated ML model (claim 21), and determining a sum of the ML model data (claim 22). Examiner understands these limitations to be insignificant extra-solution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191–192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle into a patentable process.").
The aforementioned claims also recite additional elements including an "interface system" for transmitting and receiving data, "one or more sensors" for gathering data in the first preview environment, a "data aggregation device" for collecting and storing data, and "one or more remote devices or servers" for implementing the federated ML model (claims 1, 21, and 22). These limitations are recited at a high level of generality, and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
The aforementioned claims also recite a process for training a first local ML model at least in part on user engagement data or sensor data from the first preview environment; determining, based on user preference data, whether to provide that data to the first local ML model for training; receiving updated federated ML model data and updating the first local ML model according to that data (claims 1 and 22); providing updated local ML model data to the federated ML model for training (claim 21); and updating the first local ML model based on a weighted sum of current local ML model data and updated federated ML model data (claim 22). These limitations generally recite the use of machine learning models to process audience engagement data and propagate model updates between local and federated models in furtherance of the abstract idea. It also amounts to mere instructions to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment (e.g., generic computer components and sensors connected via a network in a content preview environment).
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions are merely facilitated by generic computer implementation.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Receiving, transmitting, or outputting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Providing data as input to a machine learning model for training purposes. Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984; OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092–93.
Calculating a weighted sum of data. Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984; Mayo, 566 U.S. at 79, 101 USPQ2d at 1968.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 U.S.C. § 101.
Dependent claims 2, 3, 5, 6, 10–20 are rejected for the same reasons as their respective base claims. Claim 2 adds that the local ML model is trained on first user engagement data or sensor data if provided by the first local control system; claim 3 adds that the first local control system implements the first local ML model; claim 5 adds that the federated ML model is implemented by remote devices not in the first preview environment; claim 6 adds that the federated ML model is implemented by one or more servers; claim 10 adds that the updated federated ML model data corresponds to a demographic group of at least one person in the first preview environment; claim 11 adds that the federated ML model is trained on updated local ML model data from each of a plurality of local ML models; claim 12 adds that each of the plurality of local ML models corresponds to one preview environment of a plurality of preview environments; claim 13 adds that the first local control system determines when to provide updated local ML model data from the first local ML model; claim 14 adds that updated local ML model data is provided after the first local ML model has processed data from a complete session of content consumption; claim 15 adds that updated local ML model data is provided after the first local ML model has updated user engagement data according to one or more user responses to user prompts; claim 16 adds that selected sensor data comprising some but not all types of sensor data is provided to the first local ML model; claim 17 adds that the selected sensor data corresponds to user preference data; claim 18 adds that first user engagement data is generated according to a set of detectable engagement types corresponding to user preference data; claim 19 adds that the set of detectable engagement types corresponds to detectable engagement data provided with or indicated by metadata received with the content stream; and claim 20 adds that first detectable engagement data corresponding to a first portion of the content stream differs from second detectable engagement data corresponding to a second portion. None of these additional limitations transform the abstract idea into patent-eligible subject matter.
Therefore, claims 1–3, 5–6, 10–22 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Relevant Prior Art
Though not cited in the aforementioned rejections, the following references are nevertheless deemed to be relevant to Applicant’s disclosures:
Ken-Dror (11068926), directed to a system and method for analyzing and predicting emotion reaction.
Anthony (20190228439), directed to dynamic content generation based on response data.
Ram et al. (12242980), directed to machine learning with multiple constraints.
Marathe et al. (20230394374), directed to a hierarchical gradient averaging for enforcing subject level privacy.
Laskaridis (12450489), directed to a method for federated learning.
Response to Arguments
The previous objection has been withdrawn in response to Applicant’s amendments.
Applicant’s arguments regarding the sufficiency of the claims under 35 USC 101, however, remain unpersuasive.
Applicant argues that the recitation of a federated ML model architecture — including a local ML model updated via federated model data, a privacy-based training control mechanism, partial training control (claim 21), and a weighted sum update technique (claim 22) — defines a concrete model-lifecycle framework that improves the operation of machine learning models and therefore integrates any alleged judicial exception into a practical application under Step 2A, Prong Two, or alternatively amounts to significantly more under Step 2B. Examiner respectfully disagrees.
As an initial matter, the examiner has not oversimplified the claims at a high level of generality. The abstract idea as identified — collecting and analyzing audience engagement with content and applying user preferences to determine how that engagement data is used — is drawn directly from the claim limitations themselves and accurately characterizes the core concept of the invention. The examiner's identification of the abstract idea does not ignore the ML model limitations; rather, as explained in the rejection, the ML model limitations are correctly treated as additional elements under Step 2A, Prong Two, and Step 2B, not as part of the abstract idea. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2355–57, 110 USPQ2d 1976, 1981–82 (2014).
Applicant's argument that the federated ML architecture improves the operation of machine learning models is not persuasive because the claims do not recite any specific improvement to the ML models themselves or to the technology underlying them. The claims recite the use of a local ML model and a federated ML model at a high level of generality — the claims do not disclose a new training algorithm, a novel model architecture, a specific technical mechanism for aggregating federated updates, or any other technical advance within the ML models themselves. Rather, the claims merely deploy known ML models as tools to receive, process, and propagate data in furtherance of the abstract idea. The Federal Circuit has consistently held that applying a judicial exception using a computer or known technology, without more, does not integrate the exception into a practical application. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983; MPEP § 2106.05(f). The mere recitation that a local ML model receives updated federated model data and is updated accordingly does not describe how the federated update improves the ML model itself — it describes only the data flow between generic components.
Applicant's argument that the privacy-based training control mechanism (i.e., the determination not to provide user engagement data or sensor data to the local ML model responsive to user preference data) represents a specific technical improvement to local model update behavior is similarly unpersuasive. This limitation describes a data routing decision based on user preferences, which is precisely the kind of organizing human activity and mental process step that the examiner identified as the abstract idea. The fact that this routing decision affects what data a ML model is trained on does not transform it into a technical improvement to the ML model or to computer technology generally. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (the "focus of the claims" on "collecting information, analyzing it, and displaying certain results of the collection and analysis" is directed to an abstract idea regardless of the technological environment in which it is implemented).
Applicant's argument regarding claim 21's partial training control and claim 22's weighted sum update technique is likewise unpersuasive. As explained in the rejection, both of these limitations constitute insignificant extra-solution activity — they represent data routing and mathematical calculation steps that are incidental to the abstract idea and do not reflect any improvement to computer or ML model technology. Calculating a weighted sum is a fundamental mathematical operation, and controlling what portion of data is fed into a model is a routine data management decision. Neither limitation recites a specific technical mechanism that achieves an improvement in ML model performance, training efficiency, or any other technical metric. Alice Corp., 134 S. Ct. at 2358–59, 110 USPQ2d at 1983–84; Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79, 101 USPQ2d 1961, 1968 (2012).
Finally, Applicant's reliance on Ex parte Desjardins and the requirement to evaluate claims "as a whole" and "as an ordered combination" is noted, but does not alter the analysis. The examiner has evaluated the claims as an ordered combination, as required under MPEP § 2106.05(d). As noted above, the ordered combination of an interface system, sensors, a data aggregation device, a local ML model, and a federated ML model — each performing its ordinary function — does not produce a technical improvement beyond the abstract idea itself. The combination adds nothing that is not already present when the elements are considered individually. Applicant's assertion that the absence of prior art rejections reinforces a finding of significantly more is also unpersuasive, as patentability under § 102/§ 103 and eligibility under § 101 are independent inquiries. See Ass'n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591, 106 USPQ2d 1972, 1979 (2013).
Therefore, for at least these reasons, the rejection under 35 USC 101 is sustained.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BUSCH whose telephone number is (571)270-7953. The examiner can normally be reached M-F 10-7.
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/CHRISTOPHER C BUSCH/Examiner, Art Unit 3621
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621