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 .
Applicant’s amendments dated 7/7/26 are hereby entered.
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, 4, 6-10, and 13-24 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, 3, 4, 6-10, and 13-24 are directed to an abstract idea without significantly more. The claims recite a mental process that can be performed by human being and/or as a method of organizing human activity and/or claim training/employing a machine learning model in a particular technological environment.
In regard to Claims 1 and 20, the following limitations can be performed as a mental process by a human being in terms of claiming collecting data, analyzing that data, and providing outputs based on that analysis which has been held by the CAFC to be an abstract idea in decisions such as, e.g., Electric Power Group, University of Florida Research Foundation, and Yousician v Ubisoft (non-precedential); and/or recite a method of organizing human activity in terms of claiming the teaching/training/evaluation of a human subject’s which has been identified by MPEP 2106.04(a)(2)(II) as being a method of organizing human activity, in terms of the Applicant claiming:
[a] method, comprising:
Initiating […] an interaction […] between […] one or more [users] wherein the interaction […] includes at least one non-player character, and at least one avatar corresponding to one or more users […];
receiving […] during the […] interaction […] one or more data streams of biometric sensor data [concerning] a neuro-physiological response of a first user […];
calibrating […] the biometric sensor data…using an adaptive [algorithm] configured to dynamically adjust one or more sensor-specific calibration parameters […] based on one or more detected involuntary physiological responses of the first user;;
receiving […] an event trigger…during the play of the interaction […], wherein the biometric sensor data…exceeding a predetermined threshold;
in response to the event trigger, calculating […], a neuro-physiological state of the first user by:
[providing a] stimulus that includes a non-arousing stimulus and a known arousing stimulus […]
[receiving data regarding] measure[ments of] an involuntary response of the first user while interacting with the […] stimulus;
determining […] at least an expectation baseline arousal value based on the calibrated sensor data and the involuntary response to normalize an individual physiological variability of the first user relative […];
calculating […] a weighting value based on one or more source identities for the calibrated sensor data;
calculating […] a set of measures concurrently for the first user based on different combinations of the calibrated sensor data, the expectation baselines, and the weighting value, wherein the set of measures includes one or more of an arousal measure, a valence measure, and a confidence measure;
receiving […] context-indicating data of the first user […] wherein…noise level;
calibrating […] at least a pupil dilation measurement in the biometric sensor data based on the ambient light level to discriminate a lighting change pupil dilation caused by one or more lighting changes from an emotional pupil dilation caused by an emotional arousal;
correlating […] via a trained […] algorithm, the set of measures with the biometric data and the context-indicating data of the first user to generate a […] control signal representative of the neurophysiological state;
and
based on the correlated set of measures and the calibrated pupil dilation measurement, determining […] the neuro-physiological state of the first user, and a [visual] representation of the neuro-physiological state of the first user; and
displaying […] the […] representation of the neuro-physiological state […];
calculating […] an error value based on a difference between the neuro-physiological state and a predicted neuro-physiological state for a current state of the interaction application;
modifying […] an appearance and a behavior of the at least one avatar by controlling one or more rendering parameters of the [visual display] based on the […] control signal;
applying [an] algorithm to a parameter of the interaction […] to optimize a parameter based on the neuro-physiological state; and
modifying […] one or more characteristics of a non-player character of the interaction […] based on the optimized parameter to dynamically adjust an interaction pacing and a challenge intensity rendered by the [visual display];
transmitting [display] instructions based on the modified one or more characteristics;
and
outputting […] the modified interaction […] to a display […].
In regard to Claims 1 and 20, the following limitations recite training/employing a machine learning algorithm in a particular technological environment, which has been held to be an abstract idea by the CAFC in, e.g., Recentive Analytics, in terms of the Applicant claiming:
calibrating […] biometric sensor data for each of the one or more data streams using an adaptive machine learning algorithm…first user;
correlating […] via a trained machine-learning algorithm, the set of measures with the biometric data and the context-indicating data of the first user to generate a […] control signal…state;
applying […] a machine learning algorithm to a parameter of the interaction […] to optimized the parameter based on the neuro-physiological state.
In regard to the dependent claims, they also claim an abstract idea to the extent that they merely claim further limitations that likewise could be performed as a mental process by a human being and/or as a method of organizing human activity and/or claimed training/employing a machine learning model in a particular technological environment.
Furthermore, this judicial exception is not integrated into a practical application because to the extent that additional elements are claimed either alone or in combination such as, e.g., a processor, at least one sensor, one or more devices, a communication component, a communication network, employing audio/video, employing VR devices, embodying Applicant’s abstract idea as computer software that processes data concurrently/in parallel and/or digitally and/or in real-time, a computer memory, and/or computer applications, these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering), to embody the abstract idea on a general purpose computer, and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, the claims do not include additional elements that taken individually, and also taken as an ordered combination, are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g., a processor, at least one sensor, one or more devices, a communication component, a communication network, employing audio/video, employing VR devices, embodying Applicant’s abstract idea as computer software that processes data concurrently/in parallel and/or digitally and/or in real-time, a computer memory, and/or employing real-time and/or offline computer applications, these are generic, well-known, and conventional computer elements and are claimed for the generic, well-known, and conventional functions of collecting and processing data and/or providing an analysis based on that processing. As evidence that these additional elements are generic, well-known, and conventional, Applicant’s specification teaches the support for these elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). See generally, e.g., Figures 1-4 in Applicant’s PGPUB and text regarding same; see, e.g., p150 in regard to processing data “concurrently”; see, e.g., p50 in regard to processing data in “real-time”; and, e.g., p42 in regard to controlling real-time and/or off-line applications.
Response to Arguments
All of Applicant’s claims have been rejected under 35 USC 101 under the Mayo test as claiming abstract ideas and without claiming “significantly more”. The Mayo test is a legal test and, as such, the question in regard to making such a rejection is what is the most analogous case law in terms of identifying whether an applicant has claimed patent eligible versus ineligible subject matter. To that end, the 101 rejections made supra cite legal authorities in regard to why the Applicant is alleged to have claimed patent ineligible subject matter under the Mayo test. Applicant argues that it has claimed a “practical application” and thereby claimed patent eligible subject matter under the Mayo test. Applicant’s argument is not persuasive. The Mayo test is a legal test and “practical application” is not part of the Mayo test but is, instead, a burden placed on examiners by the Office when they are making a 101 rejection employing the Mayo test. In regard to “practical application”, the MPEP provides examples of Supreme Court and CAFC decisions where a claimed invention has been held to be directed to patent eligible subject matter. See MPEP 2106.05(d)(I). Simply invoking “practical application” but without citing specific legal authority in support of Applicant’s argument, such as from these examples, that it has claimed patent eligible subject matter under the two-part Mayo test, therefore, does not provide a proper basis or rationale as to why the 101 rejection being made is allegedly deficient. Applicant’s sole citation to legal authority in regard to its arguments regarding “practical application” is to the ARP decision in Desjardins. Desjardins, however, concerned a method of training a machine learning model on multiple tasks so that once the model has been trained, the model can be used for each of the multiple tasks with an acceptable level of performance and, as a result, systems that need to be able to achieve acceptable performance on multiple tasks can do so while using less of their storage capacity and having reduced system complexity. Applicant’s claimed invention does not concern anything analogous to the subject matter of Desjardins. Instead, Applicant’s claimed invention concerns collecting sensor data, analyzing that data, and then providing some dynamic visual output based on that analysis, and is much more closely analogous to the similar subject matter held by the CAFC to be patent ineligible in, e.g., Yousician (non-precedential). To the extent that Applicant now additionally claims collecting data regarding pupil dilation measurements, analyzing that data to calculate an error value, and then providing a visual output based, in part on that error value, that is likewise abstract for similar reasons as just stated.
Applicant argues that it has claimed “significantly more” because of its claimed “ordered combination” of limitations. Applicant cites no legal authority in support of this argument. And, to the contrary, to the extent that Applicant cites, e.g., computing devices, sensors, and a display device in addition to its claimed abstract idea the ordered combination of such limitations has been repeatedly held by the CAFC to not render “significantly more”. See, e.g., Electric Power Group and University of Florida Research Foundation.
Applicant also argues that the Final failed to provide a Berkheimer finding in regard to Applicant’s claimed “ordered combination” of Applicant’s abstract idea and the elements that Applicant claims in addition to that abstract idea. There is no such requirement, however, that such a finding be made in regard to making a prima facie rejection under the Mayo test. To the contrary, the Berkheimer finding need only be made in regard to the elements claimed in addition to the abstract idea, or the combination thereof. See MPEP 2106.05(d): “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)”, original emphasis omitted).
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 extension fee 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 Mike Grant whose telephone number is 571-270-1545. The Examiner can normally be reached on Monday through Friday between 8:00 a.m. and 5:00 p.m., except on the first Friday of each bi-week.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's Supervisory Primary Examiner, Peter Vasat can be reached at 571-270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL C GRANT/Primary Examiner, Art Unit 3715
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