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 .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/16/2026 has been entered.
Response to Amendment
3. Applicant has amended the claims and argues that the claims do not recite a mental process (Remarks 6 filed 3/16/2026). Examiner concurs. The abstract idea of a mental process is withdrawn from the consideration under the 101 analysis. Moving forward, in light of new guidance following Ex Parte Desjardins, Examiner maintains the 101 rejection for claiming abstract mathematical concept.
4. Applicant also argues that the claim provides a “profound technical improvement to the field of non-contact physiological sensing by solving the physical problem of motion artifacts” (Remarks 6). Applicant argues that the amended language provides specific computational rules and alleges that the McRO standard has been met (Remarks 7). Examiner notes that the above improvement cannot be located in Applicant’s disclosure now necessary under Desjardins. (See Advance notice of change to the MPEP in light of Ex Parte Desjardins (December 5, 2025)). And while Examiner believes Applicant has made a bona fide attempt to cure the claims in view of McRO, yet the additional limitations lack any support in the specification of providing any technological improvement akin to the description of the improvement afforded by the rule sets in McRO. Thus, the rejection of the claims under 35 USC § 101 is maintained. In the rejection below, Examiner provides what is required in light of Desjardins for Applicant’s consideration.
5. Applicant has amended the claims are contends, and examiner concurs overcome the art of record. Thus, the rejection under 35 USC § 103 is withdrawn.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
8. Step 1
Claims 1-13 are directed to a method meeting the requirements for Step 1.
Claims 14-20 are directed to an apparatus/system meeting the requirements for Step 1.
9. Step 2A Prong 1
Independent Claim 1 (below) recites “automatically analyze the sensor data to determine one or more metrics including by applying a trained deep learning model.” Independent Claims 14 and 20 recite a similar limitation. This limitation recites a mathematical calculation, which is a mathematical concept, and, thus, an abstract idea.
Claim 1 is selected as representative of Claims 14 and 20:
A method, comprising:
receiving sensor data of a subject, wherein the sensor data includes eye tracking data and visible light image data; and thermal image data of an area associated with nostrils of the subject;
generating a three-dimensional mesh of a face of the subject based on the sensor data to dynamically locate one or more facial landmarks including the nostrils;
cropping the thermal image data based on the located one or more facial landmarks to spatially isolate the area associated with the nostrils;
using one or more processors to automatically analyze the sensor data to determine one or more metrics including by applying a trained deep learning model to the cropped thermal image data to predict a respiration rate of the subject; wherein predicting the respiration rate comprises detecting a frequency of cyclical temperature changes corresponding to ambient air entering the nostrils and body-temperature associated air exiting the nostrils; and
using the one or more metrics including the predicted respiration rate to determine an indicator associated with a likelihood the subject is being deceptive.
10. Step 2A Prong II
There is no discernable additional element (or combination of elements) recited in Claims 1, 14, or 20 that have integrated the judicial exception into a practical application. In making this determination Examiner relies on the two-step guidance from “Advance notice of change to the MPEP in light of Ex Parte Desjardins (December 5, 2025) wherein the claims were directed to a machine learning model on a series of tasks. First the specification must be evaluated to decide if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement “in the functioning of a computer, or an improvement to other technology or a technical field.” (Emphasis in original). Second, if the specification sets forth an improvement, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement i.e., the components or steps of the invention that provide the improvement described in the specification.
In Desjardins, the specification explained how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Moreover, the specification disclosed enumerated improvements, namely: the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. “Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation” according to the guidance.
Here, examination of the specification indicates no such improvements in the functioning of a computer, or of an improvement to other technology or a technical field. According to Applicant in his specification the machine learning models are used as originally designed and operate of various types on data. For example, Applicant’s specification makes several references to the use of deep learning models that “can be used to identify different behavioral cues and/or predict the likelihood of deception associated with a detected behavioral cue.” (Spec. [0024 and similarly in “applying deep learning and/or computer vision techniques” [0031], “deep learning models used for predicting deception analysis are trained” and “a deep learning model can be trained for predicting respiration rate using thermal sensor data” [0044], and applying deep learning techniques…one of more metrics can be determined to identify relevant behavioral cues [0053], and “thermal sensor data prepared at 1003 is used as input to a machine learning model to predict respiration rate” [0092].
But these examples are not improvements to the learning models themselves on par with the articulated improvements of Desjardins, merely a description of various uses to identify different behavioral cues and/or predict the likelihood of deception (Spec. 0024]); respiration rates (Spec. [0031]); and facial expressions (Spec. [0044]). In contrast to Desjardins, the Federal Circuit held machine learning claims ineligible in Recentive Analytics, Inc. v. Fox Corp., No. 23-2437 (Fed. Cir. 2025).
In Recentive, the claims were directed towards providing parameters to a machine learning model to train the model to identify relationships between different event parameters and target features using historical data corresponding to one or more previous series of live events, and changing conditions, and, using machine learning, dynamically generate optimized maps and schedules. However, the Court found that the technology described in the specification and recited in the claims was conventional. Neither the specification nor the claims described and recited, respectively, how any such improvements in AI was accomplished i.e., by an articulated specific technological improvement to the underlying machine learning method not just claimed uses in new environments. Consequently, the Court held the claims failed to provide “significantly more” than the abstract idea of generating event schedules and network maps through the application of machine learning.
Second, if the specification sets forth an improvement, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement i.e., the components or steps of the invention that provide the improvement described in the specification. In this case, there is no disclosed improvement to reflect in the claims.
In weighing these considerations, Examiner deems that Applicant’s disclosure and claims, unlike those of Desjardins, are similar to those of Recentive because the specification describes various collections of data used to train machine models but fails to disclose any improvements to the models themselves e.g., an improvement to the algorithm; reduction in complexity; or a savings via efficient use of storage space as in Dejardins.
Examiner deems the balance of the claim limitations do not provide a practical application. Applicant's specification does not disclose new data sensor technology; new ways to communicate data; new metrics; new sources; or new indicators, but discloses only known technologies used in their customary ways. Here, the processor, memory, and instructions are recited so generically (no details whatsoever are provided other than in name only) that they represent no more than mere instructions to apply the judicial exception on a computer. Examiner finds in the specification, the computing environment to comprise a general purpose digital processor (Spec. 0119]) and “[a]s is well-known in the art, primary storage can be used as a general storage area: (Spec. [0120]). “Also as is well known in the art, primary storage typically includes basic operating instructions, program code, data and objects used by the processor 1702 to perform its functions (e.g., programmed instructions).” (Spec. [0120]) where “the computer-readable medium is any data storage device that can store data which can thereafter be read by a computer system.” (Spec. 0125]). Sensors comprise well-known eye tracking sensor 301, RGB camera sensor 311, thermal sensor 321, microphone 331, display 341, and audio output 351.” (Spec. [0039]). Where data is collected on a human subject, “the only physical contact that may be required is the use of a conventional input device such as a mouse, touchpad, and/or touchscreen.” (Spec. 0040]). Lastly, the claim recites metrics and indicators where “In some embodiments, one or more of the metrics are provided at least partially by the sensor equipment and/or by applying deep learning and/or computer vision techniques. For example, visible image data captured using an RGB camera can be fed into a trained deep learning model to predict the subject's heart rate. Similarly, cropped thermal image data of the area surrounding a subject's nostrils can be fed into a trained deep learning model to predict the subject's respiration rate. In some embodiments, computer vision techniques are applied, for example, as another technique for predicting the subject's respiration rate using captured thermal images.” (Spec. [0031]) where the metrics are of routine pause time, blink rate, pupil features, gaze fixation, gaze target, respiration rate (Spec. [0028]). “Indicators can display in real time the subject's response pause time, respiration rate, blink rate, pupil features, and/or gaze fixation duration, among other metrics. The determined metrics can be associated with responses and/or questions presented to the subject during an interview.” (Spec. [0025]). Examiner deems the sensors, sensor data, metrics, and indicators are the result of the use of pre-existing means of data collection subjected to established analytical techniques used in their conventional ways, and as such, comprise extra-solution data gathering, processing, and storage activity. Thus, combined with the computing elements provide a generic computing environment to carry out the abstract idea.
Thus, Claim 1, and similarly Claim 14 and 20, lack the eligibility requirements of Step 2 Prong II.
11. Step 2B
According to the advanced guidance to be reflected in MPEP 2106, in addition to the considerations discussed in Step 2A, an additional consideration indicative of an inventive concept (aka “significantly more”) is the addition of a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP 2106.05(d)). Conversely, an additional consideration not indicative of an inventive concept is simply appending well-understood, conventional activities previously known to the industry, specified at a high level of generality, to the abstract idea (MPEP 2106.05(d) and Berkheimer Memo, April 20, 2018). Thus, the additional elements evaluated under Step 2A are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
Claim 1, and similarly for the processor and memory and medium of Claims 14 and 20, do not recite additional elements, individually or in combination, that amount to significantly more than the abstract idea. As discussed above with respect to the lack of a practical application, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer components. The same analysis applies here, i.e., mere instructions to apply an exception using generic computer component(s) cannot provide an inventive concept in Step 2B.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Examiner has identified the sensor data including eye tracking data and visible light image data, thermal data, and of one or more metrics as extra-solution data collection and data processing. This has been deemed to be well-known, routine, and conventional under Electric Power Group and in the MPEP (See receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i), performing repetitive calculations (MPEP 2106.05(d)(II)(ii), and electronic recordkeeping (MPEP 2106.05(d)(II)(iii)). In Electric Power Group, looking at the claim as a whole, the court held the claim to be directed to abstract collecting and analyzing data even though the type of data collected was limited to data from an electric power grid. The claim was not patent-eligible, because it also failed to include an inventive concept. The court noted that the claims did not relate to a new source or type of information, or a new algorithm for analyzing the information. Likewise, here, while the data may pertain to the field of deception detecting, it utilized known sources, sensing data collection, and outcomes to provide an inventive concept. Thus, Claim 1 is ineligible. Independent Claims 14 and 20 inherit the same abstract idea as Claim 1 and are similarly ineligible.
12. Dependent Claims
Claims 2-5, 9-10, and 15-17 further recite additional extra-solution metrics or indicators. Claims 6, 8, 11, 12, and 18 further recite more abstract analysis. Claims 7 and 19 further recite additional extra-solution sensor data collection. Claim 13 recites extra-solution storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is in the Notice of References Cited.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Paul A. D’Agostino whose telephone number is (571) 270-1992.
15. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
16. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-270-2992.
/PAUL A D'AGOSTINO/Primary Examiner, Art Unit 3715
Prosecution Insights