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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 21, 2026 has been entered.
Response to Arguments
I. Claim Rejections under 35 U.S.C. § 101
Applicant’s remarks concerning the previous § 101 rejections have been fully considered but are not persuasive.
Applicant first argues that the human mind “cannot practically receive and process continuous, real-time streams of EEG, GSR, heart rate, and facial cue data from multiple users simultaneously to constantly recalculate dynamic weights and generate real-time group synchrony scores.” The Examiner respectfully disagrees. As an initial matter, this is not an accurate restatement of the limitations as currently claimed. For example, the only mention of “real-time” in the claims is in claim 6, where it is listed as one possible alternative option. As another example, the claims do not require that particular combination of biomarkers. Nevertheless, more importantly, the human mind is fully capable of reviewing multiple streams of data over time, and repeatedly performing analysis/calculation steps, as opposed to just a single snapshot as argued by Applicant. The Examiner acknowledges that a human mind alone of course cannot match the speed and complexity of data analysis possible by using a processor. However, there are numerous court decisions which clearly explain that an otherwise mental process cannot become eligible under § 101 merely by claiming that it is to be done faster than is possible by the human mind as a result of the ordinary capabilities of a computer. For example, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Similarly, the following example was found to be mere instructions to apply an exception because it does no more than merely invoke computers or machinery as a tool to perform an existing process: a process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). Lastly, as described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must “play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly”).
Applicant also argues that the recitation of the machine learning model encompasses AI functionality that cannot practically be performed in the human mind. The Examiner respectfully disagrees. Similar to the arguments above, merely invoking a generic “machine learning model” is simply an obvious mechanism for permitting a solution to be achieved more quickly than requiring a human mind(s) to learn the relationship and perform the prediction. The claims here merely recite a generic machine learning model to perform steps that a human mind could otherwise perform. In that way, as far as the § 101 analysis, it has the same significance as reciting a general-purpose processor.
Finally, Applicant argues that the claims here reflect an improvement to the functioning of a computer or another technology or technical field, due to allegedly using “a specific, novel technological architecture (e.g., a closed-loop, predictive bio-feedback system). The Examiner respectfully disagrees. The claims here do not provide any improvement, or even any change, to the underlying technology such as the processor, machine learning model, sensors, etc. For example, the claims here do not reflect any improvement to the processor itself, or to the machine learning model, such as improved processing speeds or less memory usage etc. Rather, the claims here take a process that could be done mentally, and simply use generic existing technology to implement it.
II. Claim Rejections under 35 U.S.C. § 103
Applicant’s remarks concerning the § 103 rejections have been fully considered but are not persuasive. Applicant argues that Stevens uses artificial neural networks (ANNs) exclusively for classifying a current state rather than generating a predictive forecast of a future outcome. The Examiner respectfully disagrees. The cited portions illustrate that the ANNs are used as part of the overall data analysis process including prediction, seen in e.g. the combination of Paras. 118-119 (cited in the rejection of claim 10 previously) and Para. 125 (cited in the rejection of claim 9 previously). Specifically, Para. 125 discusses how “NS (a transformed set of real-valued data) patterns as described in this application is that the sequence of their expression can be modeled into temporal trajectories using the NS pattern designations through a process termed symbolic analysis. There are many reasons for investigating temporal patterns of neurophysiologic synchronies: … this can then be used to predict whether a team is going into or out of synch.” In short, the prediction of future outcome (a team going into synch) relies on pattern analysis of more current data and is accomplished via a machine learning model. The analysis, including the prediction, of Stevens alone is based at least on the score and current activity, and is further in view of the learned relationship as a result of the modification in view of Talwai.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on April 29, 2022. It is noted, however, that applicant has not filed a certified copy of the CN202210472911.9 application as required by 37 CFR 1.55.
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 1-8 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Step 1: All of claims 1-8 and 11-20 are directed either to a method/process (claims 1-8 and 11-14) or to a system/machine (claims 15-20).
Step 2A, Prong One: The claims recite a mental process including steps such as “receiving biomarker data …” and “computing … a group synchrony score …” and “determining … an effect …” and “… generating … a recommended action …” “determining … an outcome …” and “generating … a prediction” (see e.g. claim 1 – all of the steps except for the last output step can be performed mentally) which could be performed by the human mind and/or by a human with a physical aid such as pen and paper. For example, a human such as a doctor, coach etc. could mentally receive biomarker data from multiple users in a group, and then mentally compute a group synchrony score and making determinations and recommendations in the manner claimed here.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application because the claims merely implement the mental process using generic processing technology and add insignificant extra-solution activity. Specifically: the step of “receiving biomarker data collected from one or more sensors …” is considered insignificant pre-solution activity of mere data gathering, since it merely collects the data necessary to carry out the mental process (note that in Prong One above, this step may also simply be considered part of the mental process, since the human mind can mentally receive data); the step of “providing the group synchrony score as an output in a user interface of a device” is considered insignificant post-solution activity since it merely outputs the result of the mental process using a generic output modality. Furthermore, merely carrying out mental steps using generic computing technology such as “at least one processor,” “computer-executable instructions stored in a memory,” “a machine learning model” and “at least one memory including computer program code” [see e.g. claim 15] etc. is well established to not amount to an integration into a practical application under the § 101 analysis. See, e.g., MPEP §§ 2106.04(a)(2)(III)(C) and 2106.04(d)(I) and 2106.05(f).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements recited in the claims are generic processing/computing components such as “at least one processor” and “at least one memory” etc., and generic data collection and output components such as “sensors” and “a user interface.” The Examiner previously took official notice that these are basic, generic components which are well-understood, routine and conventional in the medical diagnostic arts, and the claims here merely use them for their well-understood, routine and conventional functions. Applicant’s subsequent reply did not traverse the Examiner's assertion of official notice; therefore, the facts under official notice are now taken to be admitted prior art. See MPEP § 2144.03(C) (“If applicant does not traverse the examiner' s assertion of official notice or applicant' s traverse is not adequate, the examiner should clearly indicate in the next Office action that the common knowledge or well-known in the art statement is taken to be admitted prior art because applicant either failed to traverse the examiner' s assertion of official notice or that the traverse was inadequate.”). As such, those additional elements cannot be considered “significantly more” than the judicial exception in Step 2B of the § 101 analysis.
Dependent Claims 2, 4, 7-8, 16 and 19 merely add details to the mental step and clarify that the insignificant post-solution activity of outputting includes those additional details of the mental step.
Dependent Claims 3, 6, 17 and 20 merely add details to the mental step.
Dependent Claim 5 merely adds detail to the insignificant pre-solution data gathering activity (“recording the biomarker data …”) and repeats the insignificant post-solution activity (“replaying …”).
Dependent Claim 11 merely adds detail to the insignificant pre-solution data gathering activity and adds detail to the mental step to take into account the extra data gathered.
Dependent Claims 12-14 merely add detail to the insignificant pre-solution data gathering activity.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20110213211 A1 to Stevens et al. (hereinafter “Stevens”) in view of US 2013/0046151 A1 to Bsoul et al. (hereinafter “Bsoul”) in view of US 10,292,585 B1 to Talwai et al. (hereinafter “Talwai”).
Regarding Claims 1, 15 and 18, Stevens teaches a method comprising:
receiving, by at least one processor, multi-model biomarker data collected from one or more sensors for a group of at least two users, wherein the biomarker data includes a plurality of biomarker value streams combined with social cue data, the social cue data comprising at least one of a facial expression, a movement, contextual speech, or a verbal cue (see e.g. “monitoring neurophysiologic indicators of the members of a team while performing one or more collaborative tasks” in the abstract; see examples of sensor types in e.g. Paras. 43-45 and 59);
computing, by the at least one processor, a group synchrony score (see e.g. Paras. 46-48 discussing task and team metrics, and e.g. Para. 56 describing “neurophysiological synchronies”);
determining, by the at least one processor from the group synchrony score over a designated period of time, an effect of one or more actions performed by the group on the group synchrony score (see generally Paras. 46-48 and 56);
automatically generating, by a behavior engine comprising computer-executable instructions stored in a memory and executed by the at least one processor, a recommended action to be performed by the group to change the group synchrony score in response to the determined effect (see e.g. Paras. 84-86);
generating, via a machine learning model (see e.g. discussion of artificial neural networks in Paras. 118-119), a prediction of whether the group will achieve a target interaction state (see the discussion of prediction in Paras. 125, 144 and 153) based on a current group activity and the group synchrony score (see portions cited above)
providing the recommended action and the prediction as an output in a user interface of a device (see e.g. discussions of the user interface in Paras. 81-82, 104, 143, 151; see e.g. Paras. 84-86 discussing the providing of the recommended action; as noted above, see the discussion of prediction in Paras. 125, 144 and 153).
Concerning the generic computer details, see e.g. Paras. 74-76 which discuss a processor, memory, etc.
Stevens fails to teach (1) that “computing … a group synchrony score” is based on “weighting of the [data] according to a predetermined contextual relevance or importance,” and (2) “determining … an outcome of the recommended action by monitoring a subsequent change in the group synchrony score after the recommended action is performed to learn, via a machine learning model, a relationship between the recommended action and the outcome.”
Concerning (1) above, another reference, Bsoul, teaches the computation of a single index based on data fusion of multiple sensor types, and further teaches that each sensor may be separately weighted based on context information (see e.g. Paras. 57-60). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Stevens to allow each data type to be weighted based on context, as seen in Bsoul, because it would predictably and advantageously increase the accuracy of the resulting score since different data types could be more or less important in different contexts.
Concerning (2) above, another reference, Talwai, teaches a system for making recommendations to team members in which the system tests the effectiveness of its own recommendations to learn, via a machine learning model, the relationship between the recommendations and actual outcomes, thereby helping improve future recommendations (see e.g. Col. 2 lines 59-64: “One of many useful aspects to this measure-prompt change-remeasure system is how the mental state measurement system gets more effective over time. The mental state measurement system will keep track of what works for whom and when, and adapt to situations over time to prompt the most effective changes”, Col. 8 lines 33-42: “Embodiments provide a virtuous cycle, where the system measures frustration or fatigue of a user, recommends the user move to a place where members have a measured mental state with a better mental state metric, and again measure if the user's mental state metric improves after moving to the recommended place. This cycle tests the effectiveness of the system's recommendations (i.e. is a member's mental state better after the recommendations) and reinforces the effect (i.e. members learn where their mental state improves)”; also see claim 2; concerning the learning being via a machine learning model, see Col. 5 lines 33-35: “In one embodiment, the MSM system can use a support vector machine to perform classification of physiological data and to derive mental state data”). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to further modify Stevens to determine an outcome of the recommended action by monitoring a subsequent change in the group synchrony score after the recommended action is performed to learn, via a machine learning model, a relationship between the recommended action and the outcome, akin to that seen in Talwai, because doing so would predictably and advantageously help improve future recommendations based on actual effectiveness of prior recommendations. As a result of this modification, the prediction of Stevens would also be further based on that learned relationship since the relationship is known to affect the predicted outcome based on a given recommendation, in view of Talwai’s teachings.
Regarding Claim 2, 8, 16 and 19, see e.g. Paras. 47-48 discussing comparisons to “expert” data which is a reference group interaction. Also see “expected or acceptable levels” in Para. 47 and Para. 80: “the expert data can include expected team performance under certain conditions and/or interpretations of neurophysiologic synchronies expressed by a team during a monitored performance.”
Regarding Claim 3, 7, 11, 17 and 20, see e.g. Para. 99 discussing the normalization process.
Regarding Claim 4, see e.g. Paras. 84-86 discussing feedback and recommendations.
Regarding Claims 5-6, see e.g. “Assessments of team performance can be performed in real time and feedback can also be provided in real time” in the abstract; also see e.g. Paras. 49-50.
Regarding Claims 12-14, see examples of sensor types in e.g. Paras. 43-45 and 59.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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 JOHN R DOWNEY whose telephone number is (571)270-7247. The examiner can normally be reached Monday-Friday 8:30am-5:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, NIKETA PATEL can be reached at (571)-272-4156. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN R DOWNEY/Primary Examiner, Art Unit 3792
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