DETAILED ACTION
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) submitted March 12, 2026. This application claims the priority benefit of Taiwan application serial no. 112150251, filed on December 22, 2023. Claims 1, 3, 9, and 11 are amended. Claims 7 and 15 were previously cancelled. Claims 1 – 6 and 8 – 14 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 12, 2026 has been 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 – 6 and 8 – 14 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.
Step One
Claims 1 – 6 and 8 – 14 are drawn to a system and method, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1 and 9 recite determining the emotional status of a user according to the user’s facial image, analyzing the image when it is determined the emotional status meets a preset emotional status, and determining whether to issue an alert based on the result of the analysis.
The respective dependent claims 2 – 6, 8, and 10 – 14, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that present invention “relates to an electronic system and a physiological monitoring method, and in particular relates to an electronic system and a physiological monitoring method configured to monitor a health status ” (paragraph 2 of the published specification). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a need “perform real-time monitoring of the physiological status of the user to issue an alert when the physiological status of the user is critical, and effectively issue alerts for the critical status of the user caused by fatigue, excessive use of electronic devices or stress so that the user may take emergency measures accordingly” (paragraph 30 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “electronic system”, “camera”, “auxiliary feature sensor”, “accelerator”, “processor”, “executing an artificial intelligence model, wherein the processor of the accelerator is further configured to classify the facial image into preset emotional statuses”, “wherein the artificial intelligence model is trained by a data set including a plurality of facial images labeled with the preset emotional statuses “, “network”, “first alert device to issue a first alert signal”, “a network setting of the electronic system is turned on”, “second alert signal”
Claim 2: “electronic system”, “accelerator”
Claim 3: “electronic system”, “accelerator”, “image recognition technology”, “artificial intelligence model”
Claims 4, 6: “electronic system”, “auxiliary feature”, “accelerator”
Claim 5: “electronic system”, “auxiliary feature”
Claim 8: “electronic system”, “the accelerator is disposed on a motherboard or a connector, or is disposed on an edge device connected through a connection terminal or a transmission line”
Claim 9: “electronic system”, “processor” “taking a facial image of a user”, “sensing an auxiliary feature”, “executing an artificial intelligence model, classifying the facial image into preset emotional statuses”, “wherein the artificial intelligence model is trained by a data set including a plurality of facial images labeled with the preset emotional statuses “ “network”, “first alert device to issue a first alert signal”, “a network setting of the electronic system is turned on”, “second alert signal”
Claim 10: “image recognition technology”
Claim 11: “image recognition technology”, “artificial intelligence model”
Claims 12 – 14: “auxiliary feature”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, 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.
Hence, the 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. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0022] In some embodiments, the disposition location of the accelerator 12 may be, for example, integrated on the motherboard, or separately disposed on a keyboard, mouse, connector, or peripheral device. In some embodiments, the accelerator 12 may be, for example, a central processing unit (CPU), a graphics processing unit (GPU), an arithmetic logic unit (ALU), a field programmable gate array (FPGA), any other type of integrated circuit, state machine, an advanced RISC machine (ARM) based processor, other similar elements, or a combination of the above elements. The accelerator 12 may store a pre-trained artificial intelligence (AI) model. The artificial intelligence model may be trained by a data set including multiple facial images with pre-labeled emotional statuses, so that the artificial intelligence model may classify subsequent input facial images to determine whether the facial images fall into any of the preset emotional statuses. The accelerator 12 may perform image recognition on the facial image by executing the artificial intelligence model to determine the emotional status of the user. For example, the artificial intelligence model stored in the accelerator 12 may be a machine learning model of supervised or unsupervised learning, or a deep learning model under the above classification, such as a region-based convolutional neural network (RCNN) series, which may be, for example, RCNN, fast RCNN, mask RCNN and faster RCNN, SSD and YOLO (you only look once) series (e.g., YOLO, YOLOv2, YOLO 9000 and YOLOv3) and other models. Furthermore, the above-mentioned artificial intelligence model may be built based on decision trees, logistic regression, SVM (support vector machine), naive Bayes, kNN, or other suitable algorithms. In addition, for the aforementioned deep learning model, one may also consider aspects such as its image pre-processing, model architecture, model depth, convolution layer parameters, pooling layer, activation function, loss function, specific architecture of each model, design philosophies, accuracy, and execution speed. By combining, adding, adjusting, and modifying these aspects, one may create a customized deep learning model.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 6, 8, and 10 – 14 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 1 – 6 and 8 – 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horseman et al., herein after Horseman (U.S. Publication Number 2017/0162072 A1) in view of Popescu et al., herein after Popescu (WO 2022/242825 A1) further in view of Tzvieli et al., herein after Tzvieli (U.S. Publication Number 2018/0092587 A1).
Claim 1 (Currently Amended). Horseman teaches an electronic system (Figure 2; paragraph 2 discloses systems, machines, and non-transitory computer medium having computer program instructions stored thereon, for providing training systems), comprising:
a camera, configured to take a facial image of a user (paragraph 72 discloses a facial recognition sensor which may include image sensors (such as a camera) operable to record images of a user’s face during a training simulation);
an auxiliary feature sensor, configured to sense an auxiliary feature of the user (paragraph 9 discloses the training system includes one or more sensors in communication with the one or more input and output units, where, for example, the sensors may include one or more heart rate sensors, one or more respiratory rate sensors, one or more skin conductance sensors, one or more blood pressure sensors, and/or one or more facial recognition sensors); and
an accelerator (paragraph 9 discloses the training system includes one or more processors and one or more input and output units in communication with the one or more processors, where the processer is interpreted as an accelerator), comprising a processor (paragraph 8 discloses one or more processors and one or more input and output units in communication with the one or more processors), wherein the processor of the accelerator is configured to calculate an emotional status of the user according to the facial image (paragraph 10 discloses the obtaining may include converting physical facial features captured by the one or more facial recognition sensors into electronic facial data indicative of one or more of gender, age, and emotion of the first user), and
configured to calculate an emotional level of the user in the emotional status based on the auxiliary feature (paragraph 10 discloses determining a stress level of the first user responsive to analysis of at least the electronic heart rate data, the electronic respiratory rate data, the electronic skin conductance data, the electronic blood glucose data, and the electronic blood pressure data).
Horseman fails to explicitly teach the following limitations met by Popescu as cited:
by executing an artificial intelligence model, wherein the processor of the accelerator is further configured to classify the facial image into preset emotional statuses (paragraph 6 discloses an emotional status of the user based at least in part on the one or more facial characteristics; paragraph 7 discloses analyzing a face of a user in order to electronically evaluate, for example by an artificial intelligence process and thus by machine learning algorithms, whether the user likes or dislikes the presented exercise-related content and/or struggles with performing an exercise following the instructions presented in the exercise-related content; paragraph 9 discloses pre-defining different types of emotions and assigning scores to them based on the one or more facial characteristics allows for categorizing collected data on the one or more facial characteristics and to provide for organized data sets based on which an algorithm may decide on an emotional status of the user);
wherein the artificial intelligence model is trained by a data set including a plurality of facial images labeled with the preset emotional statuses (paragraph 21 discloses at least one computing device may be part of the fitness device and may be configured (a) to transmit emotional data to a remote analysis server of the training system implementing machine learning algorithms and (b) to receive an analysis results, in response to transmitting the data to the remote analysis server, from the remote analysis server indicating whether and how the exercise-related content is to be adapted; paragraph 62 discloses emotions coinciding with certain (relative) positions and movements of the virtual facial markers M, in particular over time, may be trained by machine learning so that images of the face F of the user P may be associated with a set of pre-defined emotions).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Horseman to further include a training system and method with emotion assessment as disclosed by Tzvieli.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Horseman in this way to proposed training system may thus improve
user experience and/or optimize a current workout and/or a future workout based on a determined
emotional status of the user taking into account one or more facial characteristics. (Popescu: paragraph 7).
Horseman and Popescu fail to explicitly teach the following limitations met by Tzvieli as cited:
wherein the processor of the accelerator is further configured to determine whether to issue an alert signal through a network based on an urgency level of physiological status of the user, and the urgency level of physiological status of the user is determined based on both the emotional status and the emotional level calculated by the accelerator (paragraph 89 discloses a user interface (UI) may be utilized, in some embodiments, to notify the user and/or some other entity, such as a caregiver, about the physiological response and/or present an alert responsive to an indication that the extent of the physiological response reaches a threshold. The UI may include a screen to display the notification and/or alert, a speaker to play an audio notification, a tactile UI, and/or a vibrating UI; paragraph 104 discloses early notice (alert) regarding an allergic reaction since it may enable a user to take action in order to reduce the severity of the allergic reaction (urgency); paragraph 200 discloses the system includes a computer that utilizes a model to detect an emotional state and/or stress level; paragraph 320 discloses the computer may detect an emotional response of the user based on (i) facial expressions in the visible-light images utilizing image processing, and/or (ii) facial skin color changes (FSCC)),
wherein in response to that the urgency level of physiological status of the user falls into a first range, the processor of the accelerator instructs a first alert device to issue a first alert signal (paragraph 89 discloses a user interface may be used to notify the user and/or other entity (e.g. caregiver) about the physiological response and/or present an alert responsive to an indication that the physiological response reaches a threshold),
wherein in response to that the urgency level of physiological status of the user falls into a second range and a network setting of the electronic system is turned on, the accelerator issues a second alert signal as an emergency signal through the network (paragraph 71 discloses different physiological responses may involve different types of thresholds, including an upper and/or lower threshold; paragraph 107 discloses a second threshold which is higher than the first threshold (a second range)).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Horseman and Popescu to further include monitoring a user’s stress level, which can help detect which of the various potential stressors should be considered actual stressors as disclosed by Tzvieli.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Horseman and Popescu in this way to be able to collect thermal measurements at various regions of a person’s fact in order to detect stress (Tzvieli: paragraph 9).
Claim 2 (Original). Horseman, Popescu, and Tzvieli teach the electronic system according to claim 1. Horseman teaches wherein the accelerator determines the emotional status of the user by recognizing the facial image through an image recognition technology (paragraph 72 discloses a facial recognition sensor which may include image sensors (such as a camera) operable to record images of a user’s face during a training simulation).
Claim 3 (Currently Amended). Horseman, Popescu, and Tzvieli teach the electronic system according to claim 2. Horseman teaches wherein the accelerator determines the emotional status of the user by performing the image recognition technology on the facial image through executing the artificial intelligence model (paragraph 157 discloses machine learning is used to determine a set of desirable biometric response to one or more of the training modules and/or virtual reality simulations of training modules).
Claim 4 (Original). Horseman, Popescu, and Tzvieli teach the electronic system according to claim 1.
Horseman and Popescu fail to explicitly teach the following limitations met by Tzvieli as cited:
wherein the auxiliary feature comprises a facial temperature change signal, and the accelerator determines the emotional level of the user in the emotional status by analyzing the facial temperature change signal of the user (paragraph 200 discloses a computer that utilizes a model to detect an emotional state and/or stress level based on THROI1, THROI2, THROI3, and THROI4, where TH indicates a thermal measurement of a region on a periorbital area of the user (see paragraph 11)).
The motivation to combine the teachings of Horseman, Popescu, and Tzvieli are disclosed in the teachings of claim 1, and incorporated herein.
Claim 5 (Original). Horseman, Popescu, and Tzvieli teach the electronic system according to claim 1. Horseman teaches wherein the auxiliary feature comprises a physiological change signal, the physiological change signal comprises at least one of blood pressure, blood oxygen, pulse, and body temperature, the accelerator determines the emotional level of the user in the emotional status by analyzing the physiological change signal (paragraph 55 discloses the collected data may include one or more of heart rate, respiratory rate, skin conductance, blood glucose, electrical activity (e.g. brain and nerve activity), blood pressure, and facial features ( e.g. shapes, positions, sizes, etc.)).
Claim 6 (Original). Horseman, Popescu, and Tzvieli teach the electronic system according to claim 4.
Horseman and Popescu fail to explicitly teach the following limitations met by Tzvieli as cited:
wherein the auxiliary feature further comprises a physiological change signal, the accelerator determines the emotional level of the user by analyzing the facial temperature change signal, and then verifies the emotional level by analyzing the physiological change signal (paragraph 64 discloses detecting physiological responses including stress, an allergic reaction, an asthma attack, a stroke, dehydration, intoxication, or a headache, where detecting a physiological response may include determining whether the user has/had the physiological response, identifying an imminent attack associated with the physiological response, and/or calculating the extent of the response.
The motivation to combine the teachings of Horseman, Popescu, and Tzvieli is discussed in the rejection of claim 1, and incorporated herein.
Claim 8 (Original). Horseman, Popescu, and Tzvieli teach the electronic system according to claim 1. Horseman discloses wherein the accelerator is disposed on a motherboard or a connector, or is disposed on an edge device connected through a connection terminal or a transmission line (Figure 3; paragraph 63 discloses The processor may be any suitable processor capable of executing/performing program instructions, where the processor may include a central processing unit (CPU) that carries out program instructions ( e.g., of the mobile device module) to perform arithmetical, logical, and input/output operations of the user computer).
Method claims 9 – 12 and 13 – 14 repeat the subject matter of claims 1 – 4 and 5 – 6. As the underlying processes of claims 9 – 12 and 13 – 14 have been shown to be fully disclosed by the teachings of Horseman, Popescu, and Tzvieli in the above rejections of claims 1 – 4 and 5 – 6; as such, these limitations (9 – 12 and 13 – 14) are rejected for the same reasons given above for claims 1 – 4 and 5 – 6 and incorporated herein.
Response to Arguments
Applicant's arguments filed March 12, 2026 have been fully considered but they are not persuasive. The Applicant’s arguments have been addressed in the order in which they were presented.
Discussion of Claim Rejections under 35 USC § 101
The Applicant argues amended claim 1 is integrated into a practical application. The Examiner respectfully disagrees. The additional elements of the present claims fail to integrate the exception into a practical application of the exception. The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. For example, the 2019 PEG guidelines recite limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The present claims fail to demonstrate an improvement to the functioning of a computer or to any other technology or technical field. Thus, Applicant’s argument is not persuasive, and the rejection is maintained.
Discussion of Claim Rejections under 35 USC § 103
The Applicant argues the amended claims overcome the prior art references, Horseman and Tzvieli. In response to the Applicant’s argument, the Examiner submits that the amended limitations were not in the previously pending claims; it is respectfully submitted that the Examiner has applied new prior art to the amended claims, and as such, the amended claims are addressed in the above Office Action.
Conclusion
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KRISTINE K. RAPILLO
Examiner
Art Unit 3682
/K.K.R/Examiner, Art Unit 3682