DETAILED ACTION
Introduction
Applicant's submission filed on 12/27/2024 has been entered. Claims 1-15 are pending in the application 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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an extra solution activity abstract idea without significantly more.
According to USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, method of manufacture, or composition of matter), or
STEP 2: the claim recites a judicial exception (e.g. an abstract idea) without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? The guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts- mathematical relationships, formulas or equations, calculations
Certain methods of organizing human activity- fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people
Mental processes- concepts that are practicably performed in the human mind (including an observation, evaluation, judgement, or opinions)
STEP 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? The guidelines provide the following exemplary considerations that are indicative than an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? Consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, or conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Using the two-step inquiry, claim 10 is directed to an abstract idea as show below:
STEP 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
YES. Claim 10 is directed to a method.
STEP 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
YES. The claim recites an abstract idea:
The limitation calculating a plurality of probabilities corresponding, respectively, to a plurality of emotional states based on the voice data as drafted, is a process that, under its broadest reasonable interpretation, can be performed by a human determining the human state by listening to the voice and computing the probability.
The limitation of obtaining a weight for one or more emotional states of the plurality of emotional states based on the biometric data and the log data, as drafted, is a process that, under its broadest reasonable interpretation, recites a mathematical formula or calculation based on the gathered data.
The limitation of determining a final emotional state of the plurality of emotional states reflecting the obtained weight, as drafted, is a process that, under its broadest reasonable interpretation, recites a mathematical formula or calculation.
STEP 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO.
Claim 1 recites the additional element of processing through a “artificial intelligence device”, which are recited at a high level of generality and amounts to merely using a computer as a tool to perform an abstract idea or mere instructions to apply the exception using a generic computer component. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the insignificant extra-solution activities abstract idea but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Claim 1 also recites collecting biometric data of a user, log data of the user, and voice data corresponding to a voice uttered by the user, as drafted, is a process that, under its broadest reasonable interpretation, which is a data gathering step (or pre solution activity), that adds insignificant extra-solution activity to the judicial exception. This judicial exception is not integrated into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO.
The claim does 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, the additional element of using a convolutional neural network and vector quantizer amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 is not patent eligible.
Claim 11 further specifies steps to compute the weight of the emotional state, is a mathematical computation and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 12 further recites specifies steps to compute the weight of the emotional state, is a mathematical computation and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 13 further specifies identification of different emotional states based on the values, is a mathematical computation and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 14-15 further specifies identification of additional weights based on some parameters, is a mathematical computation and does not reflect an improvement in the functioning of a technology or computer. The claims are not patent eligible.
Claims 1-6 are analogous to claims 10-15 respectively, as directed to a device comprising a sensor component and a processing device, the processing device to perform the operations set forth in claims 10-15 and are subjected to the same rejections as claims 10-15 respectively. Claim 7 recites an equation which is a mathematical computation. The claims are not patent eligible.
Claim 8-9 recites the information details on the biometric data and emotion classification model, which are extra-solutional activity or mathematical computation and does not reflect an improvement in the functioning of a technology or computer. These claims are not patent eligible.
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.
Claims 1-2 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Fox et. al. US PgPub. 2025/0045976 in view of Kalinli-Akbacak et. al. US Patent 9,031,293(referred as Kalinli).
Regarding claim 1, Fox teaches an artificial intelligence device comprising:
a sensor configured to collect biometric data of a user, log data of the user, and voice data corresponding to a voice uttered by the user ( see Fox, [0053] 204(speech), 206 (biosensor), see Fox [0039] 202 for sweat/activity level (log data of the user) ) ; and a processor configured to: calculate a plurality of probabilities corresponding, respectively, to a plurality of emotional states based on the voice data (see Fox, [0043-0044] discusses classification based on the vector and the probability of the emotion, Fig 2 , 222 Speech emotion vector); obtain a weight for one or more emotional states of the plurality of emotional states based on the biometric data and the log data (see Fox, Fig. 3B, vectors 320 and 324 which are processed by to compute the weights for the manager neural network 326 to generate user emotional state prediction 328 ); and determine a final emotional state of the plurality of emotional states reflecting the obtained weight(see Fox, Fig. 3B 328 Manager neural network 326 generates user emotional state prediction 328 by performing end-to-end classification of the emotional state of the user ).
Fox teaches calculate a plurality of probabilities corresponding, respectively, to a plurality of emotional states based on the voice data based on classification of the vector and probability of the emotion, to further teach the probabilities of emotional states, Kalinli teaches a sensor configured to collect biometric data of a user, log data of the user, and voice data corresponding to a voice uttered by the user ( see Kalinli, col 3 lines 13-16 describes sensors to collect voice information, physical features( biometric). See Kalinli col 3 lines 57-60 context features ( log data) ) ;calculate a plurality of probabilities corresponding, respectively, to a plurality of emotional states based on the voice data(see Kalinli, col 8 lines 3-27 the machine learning algorithm 108'(Fig. 1A) may determine a probability for each of a number of different possible emotional states and determine that the state with the highest probability is the estimated emotional state. Fig. 1 A ( 107 ( speech), Fig. 1 A(108), 1 D(108A)); obtain a weight for one or more emotional states of the plurality of emotional states based on the biometric data and the log data (see Kalinli, col 9 lines 23—28 compute Ep( weight based on biometric data) Ec (weight based on log data), col 9 lines 38-47 discusses the machine learning will determine how to use and weight the individual classifiers to maximize the emotion recognition performance in a data driven way using some training data that has emotion class labels); and determine a final emotional state of the plurality of emotional states reflecting the obtained weight (see Kalinli, col 9 lines 28—33 computes the derive the final estimated emotional state 115, col 9 lines 38-47 discusses the machine learning will determine how to use and weight the individual classifiers to maximize the emotion recognition performance in a data driven way using some training data that has emotion class labels).
Fox and Kalinli are considered to be analogous to the claimed invention because both relate to user emotion prediction process based on multimodal parameters. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fox to determine user emotion based on various parameters with the different cues from different features of user along with cues from speech teachings of Kalinli to improve emotional recognition with additional modalities (see Kalinli, col 1 lines 23-33).
Regarding claim 2, Fox in view of Kalinli teaches artificial intelligence device of claim 1. Kalinli further teaches wherein the processor is further configured to obtain the weight based on activity of the user not being detected based on the log data, and a heart rate included in the biometric data changing by more than a certain rate(see Kalinli, col 2 line 66 – col 3line 2 discusses activity/presence of other player ( part of context vector(log data)); Kalinli, col 4 lines 33-38 describes heart rate gather by 113 ). The same motivation to combine as claim 1 applies here.
Regarding claim 8, Fox in view of Kalinli teaches artificial intelligence device of claim 1. Kalinli further teaches wherein the biometric data includes one or more of a heart rate of the user or a heart rate variability (see Kalinli, col 4 lines 33-35 describes heart rate gather by 113 ), wherein the log data includes one or more of location data of the user or usage data of a home appliance indicating whether the home appliance is used (see Kalinli, col 2 lines 63-col 3 line 9 describes the context data based on the environment (location) of the user and the game state ( home appliance being used)). The same motivation to combine as claim 1 applies here.
Regarding claim 9, Fox in view of Kalinli teaches artificial intelligence device of claim 1. Kalinli further teaches an artificial neural network-based emotion classification model that classifies an emotional state of the user based on the voice data (see Kalinli, col 7 lines 61-63 describes machine learning model to classify/determine the emotional state 115’, Kalinli, col 9 lines 18-23 describes the machine learning classifier to analyze acoustic and linguistic features to obtain the corresponding emotional states ), wherein the emotion classification model is learned through a supervised learning algorithm comprising a Support Vector Machine (see Kalinli, col 7 line 63- col 8 line 38, describes SVM to determine the emotion classes). The same motivation to combine as claim 1 applies here.
Regarding claim 10, is directed to a method claim corresponding to the device claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Regarding claim 11, is directed to a method claim corresponding to the device claim presented in claim 2 and is rejected under the same grounds stated above regarding claim 2.
Claims 3-6 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Fox et. al. US PgPub. 2025/0045976 in view of Kalinli-Akbacak et. al. US Patent 9,031,293(referred as Kalinli) further in view of Siegle et.al US PgPub. 2019/0076643.
Regarding claim 3, Fox in view of Kalinli teaches artificial intelligence device of claim 2. Kalinli teaches to obtain the weight based on the activity of the user not being detected based on the log data ( see Kalinli, col 2 line 66 – col 3line 2 discusses activity/presence of other player ( part of context vector(log data)) ). However, Fox in view of Kalinli fail to teach a number of times that the heart rate changes by more than the certain rate being more than a threshold number.
However, Siegle teaches further configured to obtain the weight based on the activity of the user not being detected based on the log data, and a number of times that the heart rate changes by more than the certain rate being more than a threshold number (see Siegle, [0040-0042] describes calculating stress state vs resting state and including factors based on the threshold and the change in high frequency heart rate variability (HF-HRV) to estimate stress; Siegle[0036] running estimate of power in HF-HRV band which is associated with parasympathetic nervous system activity (activity of user not being detected) and emotion regulation capability, and which can be referred to as an emotional regulation parameter or value. ).
Fox in view of Kalinli teach the multi modal processing of speech and other biometric features for emotion detection, however does not teach heart rate changes. Siegle teaches computation of heart rate variability to estimate stress. Using the known technique of heart rate variability to estimate stress as taught by Siegle (see Siegle, [0038]), to provide the determination heart rate variability in the references Fox in view of Kalinli and to determine the stress based on the heart rate variability , such as improved stress detection would have been obvious to one of ordinary skill in the art.
Regarding claim 4, Fox in view of Kalinli further in view of Siegle teaches artificial intelligence device of claim 3. Siegle teaches wherein the plurality of emotional states include a happy state, a surprise state, a fear state, a sad state, a disgust state, an angry state and a neutral state(see Siegle, [0046], a 4-layer pattern-network classifier was trained to recognize the emotion associated with short vocalizations (neutral, calm, happy, sad, fearful, angry, disgusted, surprised), wherein the processor is further configured to assign a weight having a certain value to each of the surprise state, the fear state, the angry state, and the happy state based on a cumulative number of times that the heart rate increases by more than the certain rate being more than the threshold number( see Siegle, [0036-0038] discusses processing of GSR which is based on Plethysmograph data which is based on the inter-beat series includes a time duration between each successive beat in the detected heartbeat signal; Siegle [0042]; [0052] discusses the processing of the various emotions and arousal patterns compared to a threshold to determine the emotion state for the particular user). The same motivation to combine as claim 3 applies here.
Regarding claim 5, Fox in view of Kalinli further in view of Siegle teaches artificial intelligence device of claim 4. Siegle teaches wherein the processor is further configured to assign a second weight having a certain second value to each of the disgust state and the sad state based on a cumulative number of times that the heart rate decreases by more than the certain rate being more than the threshold number ( see Siegle, [0050-0052] discusses a negative tone (fear, sadness, disgust) and the user selects a threshold to indicate the stress level based on the arousal scale based on the transducer information ( heart rate changes)). The same motivation to combine as claim 3 applies here.
Regarding claim 6, Fox in view of Kalinli further in view of Siegle teaches artificial intelligence device of claim 3. Siegle teaches wherein the plurality of emotional states include a happy state, a surprise state, a fear state, a sad state, a disgust state, an angry state and a neutral state(see Siegle, [0046], a 4-layer pattern-network classifier was trained to recognize the emotion associated with short vocalizations (neutral, calm, happy, sad, fearful, angry, disgusted, surprised), wherein the processor is further configured to assign a weight having a certain value to each of the surprise state, the fear state, the angry state, and the happy state based on a cumulative number of times that the heart rate increases by more than the certain rate being more than the threshold number( see Siegle, [0036-0038] discusses processing of GSR which is based on Plethysmograph data which is based on the inter-beat series includes a time duration between each successive beat in the detected heartbeat signal; Siegle [0042]; [0052] discusses the processing of the various emotions and arousal patterns compared to a threshold to determine the emotion state for the particular user). The same motivation to combine as claim 3 applies here.
Regarding claim 12, is directed to a method claim corresponding to the device claim presented in claim 3 and is rejected under the same grounds stated above regarding claim 3.
Regarding claim 13, is directed to a method claim corresponding to the device claim presented in claim 4 and is rejected under the same grounds stated above regarding claim 4.
Regarding claim 14, is directed to a method claim corresponding to the device claim presented in claim 5 and is rejected under the same grounds stated above regarding claim 5.
Regarding claim 15, is directed to a method claim corresponding to the device claim presented in claim 6 and is rejected under the same grounds stated above regarding claim 6.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fox et. al. US PgPub. 2025/0045976 in view of Kalinli-Akbacak et. al. US Patent 9,031,293(referred as Kalinli) further in view of Thirion US PgPub 2025/0339070 further in view of Siegle et.al US PgPub. 2019/0076643.
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Regarding claim 7, Fox in view of Kalinli teaches artificial intelligence device of claim 1. However, Fox in view of Kalinli fail to teach obtain a heart rate variability (HRV) arousal score by learning 39 features calculated from IBI (InterBeat Interval) with Random Forest;
obtain a Baevsky stress index based on [Equation 1], wherein Mo denotes a most frequent heart rate (RR) interval expressed in seconds, AMo denotes an amplitude calculated as a number of RR interval in a bin containing Mo using a 50 ms bin width, and MxDMn denotes a difference in seconds between a longest RR interval value (Mx) and a shortest RR interval value (Mn).
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However, Thirion teaches obtain a heart rate variability (HRV) arousal score by learning 39 features calculated from IBI (InterBeat Interval) with Random Forest(see Thirion,[0156], [216] ); obtain a Baevsky stress index based on [Equation 1], wherein Mo denotes a most frequent heart rate (RR) interval expressed in seconds, AMo denotes an amplitude calculated as a number of RR interval in a bin containing Mo using a 50 ms bin width, and MxDMn denotes a difference in seconds between a longest RR interval value (Mx) and a shortest RR interval value (Mn) (see Thirion, [0196-0203], equation 1 ).
Fox in view of Kalinli teach the multi modal processing of speech and other biometric features for emotion detection, however does not teach Baevsky stress index computation. Thirion teaches computation of Baevsky stress index to estimate state of stress of individual. Using the known technique of Baevsky stress index to estimate stress as taught by Thirion (see Thirion, [0035]), to provide the determination stress index in the references Fox in view of Kalinli and to determine the Baevsky stress index, such as improved stress detection would have been obvious to one of ordinary skill in the art.
However Fox in view of Kalinli further in view of Thirion fails to teach calculate a weight to be assigned to each of the plurality of emotional states based on [Equation 2], [Equation 2] Weight(w) = isNotActivation x (a x HRV Arousal score + b x Baevsky Stress Index) wherein isNotActivation has a value of 0 or 1 depending on whether the activity of the user has been detected, wherein the plurality of emotional states include a happy state, a surprise state, a fear state, a sad state, a disgust state, an angry state and a neutral state, wherein a has a matrix value that has a positive correlation with the angry state, the fear state, and the surprise state, has a matrix value that has a negative correlation with the sad state and the disgust state, and does not reflect weight with respect to the Happy state and the Neutral state, wherein b has a matrix value that has a positive correlation with the angry state, the fear state, the sad state, and the disgust state, has a matrix value that has a negative correlation with the happy state and the surprise state, and has a value of 0 with respect to the neutral state.
However Siegle teaches calculate a weight to be assigned to each of the plurality of emotional states based on [Equation 2], [Equation 2] Weight(w) = isNotActivation x (a x HRV Arousal score + b x Baevsky Stress Index) (see Siegle, [0038] discusses coefficients( weight) are seeded for stress detection)wherein isNotActivation has a value of 0 or 1 depending on whether the activity of the user has been detected(see Siegle, [0041], beta weights for the preceding equation, stress values are set to zero during rest and one (1) during the target state, e.g., stress. ), wherein the plurality of emotional states include a happy state, a surprise state, a fear state, a sad state, a disgust state, an angry state and a neutral state, wherein a has a matrix value that has a positive correlation with the angry state, the fear state, and the surprise state, has a matrix value that has a negative correlation with the sad state and the disgust state, and does not reflect weight with respect to the Happy state and the Neutral state(see Siegle, [0041-0042, 0050, 0055-0058] The various β coefficients( matrix values) that are derived through the use of the pattern recognition( emotional) neural network form a part of the individually calibrated profile that can be used to detect the onset of a period of stress or fatigue. Another aspect of the algorithm is that when more than a user-selected number of the vocal parameters (the user can select from 2-8 parameters) are outside 2 SD from the mean of neutral vocalizations, and when the person is deemed, via classification based on the RAVDESS corpus classifier, to have a negative tone (fear, sadness, disgust), the software provides user-selected stimulation waveforms to the stimulation generator. This is depicted in the user interface capture( wherein a has a matrix value that has a positive correlation) from the software that is depicted in FIG. 5 wherein the user has selected a threshold of four parameters, as is indicated by the “THRESHOLD” indicator, and which is reflected by the dashed line in the bar graph of FIG. 5. In FIGS. 7A, 7B, 7C, AND 7D Each data point in such figures is representative of how the user perceived the customization stimulation on an arousal scale between very calming and very arousing, and additionally how the user tolerated the customization stimulation on a valence scale between very negatively and very positively; includes software that allows subjective and physiologically based storage of stimulation parameters that optimally yield approach or departure from target or alarm states.), wherein b has a matrix value that has a positive correlation with the angry state, the fear state, the sad state, and the disgust state, has a matrix value that has a negative correlation with the happy state and the surprise state, and has a value of 0 with respect to the neutral state(see Siegle, see Siegle, [0041-0042, 0050, 0052, 0055-0058] The various β coefficients( matrix values) that are derived through the use of the pattern recognition( emotional) neural network form a part of the individually calibrated profile that can be used to detect the onset of a period of stress or fatigue. Another aspect of the algorithm is that when more than a user-selected number of the vocal parameters (the user can select from 2-8 parameters) are outside 2 SD from the mean of neutral vocalizations, and when the person is deemed, via classification based on the RAVDESS corpus classifier, to have a negative tone (fear, sadness, disgust), the software provides user-selected stimulation waveforms to the stimulation generator. This is depicted in the user interface capture( wherein a has a matrix value that has a negative correlation) from the software that is depicted in FIG. 5 wherein the user has selected a threshold of four parameters, as is indicated by the “THRESHOLD” indicator, and which is reflected by the dashed line in the bar graph of FIG. 5. In FIGS. 7A, 7B, 7C, AND 7D Each data point in such figures is representative of how the user perceived the customization stimulation on an arousal scale between very calming and very arousing, and additionally how the user tolerated the customization stimulation on a valence scale between very negatively and very positively; includes software that allows subjective and physiologically based storage of stimulation parameters that optimally yield approach or departure from target or alarm states).
Fox in view of Kalinli further in view of Thirion teach the multi modal processing of speech and other biometric features for emotion detection, however does not teach weight on emotional state based on activity of user. Siegle teaches beta weights for rest and target states and other customizations for stress detection. Using the known technique of customizable beta weight coefficients to estimate stress as taught by Siegle (see Siegle, [0054]), to provide the determination stress index in the references Fox in view of Kalinli further in view of Thirion and to determine the weight index, such as customizable stress detection would have been obvious to one of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Goldstein, US PgPub. 2020/0107778 teaches techniques for determining stress based on individual ideal stress index (see Goldstein, [0014]).
Sohne et al US PgPub. 2020/0035337 discusses emotion state of a subject based on flow state value determined by heart rate variability computation (see Sohne, [0121-0122]).
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/NANDINI SUBRAMANI/ Examiner, Art Unit 2656
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