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 . This action is made non-final.
This action is made in response to the claims filed on July 20, 2023. Claims 1-8 are pending in the case and have been examined. Claims 1-8 are rejected.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-8 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-3 are directed to an information processing apparatus (a machine), Claims 4-7 are directed to a method (a process), and Claim 8 is directed to a program for causing a computer to function (a manufacture). Therefore, Claims 1-8 are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “machine learning”, and “estimation model”) [see MPEP 2106.04(a)(2)(III)].
“selecting at least one feature quantity corresponding to each of a plurality of modalities from among a plurality of feature quantities based on respective utility evaluation results for the plurality of feature quantities” (e.g., evaluating information and selecting information based on the evaluation)
“based on a result of verifying estimation accuracy, a combination of feature quantities for use in the machine learning” (e.g., evaluating information and selecting information based on the evaluation)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “machine learning”, and “estimation model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding “which are usable for machine learning of an estimation model for estimating a stress level”, and “the verification being carried out by applying combinations of feature quantities included in the feature set to the machine learning of the estimation model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “machine learning”, and “estimation model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding “which are usable for machine learning of an estimation model for estimating a stress level”, and “the verification being carried out by applying combinations of feature quantities included in the feature set to the machine learning of the estimation model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “machine learning”, and “estimation model”) [see MPEP 2106.04(a)(2)(III)].
“for each of the plurality of modalities, evaluation of utility and selection of a feature quantity based on a result of the evaluation” (e.g., evaluating information and selecting information based on the evaluation) Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
In accordance with Step 2A, Prong 2, the claim does not include any additional elements and the judicial exception is not integrated into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “a behavioral modality into which a feature quantity is classified which has been generated using measurement data that pertains to a behavior reflecting a stress state of a subject”, and “a physiological modality into which a feature quantity is classified which has been generated using measurement data that pertains to a physiological phenomenon reflecting a stress state of the subject” these additional elements are recited at a high level of generality and amounts to extra-solution activity of processing specific types of data, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “a behavioral modality into which a feature quantity is classified which has been generated using measurement data that pertains to a behavior reflecting a stress state of a subject”, and “a physiological modality into which a feature quantity is classified which has been generated using measurement data that pertains to a physiological phenomenon reflecting a stress state of the subject” limitations, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of processing specific types of data, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claims 4 and 8
Claims 4, and 8 recites a selection method, and a program for causing a computer to function, respectively. Each of these claims correspond directly to the steps of claim 1, respectively, with the additional of generic computer instructions and methods which are insufficient to render the claims subject matter eligible for the same reasons described above.
Regarding claim 5
Step 2A Prong 1
Claim 5 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “machine learning”, and “estimation model”) [see MPEP 2106.04(a)(2)(III)].
“generating, …, training data for use in machine learning by associating, as correct answer data, a stress level of a subject with a combination of feature quantities which has been selected by a feature quantity selection method” (e.g., labeling/classifying/organizing data and associating it as stress level labels form selected data)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “by at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “by at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 6
Step 2A Prong 1
Claim 6 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “by at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding “generating, …, an estimation model by machine learning using training data which has been generated by a training data generation method recited in claim 5” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “by at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding “generating, …, an estimation model by machine learning using training data which has been generated by a training data generation method recited in claim 5” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 7
Step 2A Prong 1
Claim 7 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “machine learning”, and “estimation model”) [see MPEP 2106.04(a)(2)(III)].
“estimating, …, a stress level of a subject” (e.g., observing data or a subject and assigning a value/label or other label to it depending on its state)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “by at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “by at least one processor” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 2, 4, and 8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ollander et al. ("Feature and Sensor Selection for Detection of Driver Stress", referred to as Ollander.
Regarding claim 1, Ollander teaches, an information processing apparatus (Ollander is directed to a computer implemented feature/sensor selection technique for an automatic stress state detector to be executed on computer apparatuses), comprising:
a first selection means that generates a feature set by selecting at least one feature quantity corresponding to each of a plurality of modalities from among a plurality of feature quantities based on respective utility evaluation results for the plurality of feature quantities which are usable for machine learning of an estimation model for estimating a stress level (Pages 119-120 Section 5.3 and table 2: Describes selecting features from a plurality of physiological modalities/sensor signals for stress detection. It’s sensor signals track physiological data, such as, heart rate, skin conductivity, electro muscular activity, and respiration, and Table 2 lists corresponding feature quantities for those modalities, including mean heart rate, LF/HF heart rate features, mean skin conductance level, mean/absolute derivative of skin conductance, RMS of EMG, respiration rate, and standard deviation of respiration. After defining the set of features, it important to choose an optimal subset for training a generalizable and accurate model, and that an exhaustive feature selection was performed within the features of each sensor, thereby giving an optimal subset of features from every sensor signal. The HR, SC, EMG, and respiration sensor signals correspond to a plurality of modalities, the calculated HR/SC/EMG/respiration features correspond to feature quantities, and the selected optimal subset of features form each sensor signal corresponds to generating a feature set by selecting at least one feature quantity corresponding to each modality. The selected feature quantities are usable for machine learning of a stress estimation model as it uses the selected features in a classifier/stress model to distinguish driver stress states, including rest versus driving and low stress versus higher stress.); and
a second selection means that selects, based on a result of verifying estimation accuracy, a combination of feature quantities for use in the machine learning, the verification being carried out by applying combinations of feature quantities included in the feature set to the machine learning of the estimation model (Pages 119-120 Section 5.3 and table 2: Describes a wrapper feature selection in which feature combinations are selected based on classifier performance. The “solve this problem by choosing feature combinations based upon their classification performance.” He classifier performance is measured using balanced accuracy. After performing feature selection within each sensor, “the respective subsets were combined,” producing sensor pairs, sensor triplets, and an all sensor combination, and that “the combination with the best classification performance was then chosen for each use case.” Thus, the selected subsets of HR, skin conductivity, EMG, and respiration features correspond to the claimed feature quantities included in the feature set, and the selection of the best performing combined subsets correspond to the selecting, based on verified estimation/classification accuracy, a combination of feature quantities for use in machine learning of the stress estimation model.).
Regarding claim 2, Ollander teaches, the information processing apparatus according to claim 1, wherein:
the first selection means generates the feature set by carrying out, for each of the plurality of modalities, evaluation of utility and selection of a feature quantity based on a result of the evaluation (Pages 119-120 Section 5.2 and table 3: Describes filter feature selection methods that test features individually “to get an idea of their predictive power of stress levels one by one,” including Pearson correlation, Spearman correlation, Fisher score, and ROC/AUC analysis. It shows in table 3, which ranks the best features according to the filter feature selection methods. It performs feature selection within the features of each sensor, fiving optimal subset features from every sensor signal. Thus, it evaluates the utility/predictive power of feature quantities for the HR, SC, EMG, and respiration modalities/sensor signals and selects feature quantities based on the resulting feature selection scores/performance.).
Regarding claim 4, which recites substantially the same limitations as claim 1. Claims 4 and 8 further recite a method (Ollander is directed to a method to be executable on computer hardware) to perform the apparatus steps of claim 1, respectively, and is therefore rejected on the same premise.
Regarding claim 8, which recites substantially the same limitations as claim 1. Claims 4 and 8 further recite a method (Ollander is directed to a computer implemented feature/sensor selection technique for an automatic stress state detector to be executed on computers via executable computer software) to perform the apparatus steps of claim 1, respectively, and is therefore rejected on the same premise.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 3, 5, 6, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ollander et al. ("Feature and Sensor Selection for Detection of Driver Stress", referred to as Ollander), in view of Can et al. (“Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information”, referred to as Can).
Regarding claim 3, Ollander teaches, the information processing apparatus according to claim 1 or 2, wherein:
the plurality of modalities include .. a physiological modality into which a feature quantity is classified which has been generated using measurement data that pertains to a physiological phenomenon reflecting a stress state of the subject (Pages 116-118 Section 2, Section 4, Section 4.1 and Table 2: Analyzes physiological sensor signals/features for detecting driver stress, including heart rate skin conductivity, electromuscular activity, and respiration. It discloses that the MIT Stress Recognition in Automobile Drivers Database includes physiological measurement data including ECG, HR, EMG, skin conductivity, and respiration signals. Table 2 lists feature quantities generated form those physiological signals, including mean heart rate, HE frequency band features, mean skin conductance level, RMS of EMG, respiration rate, and standard deviation of respiration. It explains that HR/HRV features, skin conductivity, EMG activity, and respiration features are relevant to stress detection. Thus, its HR/ECG, skin conductivity, EMG, and respiration signals correspond to physiological modalities, and the generated HR/SC/EMG/respiration features correspond to feature quantities generated form physiological measurement data reflecting the subjects stress state)
Although Olander teaches a physiological modality into which a feature quantity is classified which has been generated using measurement data that pertains to a physiological phenomenon reflecting a stress state of the subject. It does not teach a behavioral modality into which a feature quantity is classified which has been generated using measurement data that pertains to a behavior reflecting a stress state of a subject.
Can teaches, a behavioral modality into which a feature quantity is classified which has been generated using measurement data that pertains to a behavior reflecting a stress state of a subject (Pages 8722-8724 Section II A, Figure 1, and Pages 8726-8729 Section III: Describes that contextual information, such as physical activity level and activity type, can be added to physiological signals to improve stress classification accuracy. It teaches that the smart band provides 3D acceleration data and that features are obtained from sensory signals and fed to stress predictor machine learning algorithms. It’s accelerometer feature extraction section discloses extracting accelerometer features including mean acceleration over the X axis, Y axis, and Z Axis, mean acceleration magnitude, energy, step count, and stillness. It uses activity type/ context information, including relaxation, lecture, and presentation sessions, and further teaches that physical activity related context data such as stillness and step count improves daily perceive stress detection accuracy. Thus, its acceleration/activity/stillness/step count data corresponds to measurement data pertaining to behavior reflecting a subject stress state, and its generated accelerometer/activity features correspond to feature quantities classified into a behavioral modality.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Ollander’s stress feature system with Can’s behavioral/activity context features. Doing so would have enabled the system to improve stress classification accuracy and make stress estimation more robust in real life/unrestricted environments.
Regarding claim 5, Ollander in view of Can teaches, generating, by at least one processor, training data for use in machine learning by associating, as correct answer data, a stress level of a subject with a combination of feature quantities which has been selected by a feature quantity selection method recited in claim 4 (Ollander Pages 117 Section 4.1 and Pages 120, Section 5.3.4: Teaches generating/using feature combinations selected by the feature quantity selection method Of claim for in particular Old Lander teaches that, after an optimal subset for each individual sensors has been defined, the respective subsets were combined, and the combination with the best classification performance was chosen for Each use case. Table four presents the optimal combinations of features extracted from each sensor. ;
Can Pages 8722-8724 Section II A, Pages 8725-8726 Section II E, and Pages 8727-8729 Section III B: Teaches generating training data for use in machine learning by associating stress level class labels with feature vectors. In particular, it teaches that features are obtained from sensory signals and fed to stress predictor machine learning algorithms, and that model training is performed by running machine learning algorithms on feature vectors with generated class labels. It also measures perceived stress level using self-reports, including NASA TLX frustration scores and daily perceived stress questionaries.).
Regarding claim 6, Ollander in view of Can teaches, an estimation model generation method, comprising:
Can further teaches, generating, by at least one processor, an estimation model by machine learning using training data which has been generated by a training data generation method recited in claim 5 (Pages 8722-8724 Section II A: Teaches generating an estimation model by machine learning using training data. It uses features which are obtained from sensory signals and fed to stress predictor machine learning algorithms, that pre-train machine learning models are needed, and that model training is performed by running the machine learning algorithms on feature vectors with generated class labels. It uses machine learning classifier algorithms To detect distinct levels of stress, including multilayer perception, random forest, K nearest neighbors, linear discriminant analysis, PCA, and SVM, with tenfold straight stratified cross validation and hyperparameter tuning by grid search. Thus, the trained stress predictor/classifier models correspond to the claimed estimation model generated by machine learning using the labeled feature vector training date.).
Regarding claim 7, Ollander in view of Can teaches, a stress level estimation method, comprising:
Can further teaches estimating, by at least one processor, a stress level of a subject using an estimation model which has been generated by an estimation model generation method recited in claim 6 (Pages 8722- Pages 8722-8724 Section II A, Pages 8726-8729 Section II A, B and C: Describes an automatic stress detection system using smart bands and states that session based, daily, and long-term perceived stress levels could be identified using the proposed system. It further teaches that features obtained from sensory signals are fed to stress predictor machine learning algorithms, that pretrained machine learning models are needed to use them system, and that model training is performed using feature vectors with generated class labels. Experimental results then show predicting/classifying perceived stress levels, including 2 class and 3 class session-based perceived stress levels, daily perceived stress levels, and long-term perceived stress levels. Thus, use of trained stress predictor machine learning models to predict/classify perceived stress levels corresponds to estimating a stress level of a subject using the estimation model.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892 for additional art including.
US 20120289793 A1: behavioral/activity data and stress monitoring
US 20170071551 A1: physiological stress detection with activity context
US 20160140320 A1: behavioral mobile data and survey/self-assessment predictive model
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONALD T RODEN whose telephone number is (571)272-6441. The examiner can normally be reached Mon-Thur 8:00-5:00 EST.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.T.R./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
Prosecution Insights