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 .
Status of the Claims
The status of the claims as of the response filed 7/22/2025 is as follows: Claims 1-13 are currently amended and have been considered below.
Response to Amendment
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections for claims 1-13 are upheld.
Rejection Under 35 USC 102/103
The amendments made to the claims introduce limitations that are not fully addressed in the previous office action, and thus the corresponding 35 USC 102/103 rejections are withdrawn. However, Examiner will consider the amended claims in light of an updated prior art search and address their patentability with respect to prior art below.
Response to Arguments
Rejection Under 35 USC 101
On pages 9-10 of the response filed 7/22/2025 Applicant argues that the amended claims do not recite an abstract idea and instead “recite features providing an improvement to wearable technologies.” Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that the underlying functions of receiving a plurality of biological signals of a subject, determining a time zone of interest based on an index value of the plurality of biological signals exceeding a predetermined threshold for a predetermined time period, extracting one or more feature quantities from the plurality of biological signals acquired in the time zone of interest, calculating stress level data for the predetermined time period based on a user input, generating training data that associated the one or more extracted feature quantities as explanatory variables with the stress level data as objective variables, and training an estimation model based on the training data, wherein the estimation model is configured to output an estimation value indicating an estimated stress level based on one or more input feature quantities describe an abstract idea in the form of certain methods of organizing human activity such as managing personal behavior and/or interactions between people. For example, a clinician could observe biological signals from a subject over a period of time and use their medical expertise to pick out time zones of interest based on the biological signals exceeding a threshold for a certain amount of time (e.g. heart rate and breathing rate spiking above respective “normal” ranges for more than 5 minutes). The clinician could then note other feature quantities collected during that time zone of interest (e.g. skin temperature, magnitude of sweat values, blood pressure, HRV, etc.) and calculate an estimated stress level of the subject during the time zone of interest (e.g. “high” for a panic attack). The clinician could finally utilize the extracted features and estimated stress level associated with the time period of interest to use as training data for fitting an estimation model (e.g. a regression model, decision tree, etc.) configured to output an estimated stress level based on input features. Thus, the claims do recite an abstract idea; the use of a memory and processor executing stored instructions to perform such steps, as well as receipt of the biological signals from at least one sensor of a wearable terminal are then evaluated as additional elements beyond the abstract idea itself in the Step 2A – Prong 2 and Step 2B analyses.
On pages 10-11 of the response Applicant argues that the amended claims recite “a technical solutions [sic] that leverages hardware and stored instructions to ‘generate training data… and train an estimation model based on the training data’” which provide improvements to wearable technology. Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that the steps for generating training data and training an estimation model based on the training data are part of the abstract idea itself, because a human actor could manage their personal behavior to curate a dataset for use in fitting a predictive model like a regression equation, decision tree, etc. (as explained above). Because these functions are part of the abstract idea itself, they cannot provide integration into a practical application or “significantly more” than the abstract idea and thus do not confer eligibility (see MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”) In the instant case, the only additional elements beyond the abstract idea itself that are recited in the independent claims are the use of a memory and processor executing stored instructions to perform such steps, as well as receipt of the biological signals from at least one sensor of a wearable terminal. These additional elements do not provide improvements to the field of wearable technology itself, and instead amount to mere instructions to “apply” the exception with generic computing components (i.e. by utilizing high-level processing components to automate and/or digitize the otherwise-abstract receiving, determining, extracting, calculating, generating, training, etc. steps of the invention) as well as provide insignificant extra-solution activity in the form of mere data gathering (i.e. by utilizing unspecified sensors of a wearable terminal as a high-level means of providing the biological signals needed for the main analysis steps of the invention). Accordingly, Examiner maintains that the instant claims are directed to an abstract idea without integration into a practical application or “significantly more” and are thus not patent eligible under 35 USC 101.
Rejection Under 35 USC 102/103
Applicant’s arguments on pages 13-14 of the response with respect to alleged deficiencies of Sasangohar and Smets are fully considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 7 is objected to because of the following informalities: it has been amended to recite “the state of being exposed” and “the acute stress stimulus” in line 6, despite parent claim 1 not introducing such elements. Appropriate correction is required.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-7 are directed to an apparatus (i.e. a machine), claims 8-12 are directed to a method (i.e. a process), and claim 13 is directed to a computer-readable non-transitory storage medium (i.e. a manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1, 8, and 13 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, claim 1 (as representative) recites:
An information processing apparatus, comprising: a memory storing instructions; and at least one processor, wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to:
receive, from at least one sensor of a wearable terminal, a plurality of biological signals of a subject;
determine a time zone of interest based on an index value of the plurality of biological signals exceeding a predetermined threshold for a predetermined time period; and
extract one or more feature quantities from the plurality of biological signals acquired in the time zone of interest;
calculate stress level data for the predetermined time period based on a user input;
generate training data that associated the one or more extracted feature quantities as explanatory variables with the stress level data as objective variables;
train an estimation model based on the training data, wherein the estimation model is configured to output an estimation value indicating an estimated stress level based on one or more input feature quantities.
But for the recitation of generic computer components like a memory and processor executing stored instructions, the italicized functions, when considered as a whole, describe a medical diagnostic operation that could be achieved by a clinician or other medical professional managing their personal behavior and/or interactions with others. For example, a clinician could observe biological signals from a subject over a period of time and use their medical expertise to pick out time zones of interest based on the biological signals exceeding a threshold for a certain amount of time (e.g. heart rate and breathing rate spiking above respective “normal” ranges for more than 5 minutes). The clinician could then note other feature quantities collected during that time zone of interest (e.g. skin temperature, magnitude of sweat values, blood pressure, HRV, etc.) and calculate an estimated stress level of the subject during the time zone of interest (e.g. “high” for a panic attack). The clinician could finally utilize the extracted features and estimated stress level associated with the time period of interest to use as training data for fitting an estimation model (e.g. a regression model, decision tree, etc.) configured to output an estimated stress level based on input features. Thus, the steps recited in this claim describe various steps that a clinician could follow to manage their personal behavior, and accordingly claim 1 recites an abstract idea in the form of a certain method of organizing human activity. Claims 8 and 13 recite substantially similar subject matter as claim 1 and are found to recite an abstract idea under the same analysis.
Dependent claims 2-7 and 9-12 inherit the limitations that recite an abstract idea from their dependence on claims 1 and 8, respectively, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2-7 and 9-12 recite additional limitations that further describe the abstract idea identified in the independent claims.
Specifically, claims 2 and 9 specify that the feature quantities are extracted based on a change in a biological signal at a start time or an end time of the time zone of interest, which a clinician could accomplish by extracting features that change at the beginning or end of the identified time zones.
Claims 3-7 further specify various conditions of the identification and extraction steps, while claims 6-7 recite a further determination step. Each of these steps are types of determinations and judgements that a clinician would be capable of making when recognizing whether a subject is exposed to an acute stress stimulus, identifying time zones, and extracting corresponding features from the time zones for different types of subjects (e.g. males and females).
Claim 10 recites generating training data by associating, as correct answer data, a stress level of a subject with one or more feature quantities that have been extracted as in claim 8, which a clinician would be capable of achieving by labeling the extracted features in the training data with known ground truth stress level data.
Claim 11 recites generating an estimation model using training data generated as in claim 10, which a clinician could achieve by fitting a simple stress level estimation model (e.g. a decision tree, regression equation, etc.) based on the labeled training data.
Claim 12 recites estimating a stress level of a subject using the estimation model generated as in claim 11, which a clinician could achieve by executing the stress level estimation model (e.g. following appropriate branches through a decision tree, plugging values into an equation, etc.).
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1, 8, and 13 do not include additional elements that integrate the abstract idea into a practical application. The additional elements of claims 1, 8, and 13 include an information processing apparatus comprising a memory storing instructions and at least one processor for carrying out the steps (and equivalent processor/computer elements in claims 8 and 13), as well receiving the plurality of biological signals of a subject specifically from at least one sensor of a wearable terminal. These additional computer hardware elements, when considered in the context of each claim as a whole, merely serve to automate functions could be achieved via a human actor managing their personal behavior (as described above), and thus amount to instructions to apply the abstract idea with generic computer components. For example, a clinician can identify time periods of interest by looking at biological signals from a subject and extract feature quantities corresponding to the time periods for use in generating or executing a stress estimation model, and the use of high-level processors and other computing components to perform these steps merely digitizes and/or automates such otherwise-abstract functions (see MPEP 2106.05(f)). Additionally, the receipt of biological signals from at least one sensor of a wearable terminal amounts to insignificant extra-solution activity in the form of mere data gathering, because this element merely serves as a high-level means of obtaining the biological signal data needed for the main analysis steps of the invention (see MPEP 2106.05(g)). Accordingly, claims 1, 8, and 13 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-7 and 9-12 is also not integrated into a practical application under a similar analysis as above. Claims 2-7, 9-10, and 12 are performed with the same additional elements (e.g. high-level processor components) identified for the independent claims, without introducing any new additional elements of their own, and accordingly also amount to instructions to apply the abstract idea using these same additional elements. Claim 11 specifies that the estimation model is generated by machine learning, which also amounts to instructions to “apply” the exception because the otherwise-abstract function of using training data to fit a model is merely being implemented with high-level, unspecified “machine learning” methods such that this process is automated/digitized in an electronic environment.
Accordingly, the additional elements of claims 1-13 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-13 are directed to an abstract idea.
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, the additional elements of a processor or computer device and high-level machine learning for performing the receiving, determining, extracting, calculating, generating, training, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes Fig. 10 and paras. [0186]-[0190] of Applicant’s specification, where an example computer with known processing and memory elements is disclosed. Examiner notes that there is no disclosure of any particular type of machine learning model in the specification, leaving one of ordinary skill in the art to rely on their own knowledge of known types of machine learning models that may be used to implement the system.
Receipt of biological signals specifically from at least one sensor of a wearable terminal amounts to insignificant extra-solution activity in the form of mere data gathering, as explained above. Examiner also notes that it is well-understood, routine, and conventional to receive or transmit data over a network, as outlined in MPEP 2106.05(d)(II). Further, the use of a sensor of a wearable terminal to provide biological signal data for the purpose of stress estimation and analysis is well-understood, routine, and conventional, as evidenced by at least abstract & [0010] of Sasangohar et al. (US 20210233641 A1); abstract, Fig. 1, & [0136] of Yocca et al. (US 20220395222 A1); Fig. 2 & [0003] of Chadderdon et al. (US 20160089038 A1); and [0034] of Das et al. (US 20190175091 A1).
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer implementation, wearable sensor, and machine learning in combination is to digitize and/or automate the otherwise-abstract diagnostic analysis of stress-related data obtained via well-understood sensors. Thus, when considered as a whole and in combination, claims 1-13 are not patent eligible.
Claim Rejections - 35 USC § 102
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.
Claims 1-2 and 6-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chadderdon et al. (US 20160089038 A1).
Claims 1, 8, and 13
Chadderdon teaches an information processing apparatus, comprising: a memory storing instructions; and at least one processor, wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to (Chadderdon [0018]-[0020], noting hardware including processors executing instructions stored in memory to implement the functions of the invention):
receive, from at least one sensor of a wearable terminal, a plurality of biological signals of a subject (Chadderdon abstract, Figs. 1-2, [0028]-[0029], noting biological signals such as heart rate, skin conductance, skin temperature, and/or movement data are obtained by sensors of a wearable device and sent to the data processing components of the system for analysis);
determine a time zone of interest based on an index value of the plurality of biological signals exceeding a predetermined threshold for a predetermined time period (Chadderdon [0026]-[0027], [0032], noting the system determines whether each minute of collected data indicates a stress period (i.e. a time zone of interest) by determining whether biological signal values over each minute exceed pre-calibrated thresholds or baseline levels);
extract one or more feature quantities from the plurality of biological signals acquired in the time zone of interest (Chadderdon [0032], [0036], noting stress feature analyzer 210 extracts a set of stress features for each minute of collected data, which would include minutes with signals exceeding baseline thresholds for indicating stress (i.e. time zones of interest, as above));
calculate stress level data for the predetermined time period based on a user input (Chadderdon [0032]-[0033], noting stress classifier 220 derives a stress classifier score for each minute based on the user biological signals (i.e. based on a user input));
generate training data that associates the one or more extracted feature quantities as explanatory variables with the stress level data as objective variables; and train an estimation model based on the training data, wherein the estimation model is configured to output an estimation value indicating an estimated stress level based on one or more input feature quantities (Chadderdon [0032], [0050]-[0056], noting the stress classifiers are trained via supervised learning with training data that correlates extracted features (i.e. explanatory variables) with “ground truth” labels (i.e. objective variables) of relaxed or stressed periods; the trained classifiers output an estimated stress level (e.g. an integer value) based on inputting extracted minute-to-minute features).
Claims 8 and 13 recite substantially similar subject matter as claim 1, and are also rejected as above.
Claims 2 and 9
Chadderdon teaches the information processing apparatus according to claim 1, and further teaches wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to: extract a feature quantity based on a change in a biological signal at a start time or an end time of the time zone of interest (Chadderdon [0038], noting extraction of cardiovascular features can include starting at the beginning of the signal and finding peaks or derivatives (i.e. changes) in the signal over the time period, considered equivalent to extracting a feature quantity based on a change in a biological signal at a start time of the time zone of interest).
Claim 9 recites substantially similar subject matter as claim 2, and is also rejected as above.
Claim 6
Chadderdon teaches the information processing apparatus according to claim 1, and further teaches wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to:
determine, based on the plurality of biological signals, whether the subject is in a state of being exposed to an acute stress stimulus (Chadderdon [0023]-[0026], noting the system can determine when a stress triggering event is experienced (considered equivalent to the subject being in a state of exposure to an acute stress stimulus) based on analysis of the collected signals);
identify, as the time zone of interest for a female subject, a stress occurring time zone in which the subject has been determined to be in the state of being exposed to an acute stress stimulus (Chadderdon [0023]-[0027], [0032], noting the system determines whether each minute of collected data indicates a stress period such as a response to a stress triggering event (i.e. a time zone indicating that the user is exposed to an acute stress stimulus); Examiner notes that though Chadderdon does not specify the sex of users of the system, it contemplates the system being used to monitor any user for stress, which would include both males and females such that identifying a time zone of interest for any subject is considered to include identifying a time zone of interest for a female subject); and
extract, for the female subject, a feature quantity from a biological signal acquired in the stress occurring time zone which has been identified (Chadderdon [0032], [0036], noting stress feature analyzer 210 extracts a set of stress features for each minute of collected data, which would include minutes corresponding to stress triggering events for any type of subject, including females).
Claim 7
Chadderdon teaches the information processing apparatus according to claim 1, and further teaches wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to:
determine, based on the plurality of biological signals, whether the subject is in the state of being exposed to the acute stress stimulus (Chadderdon [0023]-[0026], [0051], noting the system can determine when a stress triggering event is experienced (considered equivalent to the subject being in a state of exposure to an acute stress stimulus) based on analysis of the collected signals);
identify, as the time zone of interest for a male subject, a time zone other than a stress occurring time zone in which the subject has been determined to be in a state of being exposed to an acute stress stimulus (Chadderdon [0023]-[0027], [0032], [0051], noting the system determines whether each minute of collected data indicates a stress period or a non-stress period, such that an identified period could correspond to a period of non-stress where a user is not determined to be exposed to an acute stress stimulus; Examiner notes that though Chadderdon does not specify the sex of users of the system, it contemplates the system being used to monitor any user for stress, which would include both males and females such that identifying a time zone of interest for any subject is considered to include identifying a time zone of interest for a male subject); and
extract, for the male subject, a feature quantity from a biological signal acquired in the time zone other than the stress occurring time zone (Chadderdon [0032], [0036], noting stress feature analyzer 210 extracts a set of stress features for each minute of collected data, which would include minutes corresponding non-stress for any type of subject, including males).
Claim 10
Chadderdon teaches a training data generation method, performed by at least one processor, comprising: generating training data for use in machine learning by associating, as correct answer data, a stress level of a subject with one or more feature quantities which have been extracted by the feature quantity extraction method recited in claim 8 (Chadderdon [0050], [0056], noting the stress classifiers are trained via supervised learning with training data that correlates extracted features with “ground truth” labels (i.e. correct answer data) of relaxed or stressed periods).
Claim 11
Chadderdon teaches an estimation model generation method, performed by at least one processor, comprising: generating an estimation model by machine learning using training data which has been generated by the training data generation method recited in claim 10 (Chadderdon [0050]-[0056], noting the stress classifiers are trained via supervised machine learning with the training data).
Claim 12
Chadderdon teaches a stress level estimation method, performed by at least one processor, comprising: estimating a stress level of a subject using an estimation model which has been generated by the estimation model generation method recited in claim 11 (Chadderdon [0032]-[0033], [0050]-[0051], noting the trained stress classifiers are used to output an estimated stress level (e.g. an integer value) of a user).
Claim Rejections - 35 USC § 103
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.
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 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.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Chadderdon as applied to claim 1 above, and further in view of Smets et al. (Reference U on the PTO-892 mailed 4/22/2025).
Claim 3
Chadderdon teaches the information processing apparatus according to claim 1, showing that measured biological signal data in time zones/minutes of interest are classified to estimate user stress level on a real-time and continuous basis (Chadderdon [0001]-[0003]), indicating that the time zones could coincide with any time throughout the day (e.g. before and after a time in the morning). Although this reference contemplates the identification/classification of time zones of interest at any time throughout the day, it does not describe any specific time zones, and thus fails to explicitly disclose wherein the instructions, when executed, cause the information processing apparatus to: identify, as the time zone of interest, predetermined time zones before and after a time in the morning at which a predetermined index value of a biological signal reaches a peak based on a circadian rhythm.
However, Smets teaches that physiological signals measured from subjects can correspond to circadian rhythms, and that subjects’ measured physiological signals stress levels can be higher upon consumption of caffeinated beverages or breakfast (i.e. times in the morning) (Smets paragraph spanning the end of Col 1 to the top of Col 2 on Pg 2, noting “Based on fixed day and night intervals, circadian rhythms of the physiological signals can be identified” as well as “consumption of caffeinated beverages or breakfast corresponded to higher stress levels”). It therefore would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, for the identified stress event periods of Chadderdon to specifically be associated with predetermined time zones before and after a time in the morning at which a predetermined index value of a biological signal (e.g. physiological signals indicating stress) reaches a peak based on a circadian rhythm, because Smets shows that activities associated with times in the morning (e.g. consumption of caffeine and/or breakfast) correspond to higher stress levels. The result of such a combination would be a system that can classify/identify stress time zones of interest at any time of the day as in Chadderdon that happens to identify high stress time zones at times in the morning when physiological signals and stress indicators are high as in Smets.
Claim 4
Chadderdon teaches the information processing apparatus according to claim 1, showing that measured biological signal data in time zones/minutes of interest are classified to estimate user stress level on a real-time and continuous basis (Chadderdon [0001]-[0003]), indicating that the time zones could coincide with any time throughout the day (e.g. a standard lunch time) as well as that data could be collected for patients of any sex. Although this reference contemplates the identification/classification of time zones of interest at any time throughout the day, it does not describe any specific time zones; similarly, though it contemplates collecting data for many users (including noting differences in user by gender as noted in [0003]), it does not describe the users as being specifically male or female. Accordingly, Chadderdon fails to explicitly disclose wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to: identify, as the time zone of interest for a male subject, a standard lunch time zone of the subject in one day; and extract, for the male subject, a feature quantity from a biological signal acquired in the lunch time zone which has been identified.
However, Smets teaches collecting physiological signals from both male and female subjects (Smets last paragraph at the bottom of Col 1 of Pg 6, noting “The trial was conducted with 1002 subjects (484 male, 451 female)”), and that subjects’ stress levels can be higher around lunch time of one day (Smets Fig. 1A on Pg 3, showing that markers of stress like mean heart rate are high in the middle of the day, i.e. a standard lunch time zone; see also Fig. 1B on Pg 3, showing an example of a single subject’s stress reports correlated to time throughout the day and consumption events like lunch. The data for Dec 16 and 18 specifically show higher sadness and stress reports before and after “lunch” consumption as denoted by the navy block in the “consumptions” track, and the data for Dec 17 shows higher sadness and stress reports in the middle of the day (i.e. corresponding to a standard lunch time zone)). It therefore would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, for the identified stress event periods of Chadderdon to specifically be associated with a standard lunch time zone for male subjects, because Smets shows that lunch consumption and/or mid-day time zones can correspond to higher stress levels, including in male subjects. The result of such a combination would be a system that can classify/identify stress time zones of interest at any time of the day for any type of patient and extract corresponding features for the high stress time zones as in Chadderdon, that happens to identify high stress time zones at mid-day / standard lunch times for patients who are male as in Smets.
Claim 5
Chadderdon teaches the information processing apparatus according to claim 1, showing that measured biological signal data in time zones/minutes of interest are classified to estimate user stress level on a real-time and continuous basis (Chadderdon [0001]-[0003]), indicating that the time zones could coincide with any time throughout the day (e.g. a standard lunch time) as well as that data could be collected for patients of any sex. Although this reference contemplates the identification/classification of time zones of interest at any time throughout the day, it does not describe any specific time zones; similarly, though it contemplates collecting data for many user (including noting differences in user by gender as noted in [0003]), it does not describe the users as being specifically male or female. Accordingly, Chadderdon fails to explicitly disclose wherein the instructions, when executed by the at least one processor, cause the information processing apparatus to: identify, as the time zone of interest for a female subject, a time zone other than a standard lunch time zone of the subject in one day; and extract, for the female subject, a feature quantity from a biological signal acquired in the time zone other than the lunch time zone.
However, Smets teaches collecting physiological signals from both male and female subjects (Smets last paragraph at the bottom of Col 1 of Pg 6, noting “The trial was conducted with 1002 subjects (484 male, 451 female)”), and that subjects’ measured physiological signals stress levels can be higher upon consumption of caffeinated beverages or breakfast (i.e. times other than a standard lunch time zone, since these events typically occur in the morning) (Smets paragraph spanning the end of Col 1 to the top of Col 2 on Pg 2, noting “consumption of caffeinated beverages or breakfast corresponded to higher stress levels”). It therefore would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, for the identified stress event periods of Chadderdon to specifically be associated with non-lunch time zones for female subjects, because Smets shows that activities associated with times in the morning (e.g. consumption of caffeine and/or breakfast) correspond to higher stress levels, including in female subjects. The result of such a combination would be a system that can classify/identify stress time zones of interest at any time of the day for any type of patient and extract corresponding features for the high stress time zones as in Chadderdon, that happens to identify high stress time zones at times in the morning for patients who are female as in Smets.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Liu et al. (US 20220054080 A1) and Inz et al. (US 20220165393 A1) describe systems for evaluating biological signals from wearable devices to detect stress periods and/or predict stress level for a user.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684