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 office action is in response to the claim amendments and remarks filed on January 28, 2026 for the application filed May 28, 2024 which claims priority to a foreign application filed on December 10, 2024. Claims 1, 3-8 and 10-15 are currently pending and have been examined.
Claim Objections
Claims 1, 8 and 15 objected to because of the following informalities: Claims 1, 8 and 15 recite “the alert” in the last limitation, which should recite “an alert”. 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, 3-8 and 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1 and 3-7 are directed towards a method (i.e. a process), which is a statutory category. Claims 8 and 10-14 are directed towards a device (i.e. a machine), which is a statutory category. Claim 15 is directed towards a non-transitory computer readable medium (i.e. a manufacture), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1, 8 and 15 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the representative claim 8 is identified as:
A stress estimating device comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute instructions to:
receive heart rate data measured by one or more sensors of a wearable device worn by a target person;
calculate a first stress value by inputting the heart rate data, to a first stress calculation model;
calculate a second stress value by inputting, to a second stress calculation model, the first stress value, and perceived stress scale (PSS) data indicating subjective stress of the target person acquired through a questionnaire administered to the target person,
wherein the second stress calculation model is generated in advance by learning using the first stress value and the PSS data about a predetermined person acquired in a past, and
wherein the second stress calculation model is further generated by training that uses, as explanatory variables, the first stress values computed from biometric data measured from the predetermined person and the PSS data previously obtained from the predetermined person, and uses, as a response variable, third data related to stress obtained when biometric data was measured from the predetermined person; and
in a case where the second stress value is greater than a predetermined threshold, transmit, over a network, an instruction to a display device operated by at least one of: the target person, a family member of the target person, and a manager of the target person's workplace to cause display of the alert on the display device.
The identified limitations of the abstract idea of claims 1, 8 and 15 fall within the subject matter grouping of mental processes. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. The steps of calculating stress values using models learned/trained in advance and determining whether to issue an alert based on comparing the stress values to thresholds can be performed in the human mind using observations, evaluations, judgments and opinions and therefore recite mental processes.
Accordingly, claims 1, 8 and 15 recite an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that 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. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in representative claim 8 are identified in italics as:
A stress estimating device comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute instructions to:
receive heart rate data measured by one or more sensors of a wearable device worn by a target person;
calculate a first stress value by inputting the heart rate data, to a first stress calculation model;
calculate a second stress value by inputting, to a second stress calculation model, the first stress value, and perceived stress scale (PSS) data indicating subjective stress of the target person acquired through a questionnaire administered to the target person,
wherein the second stress calculation model is generated in advance by learning using the first stress value and the PSS data about a predetermined person acquired in a past, and
wherein the second stress calculation model is further generated by training that uses, as explanatory variables, the first stress values computed from biometric data measured from the predetermined person and the PSS data previously obtained from the predetermined person, and uses, as a response variable, third data related to stress obtained when biometric data was measured from the predetermined person; and
in a case where the second stress value is greater than a predetermined threshold, transmit, over a network, an instruction to a display device operated by at least one of: the target person, a family member of the target person, and a manager of the target person's workplace to cause display of the alert on the display device.
The additional limitations of “stress device”, “…memory…”, “network”, “display device” and “… processor…” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The device, memory, processor, network and display device are recited at a high level of generality and used in their ordinary capacity to perform the abstract idea. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea.
The additional limitations of “receiving hear rate data..” and “transmit, over a network, an instruction to a display device operated by at least one of: the target person, a family member of the target person, and a manager of the target person's workplace to cause display of the alert on the display device” are determined to be no more than insignificant extra-solution activity to the judicial exception of mere necessary data gathering and data outputting under MPEP §2106.05(g).
Accordingly, claims 1, 8 and 15 do not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements 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 limitations to be mere instructions to apply an abstract idea under MPEP §2106.05(f) or mere necessary data gathering and data outputting under MPEP §2106.05(g), which does not amount to significantly more than the abstract idea. Evidence that data receiving necessary data and transmitting data over a network in well-understood, routine and conventional is provided by MPEP §2106.05(d), subsection II. and
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. Dependent claims 3-7 and 8-14 recite limitations which can be performed in the human mind using observations, evaluations, judgments and opinions and are therefore also recite mental processes. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea because they are directed to the abstract idea.
Therefore, whether taken individually or as an ordered combination, 1, 3-8 and 10-15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-8 and 10-15 re rejected under 35 U.S.C. 103 as being unpatentable over Oji et al. (JP2012075708A) in view of Imrak et al. (U.S. Pub. No. 2022/0130518)
Regarding claim 1, Oji discloses a stress estimation method comprising:
receiving Paragraph [0012], the stress state estimation device acquires the body motion data. Paragraph [0047], The body motion data measurement unit 11 is for measuring and collecting body motion data of a subject. The body motion data is time-series data representing the magnitude of the body motion. The body motion data measurement unit 11 may include, for example, an acceleration sensor, an angular velocity sensor, or a positioning device, and may be attached to one or a plurality of portions of the body of the subject to measure the activity (body motion) of the attached portion. The wearing portion is not particularly limited, but may be, for example, an arm portion, a waist portion, a leg portion, a bodily sensation, a head portion, or the like, and is particularly preferably a wrist.);
calculating a first stress value by inputting the Paragraph [0012], the stress state estimation device acquires the body motion data, calculates the intermediate data by extracting the feature amount (for example, the cumulative probability of the frequency of the rest time of the subject, the statistical amount such as the average value, the standard deviation, the skewness, the kurtosis, and the variance of the body motion data, or the like) from the acquired body motion data. Also see paragraphs [0112]-[0117].);
calculating a second stress value by inputting, to a second stress calculation model, the first stress value, and perceived stress scale (PSS) data indicating subjective stress of the target person acquired through a questionnaire administered to the target person (Paragraph [0050], The stress evaluation unit 110 estimates a subject's stress state based on data acquired from the body motion data measurement unit 11 and the subjective stress level input unit 12 and calculates a stress index. Paragraph [0012], estimates the stress state of the subject from the calculated intermediate data using the relationship data (for example, the coefficient of the regression line or the regression curve, the learning parameter of the machine learning device, or the like) representing the estimated correspondence relationship between the intermediate data and the stress state. Paragraph [0013], when the subjective stress state data is input to the input means, the stress state estimation device inductively obtains the correspondence relationship existing between the intermediate data and the subjective stress state data by using a set of the calculated intermediate data and the input subjective stress state data. For example, regression analysis, supervised machine learning, or the like can be used as a method of inductively obtaining the value. Paragraph [0049], The subjective stress level input unit 12 is for inputting the level of stress that the subject is aware of at an arbitrary timing. For example, a form is conceivable in which a display device such as an LCD and an input device such as a keyboard or a touch panel are provided, an input of the stress state that the subject is aware of is prompted by a display such as "Please input the psychological stress level that the subject is aware of now", and an input of a response from the subject is received, but the form is not limited thereto. In addition, regarding the input format of the subjective stress level data, it is possible to input an integer of 0 to 100 by setting 0 as a state of "not feeling stress at all" and setting 100 as a state of "feeling severe stress", but the input format is not limited thereto, and the degree of subjective stress may be input as two or more values. Also see paragraphs [0118]-[0125]. The relationship data is construed as the PSS data.),
wherein the second stress calculation model is generated in advance by learning using the first stress value and the PSS data about a predetermined person acquired in a past (Paragraph [0022], In the stress state estimation device, the update unit may include a machine learning device that performs supervised machine learning, the relationship data and the data representing the correspondence relationship may be learning parameters of the machine learning device, and the machine learning device may inductively obtain the data representing the correspondence relationship between the intermediate data and the subjective stress state data by performing supervised learning using the subjective stress state data as teacher data and the intermediate data as input data. Paragraph [0120], The learning parameter update calculation processing unit 28 acquires, from the learning parameter storage unit 48, a learning parameter (relational data) such as a weight coefficient that defines the internal state of the machine learning device.), and
wherein the second stress calculation model is further generated by training that uses, as explanatory variables, the first stress values computed from biometric data measured from the predetermined person and the PSS data previously obtained from the predetermined person, and uses, as a response variable, third data related to stress obtained when biometric data was measured from the predetermined person (Paragraph [0022], In the stress state estimation device, the update unit may include a machine learning device that performs supervised machine learning, the relationship data and the data representing the correspondence relationship may be learning parameters of the machine learning device, and the machine learning device may inductively obtain the data representing the correspondence relationship between the intermediate data and the subjective stress state data by performing supervised learning using the subjective stress state data as teacher data and the intermediate data as input data. Paragraph [0054], In addition, the subjective stress level data input to the subjective stress level input unit 12 may be denoted as self_data. In addition, the subjective stress level data input to the subjective stress level input unit 12 when the body motion data measurement unit 11 is measuring the (n + 1) the body motion data may be denoted as self_data [n]. Paragraph [0073], In the present embodiment, the "set of the subjective stress level data and the stress evaluation value data corresponding to the subjective stress level data" is, in detail, a set of the subjective stress level data and the stress evaluation value data corresponding to the timing at which the subjective stress level data is input, and the stress evaluation value data corresponding to an arbitrary timing indicates a stress evaluation value calculated from the body motion data measured in a predetermined period including the arbitrary timing. Self-data [n]/subjective stress data is construed as the third data.); and
Oji does not appear to explicitly disclose that the received body motion data includes heart rate data; or in a case where the second stress value is greater than a predetermined threshold, transmitting, over a network, an instruction to a display device operated by at least one of: the target person, a family member of the target person, and a manager of the target person's workplace to cause display of the alert on the display device.
Irmak teaches that it was old and well known in the art of mental health assessment at the time of the filing to calculate a stress value using received heart rate data inputted into a model (Irmak, paragraph [0047], Data may be obtained indirectly from a subject through devices (e.g., tablet or smart-phone) or sensors (e.g., heart-rate sensor. Paragraph [0050], In some cases, the data collected from the subject may comprise…biological data… Non-limiting examples of the biological data may comprise a heart rate. Paragraph [0055], In some cases, the heart rate or other cardiac measurements (e.g., pulse wave velocity) is collected to determine a mental health status of a subject.) An increased level of anxiety or stress can be predicted using data associated with the cardiac measurements.); and
in a case where the stress value is greater than a predetermined threshold, transmitting, over a network, an instruction to a display device operated by at least one of: the target person, a family member of the target person, and a manager of the target person's workplace to cause display of the alert on the display device (Irmak, paragraph [0044], At the operation 110, the method 100 may further comprise processing the collected data by the machine learning algorithm comprising determining a set of parameters related to a status of mental health of the subject. At an operation 115, the method 100 may comprise determining a status of mental health of the subject based at least in part on the processed data from the operation 110. At an operation 120, the method 100 may comprise providing a status of mental health of the subject to a recipient. The recipient may be the subject or a person other than the subject. Paragraph [0034], The recipient can be a family member or a friend of the subject, a helper, a medical professional, a medical provider, a hospital representative, or a health care clinic representative. Paragraph [0090], a threshold of a probability of a mental health status in a subject may comprise one or more numbers between 0 to 1 or a number between 0% to 100%. There may be more than one threshold in the output values that may indicate a probability of higher or lower severity of a mental health status in a subject. For example, an ML may predict a status of mental health of a subject to be at least a 50% probability indicating that there may be a need for an intervention as a result of a mental health status (e.g., a negative status, a perinatal depression, or anxiety). For example, a probability of less than 50% may indicate an absence of a mental health status in a subject. Paragraph [0110], The computer system 401 can include or be in communication with an electronic display 435 that comprises a user interface (UI) 440 for providing, for example, an inquiry, a questionnaire, a status of a mental health of a subject to a recipient or a subject. Also see paragraphs [0071] and [0089].) to improve an accuracy of a determination of a mental health status of the subject and provide early detection of medical disorders to warn subjects and prevent more sever disorders (Irmak, paragraphs [0040] and [0045]).
Therefore, it would have been obvious to one of ordinary skill in the art of mental health assessment at the time of the filing to modify the method of Oji such that the body movement data includes heart rate data and to include, in a case where the second stress value is greater than a predetermined threshold, transmitting, over a network, an instruction to a display device operated by at least one of: the target person, a family member of the target person, and a manager of the target person's workplace to cause display of the alert on the display device.
Regarding claim 3, Oji further discloses wherein the third data is data based on stress-related subjective data about the predetermined person (Paragraph [0022], In the stress state estimation device, the update unit may include a machine learning device that performs supervised machine learning, the relationship data and the data representing the correspondence relationship may be learning parameters of the machine learning device, and the machine learning device may inductively obtain the data representing the correspondence relationship between the intermediate data and the subjective stress state data by performing supervised learning using the subjective stress state data as teacher data and the intermediate data as input data.).
Regarding claim 4, Oji further discloses wherein the stress estimation method comprises acquiring the PSS data from the target person in advance (Paragraph [0078], That is, the stress index calculation parameter storage unit 45 stores the stress index calculation parameter which is set in advance or updated by the stress index calculation parameter update calculation processing unit 25, and the stress index calculation parameter is updated every time the subjective stress level data is input to the subjective stress level input unit 12. Also see paragraphs [0022] and [0077].),
the first stress value is calculated by inputting, to the first stress calculation model, the first data based on the biometric data acquired from the target person thereafter (Paragraph [0073], a set of the subjective stress level data and the stress evaluation value data corresponding to the timing at which the subjective stress level data is input, and the stress evaluation value data corresponding to an arbitrary timing indicates a stress evaluation value calculated from the body motion data measured in a predetermined period including the arbitrary timing. Also see paragraph [0022].), and
the second stress value is calculated by inputting, to the second stress calculation model, the calculated first stress value and the PSS data acquired in advance (Paragraph [0073], a set of the subjective stress level data and the stress evaluation value data corresponding to the timing at which the subjective stress level data is input, and the stress evaluation value data corresponding to an arbitrary timing indicates a stress evaluation value calculated from the body motion data measured in a predetermined period including the arbitrary timing. Also see paragraphs [0022] and [0077]-[0078].).
Regarding claim 5, Oji further discloses wherein: every time the biometric data is acquired from the target person, the first stress value is calculated by inputting, to the first stress calculation model, the first data based on the biometric data (Paragraphs [0014], [0039], [0073] and [0114]), and
every time the first stress value is calculated, the second stress value is calculated by inputting, to the second stress calculation model, the calculated first stress value and the PSS data acquired in advance (Paragraphs [0014], [0039], [0073] and [0114]).
Regarding claim 6, Oji further discloses wherein: at preset timings, the acquisition of the biometric data from the target person, and the calculation of the first stress value by inputting, to the first stress calculation model, the first data based on the acquired biometric data are repeatedly performed (Paragraphs [0039] and [0114]), and
every time the first stress value is calculated, the second stress value is calculated by inputting, to the second stress calculation model, the calculated first stress value and the second data acquired in advance, and information based on the calculated PSS stress value is output (Paragraphs [0014], [0039] and [0114]).
Regarding claim 7, Oji further discloses wherein the PSS data calculated on a basis of an answer from the target person to a preset question is acquired (Paragraphs [0022] and [0049]).
Claims 8 and 10-15 are rejected using the same rationale as claims 1 and 3-7. Also see paragraphs [0040] and [0170]-[0172].
Response to Arguments
Applicant's arguments filed January 28, 2026 regarding claims 1, 3-8 and 10-15 being rejected under 35 U.S.C. §101 fully considered but they are not persuasive.
Applicant argues that the claims amount to an improvement to stress estimation systems by providing better precision over prior approaches.
In response, while the stress estimation may provide better precision, this is an improvement to the abstract idea itself and not an improvement to technology, which is not enough to integrate the abstract idea into a practical application under step 2A, prong 2.
Applicant's arguments filed January 28, 2026 regarding claims 1, 3-8 and 10-15 being rejected under 35 U.S.C. §102 have been fully considered but they are moot in view of the new grounds of rejection.
Conclusion
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 Devin C. Hein whose telephone number is (303)297-4305. The examiner can normally be reached 9:00 AM - 5:00 PM M-F MDT.
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/DEVIN C HEIN/Examiner, Art Unit 3686
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