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 .
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.
Drawings
The drawings are objected to because element "905" is disclosed within the specification as a "block" but is represented within Figure 9 as line item that is not within a "block". Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The “means for” language will be interpreted as follows:
“Means for obtaining one or more measurements” recited in Claim 19 will be interpreted as using a control system to obtain one or more measurements.
“Means for determining at least one physiological characteristic” recited in Claim 19 will be interpreted as using a control system to determine at least one physiological characteristic.
“Means for predicting at least one secondary characteristic” recited in Claim 19 will be interpreted as using a control system to predict at least one secondary characteristic.
“Means for outputting the predicted at least one secondary characteristic” recited in Claim 19 will be interpreted as using a control system to output a predicted at least one secondary characteristic.
“Means for identifying a plurality of categories” recited in Claim 24 will be interpreted as using a neural network - machine learning - to identify a plurality of categories.
“Means for determining a correlation between the plurality of categories” recited in Claim 24 will be interpreted as using a neural network - machine learning - to determine a correlation between a plurality of categories.
“Means for generating the one or more machine learning models” recited in Claim 24 will be interpreted as using a neural network - machine learning - to generate one or more machine learning models.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of Claim 1 follows.
STEP 1
Regarding Claim 1, the claim recites a series of steps or acts, including obtaining one or more measurements from a target object; determining at least one physiological characteristic associated with the user; predicting at least one secondary characteristic associated with the user; and outputting the predicted at least one secondary characteristic associated with the user. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of determining at least one physiological characteristic associated with the user and predicting at least one secondary characteristic associated with the user set forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites outputting the predicted at least one secondary characteristic, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The outputting of the at least one secondary characteristic does not provide an improvement to the technological field, the method does not affect a particular treatment or effect a particular change based on the output, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, Claim 1 recites additional steps of obtaining one or more measurements from a target object. The obtaining step is recited at a high level of generality such that it amounts to insignificant pre-solution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as obtaining step do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding Claims 11 and 19, the claims recite a generic device configured to obtain one or more measurements from a target object of a user, determine at least one physiological characteristic associated with the user, predict at least one secondary characteristic associated with the user, and output a predicted at least one secondary characteristic associated with the user. Thus, the claim is directed to a machine, which is one of the statutory categories of invention. The functions of determining at least one physiological characteristic associated with the user, predicting at least one secondary characteristic associated with the user set forth a judicial exception. These functions describe a concept performed in the human mind (including an observation, evaluation, judgement, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. Additionally, the device recited in the claim is a generic device comprising generic components, such as “a control system”, configured to perform the abstract idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The same rationale applies to claim 25. The claim recites a computer system configured to perform the abstract idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Dependent Claims 2-10, 12-18, 20-24, and 26-30 fail to add something more to the abstract independent claims as they generally recite steps and/or functions pertaining to data gathering and processing. Regarding Claims 6-10, 16-18, 24, and 30 recites machine learning models that are recited at a high level of generality that they amount to generic computers and generic computer programs. The steps and/or functions of predicting at least one secondary characteristic, identifying a plurality of categories of users, determining a correlation between the plurality of categories of users, and generating one or more models are mere pre-solution data gathering.
The obtaining, determining, predicting, and outputting functions recited in the independent claims, Claims 1, 11, 19, and 25, maintain a high level of generality even when considered in combination with the dependent claims.
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.
Claims 1-2, 5, 11-12, 15, 19-20, 23, 25-26, and 28-29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goldberg et. al.'633 (U.S. Publication Number 20110245633).
Regarding Claim 1, Goldberg et. al.'633 discloses predicting a user characteristic using a category-based model (Paragraph [0038] - The wearable biosensor devices, systems, and methods of the invention are also useful in helping to predict the onset of a psychological episode (emphasis added); Paragraph [0076] - DBN learning algorithm incorporates prior knowledge into a suitable prior distribution over structures, which guides the search toward models that are physiologically relevant while also favoring simple models (emphasis added)), the method comprising:
obtaining, by a control system, one or more measurements from a target object of a user using one or more sensors (Paragraph [0050] - one or more sensors 1 for measuring one or more physiological parameters…a processor or microprocessor 3 for reading/analyzing the physiological data (emphasis added));
determining, by the control system, at least one physiological characteristic associated with the user based on the one or more measurements from the user (Paragraph [0032] - the wearable biosensor devices include one or more sensors, such as a galvanic skin response (GSR) sensor, a temperature sensor, a heart rate sensor, an oxygen saturation sensor, a blood pressure sensor, or a combination thereof; Paragraph [0050] - a processor or microprocessor 3 for reading/analyzing the physiological data detected by the one or more sensors (emphasis added));
predicting, by the control system, at least one secondary characteristic associated with the user, based on the at least one physiological characteristic associated with the user (Paragraph [0038] - The wearable biosensor devices, systems, and methods of the invention are also useful in helping to predict the onset of a psychological episode (emphasis added); Paragraph [0084] - the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (emphasis added)); and
outputting, by the control system, the predicted at least one secondary characteristic associated with the user (Paragraph [0008] - and an interface for displaying information concerning said psychological profile; Paragraph [0085] - The data and/or instructions generated by the on-board processor are can be translated into an alert or signal to the wearer via the LED display, to alert the wearer in real-time of a detected physiological and/or psychological state or condition (emphasis added)).
Regarding Claim 2, Goldberg et. al.'633 discloses the predicted at least one second characteristic associated with the user relates to a behavioral pattern of the user (Paragraph [0038] - The wearable biosensor devices, systems, and methods of the invention are also useful in helping to predict the onset of a psychological episode (emphasis added); Paragraph [0085] - to alert the wearer in real-time of a detected physiological and/or psychological state or condition (e.g., red LED=extremely stressed/anxious/agitated; yellow LED=warning, anxiety/agitation level rising; rising; green=normal/relaxed/baseline state) (emphasis added)).
Regarding Claim 5, Goldberg et. al.'633 discloses the at least one physiological characteristic associated with the user comprises a blood pressure of the user (Paragraph [0056] - The wearable biosensor devices may contain one or more sensors for gathering physiological data regarding heart rate (sympathetic and parasympathetic arousal)…blood pressure (emphasis added)).
Regarding Claims 11-12, 15, 19-20, and 23, the sections of Goldberg et. al.’633 cited above disclose an apparatus comprising the elements set forth in the claims.
Regarding claims 25-26, and 28-29, the sections of Goldberg et. al.’633 cited above inherently disclose a non-transitory computer-readable apparatus comprising a storage medium, the storage medium comprising instructions configured to execute the steps recited in the claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 13, 21, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et. al.'633 (U.S. Publication Number 20110245633) as applied to Claims 1, 11, 19, and 25 above, and further in view of Nam et. al.'551 (U.S. Publication Number 20230157551).
Regarding Claim 3, Goldberg et. al.'633 discloses the method outlined in Claim 1 above as well as the one or more sensors are configured to receive PPG signals (Paragraph [0056] - contain one or more sensors for gathering physiological data (emphasis added); Paragraph [0063] - the wearable biosensor devices include at least one photoplethysmography (PPG) for measuring).
Goldberg et. al.'633 fails to disclose one or more sensor configured to receive photoacoustic signals. Nam et. al.'551 teaches a set of photoacoustic signals (Paragraph [0044] - When the light emitted from the light source 110 reaches the object, some part of the light is absorbed inside the living tissues and converted into photoacoustic signals and returns back in the form of ultrasound, and some other part of the light is reflected and returns back in the form of light. At this time, the returning ultrasound is measured by the ultrasonic transducer 120, and the reflected light is detected by the detector 130. In this case, there may be only a very short time delay between the photoacoustic signal detection by the ultrasonic transducer 120 and the PPG signal measurement by the detector 130, so that the two signals are naturally synchronized with each other. By measuring the synchronized photoacoustic signal and PPG signal, it is possible to identify changes associated with hemodynamics of the living body with a high spatial and temporal resolution (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 to include a device with photoacoustic photoplethysmography in order to observe hemodynamic changes in blood vessels at a high spatial and temporal resolution as seen in Nam et. al.'551.
Regarding Claims 13, 21, and 27, the sections of Goldberg et. al.’633 as modified by Nam et. al.’551, disclose an apparatus comprising the elements set forth in the claim.
Claims 4, 14, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et. al.'633 (U.S. Publication Number 20110245633) as applied to Claim 1 above, in view of Frank et. al.’322 (U.S. Publication Number 20210280322).
Regarding Claim 4, Goldberg et. al.'633 discloses the method outlined in Claim 1 above as well as the target object of the user comprises a blood vessel of the user (Paragraph [0035] - the wearable biosensor devices are preferably adapted for wearing around a wrist (e.g., watch, a bracelet), an ankle (e.g., an ankle cuff), a finger (e.g., a ring); Paragraph [0056] - blood pressure, hydration level, muscle pressure, activity level, body position, and/or optical reflectance of blood vessels (emphasis added)). Goldberg et. al.’633 fails to disclose the at least one physiological characteristic associated with the user comprises a strain of the blood vessel, a stress of the blood vessel, a distension of the blood vessel, a stiffness of the blood vessel, a compliance of the blood vessel, a dimension of the blood vessel, or a combination thereof. Frank et. al.’322 teaches observing changes in physiological characteristics such as compliance of a blood vessel and dimensions of a blood vessel as a result of secondary characteristics experienced by a user (Paragraph [0632] - blood propagating through the arteries, and therefore also the blood pressure calculated based on that value, is affected by multiple factors, such as the cardiac output, the vessel compliance, vessel diameter, vessel length, and blood viscosity…the diameter of the arteries…can change in certain circumstances, such as a result of stress (e.g., due to the release of stress hormones)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.’633 to include measuring physiological characteristics such as compliance of a blood vessel or dimensions of a blood vessel in order to better understand a correlation between a user’s secondary characteristic its effect on a user’s physiological characteristic as seen in Frank et. al.’322 (Paragraph [0623] - different arteries at different locations have different properties… account for these factors and increase accuracy of blood pressure calculations).
Regarding Claims 14 and 22, the sections of Goldberg et. al.’633 cited above disclose an apparatus comprising the elements set forth in the claims.
Claims 6, 8, 16, 18, 24, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et. al.'633 (U.S. Publication Number 20110245633) as applied to Claims 1, 11, 19, and 25 above, and further in view of Heneghan et. al.'466 (U.S. Patent 11191466).
Regarding Claim 6, Goldberg et. al.'633 discloses the method outlined in Claim 1 above as well as the predicting of the at least one secondary characteristic associated with the user comprises using one or more machine learning models implemented by the control system (Paragraph [0038] - The wearable biosensor devices, systems, and methods of the invention are also useful in helping to predict the onset of a psychological episode (emphasis added); Paragraph [0084] - In certain embodiments, the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (on-board data file) (emphasis added)), the one or more machine learning models obtained by:
identifying a plurality of machine learning models used for comparative functions based on physiological characteristics determined from a training set of users and sensor signals relating to a target object of the users (Paragraph [0013] - Deliveries from this library can be driven by the physiological parameters and the individual's psychological profile, or some combination thereof, and based on…wearer demographics; Paragraph [0024] - Variations are indicated from the individual's baseline or from sample- or population-level estimates (emphasis added); Paragraph [0051] - the digital library can be contained on-board the wearable biosensor device, such that the wearable biosensor device is an all-in-one monitoring and treatment system capable of detecting a physiological parameter, deriving data indicative of a psychological state based on the detected physiological parameter (emphasis added); Paragraph [0076] - DBN learning algorithm incorporates prior knowledge into a suitable prior distribution over structures, which guides the search toward models that are physiologically relevant while also favoring simple models (emphasis added));
determining a correlation between the plurality of categories of users and one or more secondary characteristics associated with the users (Paragraph [0074] - In exemplary embodiments of the invention, machine learning with Dynamic Bayesian Networks (DBNs) is employed to better recognize patterns in physiological, affective, and contextual data…individual subjects have varying physiology. DBNs are well suited to devising hierarchical models (where data is organized into branching patterns that describe one-to-many relationships) that allow the prediction of physiological changes of an individual person (emphasis added); Paragraph [0084] - In certain embodiments, the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (on-board data file) (emphasis added)); and
generating the one or more machine learning models based on the determined correlation (Paragraph [0084] – the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (on-board data file) (emphasis added); Paragraph [0096] - The personalized profile is employed to analyze physiological data in real time, on the wearable device and/or on associated devices, in order to identify patterns, and events and thresholds that indicate the need for therapeutic intervention (emphasis added)).
Goldberg et. al.'633 further fails to disclose identifying a plurality of categories of users based on physiological characteristics. Heneghan et. al.'466 teaches identifying a plurality of categories of users based on physiological characteristics (Column 4 Lines 30-36 - In particular, analysis of how much variation there is within the various categories of inputs will prove helpful in performing the mental health and cognitive screening herein. This information can then be used to update predictive models, as well as to update individual event predictions based at least in part upon the current values of those metrics for the user (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 to include a device that identifies a plurality of categories relating to physiological parameters as part of the machine learning process in order to determine the levels of variation between an individual user and each identified category to aid in screening for mental health issues as seen in Heneghan et. al.'466.
Regarding Claims 16, 24, and 30, the sections of Goldberg et. al.’633 as modified by Heneghan et. al.'466, disclose an apparatus comprising the elements set forth in the claim.
Regarding Claim 8, Goldberg et. al.'633 discloses the method outlined in Claim 8 above as well as determining of the correlation between the plurality of users and the one or more secondary characteristics associated with the users comprises an unsupervised learning process (Paragraph [0013] - Deliveries from this library can be driven by the physiological parameters and the individual's psychological profile, or some combination thereof, and based on…wearer demographics; Paragraph [0024] - Variations are indicated from the individual's baseline or from sample- or population-level estimates (emphasis added); Paragraph [0051] - the digital library can be contained on-board the wearable biosensor device, such that the wearable biosensor device is an all-in-one monitoring and treatment system capable of detecting a physiological parameter, deriving data indicative of a psychological state based on the detected physiological parameter (emphasis added); Paragraph [0076] - DBN learning algorithm incorporates prior knowledge into a suitable prior distribution over structures, which guides the search toward models that are physiologically relevant while also favoring simple models (emphasis added)), but fails to disclose a correlation between the plurality of categories of users and the one or more secondary characteristics. Heneghan et. al.'466 teaches determining a correlation between a plurality of categories and mental health statuses of a user (Column 4 Lines 30-36 - In particular, analysis of how much variation there is within the various categories of inputs will prove helpful in performing the mental health and cognitive screening herein. This information can then be used to update predictive models, as well as to update individual event predictions based at least in part upon the current values of those metrics for the user (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 to include a device that determines a correlation between a plurality of categories of users and mental health statuses of users as part of the machine learning process in order to determine the levels of variation between an individual user and each identified category to aid in screening for mental health issues as seen in Heneghan et. al.'466.
Regarding Claim 18, the sections of Goldberg et. al.’633 as modified by Heneghan et. al.'466, disclose an apparatus comprising the elements set forth in the claim.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et. al.'633 (U.S. Publication Number 20110245633) in view of Heneghan et. al.'466 (U.S. Patent 11191466) as applied to Claims 6 and 16 above, and further in view of Nam et. al.'551 (U.S. Publication Number 20230157551).
Regarding Claim 7, Goldberg et. al.'633 discloses the method outlined in Claim 6 above as well as the sensor signals of the training set comprise a training set of PPG signals (Paragraph [0063] - the wearable biosensor devices include at least one photoplethysmography (PPG) for measuring; Paragraph [0075] - This invention may be implemented in such a way that a pattern recognition algorithm incorporates prior knowledge (in addition to training data) (emphasis added)); and
determining of the correlation comprises determining a correlation between the users and an unknown secondary characteristic associated with the users (Paragraph [0013] - Deliveries from this library can be driven by the physiological parameters and the individual's psychological profile, or some combination thereof, and based on…wearer demographics; Paragraph [0024] - Variations are indicated from the individual's baseline or from sample- or population-level estimates (emphasis added); Paragraph [0051] - the digital library can be contained on-board the wearable biosensor device, such that the wearable biosensor device is an all-in-one monitoring and treatment system capable of detecting a physiological parameter, deriving data indicative of a psychological state based on the detected physiological parameter (emphasis added); Paragraph [0076] - DBN learning algorithm incorporates prior knowledge into a suitable prior distribution over structures, which guides the search toward models that are physiologically relevant while also favoring simple models (emphasis added)).
Goldberg et. al.'633 in view of Heneghan et. al.’466 discloses the method outlined in Claim 6 above, but fails to disclose determining a correlation between the plurality of categories of users and an unknown secondary characteristic. Heneghan et. al.'466 teaches determining a correlation between a plurality of categories and mental health statuses of a user ([Column 4 Lines 30-36] - In particular, analysis of how much variation there is within the various categories of inputs will prove helpful in performing the mental health and cognitive screening herein. This information can then be used to update predictive models, as well as to update individual event predictions based at least in part upon the current values of those metrics for the user (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 to include a device that determines a correlation between a plurality of categories of users and mental health statuses of users as part of the machine learning process in order to determine the levels of variation between an individual user and each identified category to aid in screening for mental health issues as seen in Heneghan et. al.'466.
Goldberg et. al.'633 in view of Heneghan et. al.’466 discloses the method outlined in Claim 6 above, but fails to disclose a training set of photoacoustic signals. Nam et. al.'551 teaches a set of photoacoustic signals (Paragraph [0044] - When the light emitted from the light source 110 reaches the object, some part of the light is absorbed inside the living tissues and converted into photoacoustic signals and returns back in the form of ultrasound, and some other part of the light is reflected and returns back in the form of light. At this time, the returning ultrasound is measured by the ultrasonic transducer 120, and the reflected light is detected by the detector 130. In this case, there may be only a very short time delay between the photoacoustic signal detection by the ultrasonic transducer 120 and the PPG signal measurement by the detector 130, so that the two signals are naturally synchronized with each other. By measuring the synchronized photoacoustic signal and PPG signal, it is possible to identify changes associated with hemodynamics of the living body with a high spatial and temporal resolution (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 in view of Heneghan et. al.’466 to include a device with a photoacoustic photoplethysmography in order to observe hemodynamic changes in blood vessels at a high spatial and temporal resolution as seen in Nam et. al.'551.
Regarding Claim 17, the sections of Goldberg et. al.’633 as modified by Heneghan et. al.'466 and further as modified by Nam et. al.’551, disclose an apparatus comprising the elements set forth in the claim.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et. al.'633 (U.S. Publication Number 20110245633) in view of Heneghan et. al.'466 (U.S. Patent 11191466) as applied to Claim 6, and further in view of Basu et. al.'600 (U.S. Publication Number 20180116600).
Regarding Claim 9, Goldberg et. al.'633 discloses the method outlined in Claim 6 above as well as the one or more machine learning models comprise at least a first model (Paragraph [0084]) and a second model that correlate to respective ones (Paragraph [0068]);
the first model is configured to predict a first secondary characteristic associated with the user based on the at least one physiological characteristic associated with the user (Paragraph [0084] - In certain embodiments, the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (on-board data file) (emphasis added)); and
the second model is configured to predict a second secondary characteristic associated with the user (Paragraph [0068] - The wearable biosensor devices may further include a global positioning system to provide information regarding the location of an individual wearing the biosensor device. Such information may be informative of trigger factors or cues that induce or contribute to change in physiological response detected by the one or more sensors in the wearable biosensor device (emphasis added)).
Goldberg et. al.'633 in view of Heneghan et. al.’466 discloses the method outlined in Claim 6 above, but fails to disclose models that correlate to a plurality of categories. Heneghan et. al.'466 teaches correlating machine learning models to a plurality of categories of users ([Column 4 Lines 30-36] - In particular, analysis of how much variation there is within the various categories of inputs will prove helpful in performing the mental health and cognitive screening herein. This information can then be used to update predictive models, as well as to update individual event predictions based at least in part upon the current values of those metrics for the user (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 to include a device that identifies a plurality of categories of users as part of the machine learning process in order to determine the levels of variation between an individual user and each identified category to aid in screening for mental health issues as seen in Heneghan et. al.'466.
Goldberg et. al.'633 in view of Heneghan et. al.’466 discloses the method outlined in Claim 6 above, but fails to disclose predict a second secondary characteristic associated with the user based on the at least one physiological characteristic associated with the user. Basu et. al.'600 teaches using location of a user and blood pressure to understand physical activity, stress, and alertness (Paragraph [0041] - Blood pressure can vary significantly based on location, due both to the physical activity required to travel between locations and changes in stress or alertness associated with changes in location. The wearable sensing device 200 may be configured to detect the location of the patient, for example using a GPS receiver 240. The machine learning model 40 may use the location of the patient as an input (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 in view of Heneghan et. al.’466 to include a device that models GPS location in order to account for the relationship between stress and a user's location as seen in Basu et. al.'600.
Regarding Claim 10, Goldberg et. al.'633 in view of Heneghan et. al.'466 and further in view of Basu et. al.’600 discloses the method outlined in Claim 9 above, but fails to disclose the one or more machine learning models comprise an ensemble model configured to predict the at least one secondary characteristic associated with the user using both the first model and the second model. Heneghan et. al.'466 teaches coupling machine learning models to predict secondary characteristics associated with a user (Column 15 Lines 57-61 - inputting 308 the physiological data into one or more predictive models to identify 310 whether the physiological data, including any pattern found therein, correlates with one or more biomarkers relevant to mental state (emphasis added); Column 16 Lines 22-31 - The predictive models can update pattern information based on additional data to obtain more accurate pattern information. In some embodiments, the state data may be weighted or decayed such that recent physiological data has more of an impact on pattern determination to account for changes in the health of the user, such as changes in age, hormone levels, and the like. While current information can be sufficient to form a screening or initial analysis, the predictive models will become more accurate as additional information is received and analyzed (emphasis added)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Goldberg et. al.'633 to include a device that inputs multiple models into a machine learning model as way to predict a secondary characteristic of a user in order to account for as much information as possible and therefore be more accurate on account that more information is analyzed as seen in Heneghan et. al.'466.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kitchens et. al.'258 (U.S. Publication Number 20220175258) discloses a device comprising photoacoustic plethysmography (PAPG) methods when obtaining measurements from blood vessels. Stewart'180 (U.S. Publication Number 20240123180) discloses a wearable device utilizing machine learning algorithms to connect physiological parameters to a user's mental health status.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791