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 .
Response to Amendment
The amendment filed 01/02/26 has been entered. Amendments to claims 1-4, 19, and 20 are acknowledged. Claims 1-20 remain pending in the application. Applicant’s amendments to the Claims have overcome some of the 112(f) interpretation previously set forth in the Non-Final Office Action mailed 10/01/2025.
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.
Claim 19 recites the limitations:
“means for inputting, into a machine learning model”
“means for determining a condition to trigger an oxygen saturation measurement for a user associated with the wearable device”,
“means for receiving a measure of oxygen saturation”, and
“means for causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user.”
“means for inputting, into a machine learning model” will be interpreted as an antenna per para [0120]: “The input module 610 may utilize a single antenna or a set of multiple antennas.” of applicant’s specification.
“means for determining a condition to trigger an oxygen saturation measurement for a user associated with the wearable device” will be interpreted as a non-transitory computer readable medium per para [0169]: “non-transitory computer- readable medium described herein may further include operations, features, means, or instructions for selecting the condition to trigger the oxygen saturation measurement for the user associated with the wearable device” of applicant’s specification.
“means for causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user” will be interpreted as a user device per para [0117]: “the user device 106-e may cause a GUI of the user device 106-e to display an indication of the SpO2 measurements for the user” of applicant’s specification.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “inputting, into a machine learning model, a set of previous oxygen saturation measurements associated with a user, a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements, and a set of conditions associated with the set of previous oxygen saturation measurements”, “determining, in accordance with a machine learning model, a condition to trigger an oxygen saturation measurement for the user associated with the wearable device, based at least in part on inputting the set of previous oxygen saturation measurements, the respective measurement accuracy, and the set of conditions into the machine learning model, wherein the condition corresponds to a physical state of the wearable device, a physiological state of the user, or both, and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable device, application data, physiological data from the wearable device, or any combination thereof;”, “transmitting, via a first wireless communication link, a control signal to the wearable device to perform the oxygen saturation measurement for the user associated with the wearable device based at least in part on the condition being met”, “receiving, via a second wireless communication link, a measure of oxygen saturation associated with the user from the wearable device based at least in part on the condition and the transmitted control signal;”, and “causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “wearable device” , “first/second wireless communication link”, and “user device”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “Wearable devices are trending these days” of Chiu et al. (US 20230051939 A1), para [0025]: “wireless communication links operating according to any known communications protocol or standard,” of Nazarro et al. (US 20220203022 A1), and para [0003]: “distribution and use of portable devices, such as smart phones, become more common” of Kim et al. (US 20160109861 A1). Additionally regarding the “machine learning model”, a generic computer structure is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 2-18 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 1-18 is/are rejected under 35 U.S.C. 101.
Claim 19 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 19 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “inputting, into a machine learning model, a set of previous oxygen saturation measurements associated with a user, a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements, and a set of conditions associated with the set of previous oxygen saturation measurements”, “determining, in accordance with a machine learning model, a condition to trigger an oxygen saturation measurement for the user associated with the wearable device, based at least in part on inputting the set of previous oxygen saturation measurements, the respective measurement accuracy, and the set of conditions into the machine learning model, wherein the condition corresponds to a physical state of the wearable device, a physiological state of the user, or both, and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable device, application data, physiological data from the wearable device, or any combination thereof;”, “transmitting, via a first wireless communication link, a control signal to the wearable device to perform the oxygen saturation measurement for the user associated with the wearable device based at least in part on the condition being met”, “receiving, via a second wireless communication link, a measure of oxygen saturation associated with the user from the wearable device based at least in part on the condition and the transmitted control signal;”, and “causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “wearable device”, “non transitory storage medium” (see claim interpretation above), “first/second wireless communication link”, and “user device”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “Wearable devices are trending these days” of Chiu et al. (US 20230051939 A1), para [0025]: “wireless communication links operating according to any known communications protocol or standard,” of Nazarro et al. (US 20220203022 A1), and para [0003]: “distribution and use of portable devices, such as smart phones, become more common” of Kim et al. (US 20160109861 A1). Additionally regarding the “machine learning model” and “non-transitory computer storage medium”, a generic computer structure is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 19 fails to recite patent-eligible subject matter under 35 U.S.C. 101Thus, claim(s) 19 is/are rejected under 35 U.S.C. 101.
Claim 20 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 20 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “determine a condition to trigger an oxygen saturation measurement for a user associated with the wearable device, wherein the condition corresponds to a physical state of the wearable device, a physiological state of the user, or both, and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable device, application data, physiological data from the wearable device, or any combination thereof”, “receive a measure of oxygen saturation associated with the user from the wearable device based at least in part on the condition;”, and “cause a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “wearable device”, “processor”, “memory” , “first/second wireless communication link”, and “user device”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “Wearable devices are trending these days” of Chiu et al. (US 20230051939 A1), para [0025]: “wireless communication links operating according to any known communications protocol or standard,” of Nazarro et al. (US 20220203022 A1), and para [0003]: “distribution and use of portable devices, such as smart phones, become more common” of Kim et al. (US 20160109861 A1). Additionally regarding the “machine learning model”, “memory” and “processor”, a generic computer structure is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 20 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Thus, claim(s) 20 is/are rejected under 35 U.S.C. 101.
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.
Claim(s) 1-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Seo et al. (US 20230000353 A1), hereinafter Seo, in view of Smit et al. (US 20220104737 A1), hereinafter Smit.
Regarding claim 1, Seo discloses a method for performing oxygen saturation measurements from a wearable device (abstract, fig 5b), comprising: inputting, into a machine learning model ([0038]: “An artificial intelligence model may be generated by machine learning"), a set of previous oxygen saturation measurements associated with a user ([0124]: “an embodiment may identify (e.g., select, determine, or change) a specified measurement period and a specified reference range as an oxygen saturation measurement period and a reference oxygen saturation, based on the medical records related to oxygen saturation.”) and a set of conditions associated with the set of previous oxygen saturation measurements ([0080]: “the plurality of user states may include a daily life state, a state requiring an oxygen saturation check in relation to an oxygen saturation disease (e.g., upon entry into a hospital, upon receipt of a user input, or after discharge from a hospital (treatment completion)), a state in which oxygen saturation is equal to or less than a target value, hypoxia, rehabilitation in progress, exercising, and/or sleeping”); determining in accordance with an output of a machine learning model ([0104]: “the measurement module 426 may be implemented as an oxygen saturation measurement algorithm,” and [0038]: “The auxiliary processor 123 may control at least some of functions or states… instead of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or together with the main processor 121 while the main processor 121 is in an active state (e.g., executing an application).”, and [0038]: “the auxiliary processor 123 (e.g., the neural processing unit) may include a hardware structure specified for artificial intelligence model processing. An artificial intelligence model may be generated by machine learning.”, wherein the processor may be implemented as the machine learning model), a condition to trigger an oxygen saturation measurement for a user associated with the wearable device ([0083]: “may identify whether the user state is the state requiring an oxygen saturation check by identifying whether the medical records include information about a disease or treatment related to oxygen saturation.”) based at least in part on inputting the set of previous oxygen saturation measurements ([0124]) and the set of conditions into the machine learning model ([0080]), wherein the condition corresponds to a physical state of the wearable device, a physiological state of the user, or both ([0080]: “various user states related to a medical treatment state, a health state, or an activity of the user”), and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable device, application data, physiological data from the wearable device, or any combination thereof ([0045]: “The sensor module 176 may detect… an environmental state (e.g., a state of a user) external to the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state.”); transmitting, via a first wireless communication link ([0054]: “least one antenna appropriate for a communication scheme used in the communication network, such as the first network 198 or the second network 199, may be selected”), a control signal to the wearable device to perform the oxygen saturation measurement for the user associated with the wearable device based at least in part on the condition being met ([0167]: “processor (e.g., the processor 120) of the machine (e.g., the electronic device 101) may invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor”); receiving via a second wireless communication link ([0054]: “the second network 199, may be selected”), a measure of oxygen saturation associated with the user from the wearable device based at least in part on the condition and the transmitted control signal ([0011]: “measure oxygen saturation based on the specified oxygen saturation measurement period by using the biosensor, identify whether the measured oxygen saturation satisfies the specified reference oxygen saturation range,”); and causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user ([0011]: “and display the measured oxygen saturation and information indicating whether the measured oxygen saturation satisfies the specified reference oxygen saturation range on the display.”).
Seo fails to disclose inputting, into a machine learning model a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements.
Smit discloses inputting, into a machine learning model, a set of previous oxygen saturation measurements associated with a user, a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements ([0111]: “calibrate oxygen saturation prediction model 124 based on a history of accuracy of oxygen saturation prediction model 124.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Seo to include inputting a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements as disclosed by Smit in order to improve the accuracy of the machine learning model.
Regarding claim 2, Seo discloses the machine learning model comprises a mapping between the set of previous oxygen saturation measurements associated with the user (Table 1), the respective oxygcn saturation measurement accuracy associated with each of one or more previous oxygen saturation measurements measurement of the set of previous oxygen saturation measurements (as modified by Smit), and the set of conditions associated with the set of previous oxygen saturation measurements (Table 1).
Regarding claim 3, Seo discloses wherein the oxygen saturation measurement is performed in accordance with an oxygen saturation measurement cycle based at least in part on the condition (Table 1 measurement period)
Regarding claim 4, Seo discloses selecting the oxygen saturation measurement cycle from a set of oxygen saturation measurement cycles based at least in part on the machine learning model ([0008]: “a measurement period and a reference range of oxygen saturation may be identified based on the disease history of the user,”), the machine learning model comprising a mapping between one or more conditions of a set of conditions and one or more oxygen saturation measurement cycles of the set of oxygen saturation measurement cycles ([0097]: “identify a user history of an oxygen saturation-related disease and/or a user history of severity of the oxygen saturation-related disease in the medical records.”, [0124]: “measurement period and the second (or third) reference range) corresponding to a state (e.g., the existence of a history of an oxygen saturation-related disease and a severity history of the oxygen saturation-related disease)”, wherein the measurement period is mapped to the disease condition), wherein transmitting the control signal to the wearable device to perform the oxygen saturation measurement is based at least in part on the selected oxygen saturation measurement cycle ([0082]: " measure oxygen saturation every 10 minutes, and identify whether the measured oxygen saturation is about 95% or higher. When the user state is the state requiring an oxygen saturation check in relation to an oxygen saturation disease, the processor 320 according to an embodiment may identify (e.g., select, determine, or change) the second measurement period and the second reference range as the oxygen saturation measurement period and the reference oxygen saturation range, measure oxygen saturation every minute, and identify whether the measured oxygen saturation is about 93 to 95% or higher. In the state where oxygen saturation is equal to or less than a target value or in the case of hypoxia").
Regarding claim 5, Seo discloses adjusting the oxygen saturation measurement cycle from a default oxygen saturation measurement cycle based at least in part on the condition([0082]: " measure oxygen saturation every 10 minutes, and identify whether the measured oxygen saturation is about 95% or higher. When the user state is the state requiring an oxygen saturation check in relation to an oxygen saturation disease, the processor 320 according to an embodiment may identify (e.g., select, determine, or change) the second measurement period and the second reference range as the oxygen saturation measurement period and the reference oxygen saturation range, measure oxygen saturation every minute, and identify whether the measured oxygen saturation is about 93 to 95% or higher. In the state where oxygen saturation is equal to or less than a target value or in the case of hypoxia").
Regarding claim 6, Seo discloses receiving the sensor data from the wearable device associated with the user ([0075]: “motion sensor”), wherein the sensor data indicates a position value associated with the wearable device relative to an anatomical feature associated with the user ([0075]: “according to movement of the electronic device 301 or the user holding the electronic device 301”), the anatomical feature comprising a finger of the user ([0045]: “a grip sensor”), the position value being indicative of a physical locality of the wearable device on the anatomical feature associated with the user and corresponding to the physical state of the wearable device ([0075]: “The geomagnetic sensor may sense a geomagnetic direction. For example, the motion sensor 374-7 may identify whether the electronic device 301 or the user holding the electronic device 301 has moved by using the acceleration sensor and identify a motion of the user by using the gyro sensor during the movement. For example, the direction of the motion of the user may be further identified by using the geomagnetic senso”), wherein determining the condition comprises: determining that the position value associated with the wearable device relative to the anatomical feature associated with the user satisfies a threshold position value for a threshold duration ([0075]).
Regarding claim 7, Seo discloses receiving the sensor data from the wearable device associated with the user, wherein the sensor data indicates an orientation value associated with the wearable device relative to an anatomical feature associated with the user ([0075]: “The gyro sensor may sense a rotation direction or angle of the electronic device 301 according to movement of the electronic device 301 or the user holding the electronic device 301”), the anatomical feature comprising a finger of the user ([0075]: “holding”) , the orientation value corresponding to the physical state of the wearable device ([0075]), wherein determining the condition comprises: determining that the orientation value associated with the wearable device relative to the anatomical feature associated with the user satisfies a threshold orientation value for a threshold duration ([0075]).
Regarding claim 8, Seo discloses receiving the sensor data from the wearable device associated with the user, wherein the sensor data indicates a pressure value between the wearable device and an anatomical feature associated with the user ([0077]: “at least one second sensor 374 according to an embodiment may provide the processor 320 with various pieces of sensing information…a pressure value”), the anatomical feature comprising a finger of the user ([0077]: “the user holding the electronic device”), the pressure value being indicative of a force of the wearable device on the anatomical feature associated with the user and corresponding to the physical state of the wearable device, wherein determining the condition comprises: determining that the pressure value between the wearable device and the anatomical feature associated with the user satisfies a threshold pressure value for a threshold duration ([0075-0077], wherein the device determines if the user is in contact with the device).
Regarding claim 9, Seo discloses receiving the sensor data from the user device associated with the user, wherein the sensor data indicates at least one sound characteristic associated with the user ([0044]: “The audio module 170 may convert a sound into an electrical signal and vice versa. According to an embodiment, the audio module 170 may obtain the sound via the input module 150, or output the sound via the sound output module”), wherein determining the condition comprises: determining that the at least one sound characteristic associated with the user satisfies a threshold value ([0088]).
Regarding claim 10, Seo discloses receiving the application data, from the user device associated with the user, via one or more applications executable on the user device, the one or more applications executable on the user device comprising a lifestyle application, a social media application, a utility application, an information outlet application, or any combination thereof ([0038]: “or together with the main processor 121 while the main processor 121 is in an active state (e.g., executing an application”), and the application data is indicative of an activity the user is engaged in or a location of the user, or any combination thereof ([0073]: “may measure the location of the electronic device 101 or the user based on signals from satellites”), wherein determining the condition comprises: determining the activity the user is engaged in or the location of the user, or any combination thereof ([0073]: “may measure the location of the electronic device 101 or the user based on signals from satellites”).
Regarding claim 11, Seo further discloses receiving the physiological data associated with the user from the wearable device ([0082]), wherein determining the condition comprises: determining that one or more physiological parameters of the received physiological data satisfies a threshold ([0082]: “compare a result of the oxygen saturation measurement with the reference oxygen saturation range”).
Regarding claim 12, Seo further discloses wherein at least one physiological parameter of the physiological data comprises heart rate data associated with the user, the heart rate data corresponding to the physiological state of the user ([0069]: “and measure a heart rate (the number of heart beats”), the method further comprising: detecting an abnormal heart rate associated with the user based at least in part on the heart rate data ([0069]), wherein the abnormal heart rate corresponds to a heart rate associated with the user different from a range of heart rate values associated with the user, wherein determining the condition comprises: detecting the abnormal heart rate associated with the user ([0070]: “, an excited state value… based on the bio-signal measured by sensing the bio-signal of the user.”).
Regarding claim 13, Seo further discloses collecting the physiological data associated with the user from the wearable device based at least in part on a sleep state of the user, wherein determining the condition comprises: detecting the sleep state of the user ([0114]).
Regarding claim 14, Seo further discloses determining one or more breathing disturbances associated with the user during a duration associated with the sleep state of the user ([0117]: “, a sharp decrease in oxygen saturation during sleep may be a situation that requires a treatment for a respiratory disease.”) based at least in part on triggering the oxygen saturation measurement for the user during the duration associated with the sleep state of the user ([0117]: “320 may change the oxygen saturation measurement period to a shorter measurement period in the sleep state than in the daily life state and the reference oxygen saturation range to a lower reference range in the sleep state than in the daily life state”), wherein causing the graphical user interface to display the indication of the measure of the oxygen saturation comprises: causing the graphical user interface of the user device to display the one or more breathing disturbances for the user ([0117]: “transmit oxygen saturation information (or a request for a treatment) to an external server (e.g., a hospital) and output guidance information recommending a hospital visit through the display 360 or the speaker visually or audibly.”).
Regarding claim 15, Seo further discloses wherein causing the graphical user interface to display the indication of the measure of the oxygen saturation comprises: causing the graphical user interface of the user device to display an average breathing regularity during the duration associated with the sleep state of the user, a description of the average breathing regularity, a respective timestamp associated with each of the one or more breathing disturbances, or a magnitude associated with each of the one or more breathing disturbances, or any combination thereof ([0117]: “When the oxygen saturation is less than or equal to specified oxygen saturation during sleep, the processor 320 according to an embodiment may transmit oxygen saturation information (or a request for a treatment) to an external server (e.g., a hospital) and output guidance information recommending a hospital visit through the display 360 or the speaker visually or audibly.”).
Regarding claim 16, Seo further discloses receiving an input to trigger the oxygen saturation measurement for the user associated with the wearable device based at least in part on a user input or a setting executable via an application running on the user device ([0080]: “a state requiring an oxygen saturation check in relation to an oxygen saturation disease (e.g., upon entry into a hospital, upon receipt of a user input,”).
Regarding claim 17, Seo further discloses the wearable device comprises a wearable ring device (Fig 5a, wherein the watch is ring shaped).
Regarding claim 19, Seo discloses an apparatus for performing oxygen saturation measurements from a wearable device (abstract, fig 5b), comprising: means for inputting ([0036]: “an antenna module 197”), into a machine learning model ([0038]: “An artificial intelligence model may be generated by machine learning"), a set of previous oxygen saturation measurements associated with a user ([0124]: “an embodiment may identify (e.g., select, determine, or change) a specified measurement period and a specified reference range as an oxygen saturation measurement period and a reference oxygen saturation, based on the medical records related to oxygen saturation.”) and a set of conditions associated with the set of previous oxygen saturation measurements ([0080]: “identify a user state (e.g., a current user state)”); means for determining ([0167]: “implemented as software (e.g., the program 140) including one or more instructions that are stored in a storage medium”) in accordance with an output of a machine learning model ([0104]: “the measurement module 426 may be implemented as an oxygen saturation measurement algorithm,” and [0038]: “The auxiliary processor 123 may control at least some of functions or states… instead of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or together with the main processor 121 while the main processor 121 is in an active state (e.g., executing an application).”, and [0038]: “the auxiliary processor 123 (e.g., the neural processing unit) may include a hardware structure specified for artificial intelligence model processing. An artificial intelligence model may be generated by machine learning.”, wherein the processor may be implemented as the machine learning model), a condition to trigger an oxygen saturation measurement for a user associated with the wearable device ([0083]: “may identify whether the user state is the state requiring an oxygen saturation check by identifying whether the medical records include information about a disease or treatment related to oxygen saturation.”) based at least in part on inputting the set of previous oxygen saturation measurements ([0124) and the set of conditions into the machine learning model ([0080]), wherein the condition corresponds to a physical state of the wearable device, a physiological state of the user, or both ([0007]: “user states related to a medical treatment state, a health state, or an activity of the user”), and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable device, application data, physiological data from the wearable device, or any combination thereof ([0045]: “The sensor module 176 may detect… an environmental state (e.g., a state of a user) external to the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state.”); means for transmitting, via a first wireless communication link ([0054]: “least one antenna appropriate for a communication scheme used in the communication network, such as the first network 198 or the second network 199, may be selected”), a control signal to the wearable device to perform the oxygen saturation measurement for the user associated with the wearable device based at least in part on the condition being met ([0167]: “processor (e.g., the processor 120) of the machine (e.g., the electronic device 101) may invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor”); means for receiving via a second wireless communication link ([0054]: “the second network 199, may be selected”), a measure of oxygen saturation associated with the user from the wearable device based at least in part on the condition and the transmitted control signal ([0011]: “measure oxygen saturation based on the specified oxygen saturation measurement period by using the biosensor, identify whether the measured oxygen saturation satisfies the specified reference oxygen saturation range,”); and causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user ([0011]: “and display the measured oxygen saturation and information indicating whether the measured oxygen saturation satisfies the specified reference oxygen saturation range on the display.”).
Seo fails to disclose inputting, into a machine learning model a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements.
Smit discloses inputting, into a machine learning model, a set of previous oxygen saturation measurements associated with a user, a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements ([0111]: “calibrate oxygen saturation prediction model 124 based on a history of accuracy of oxygen saturation prediction model 124.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Seo to include inputting a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements as disclosed by Smit in order to improve the accuracy of the machine learning model.
Regarding claim 20, Seo discloses an apparatus for performing oxygen saturation measurements from a wearable device (abstract, fig 5b), comprising: a memory ([0039]: “The memory 130 may store various data used by at least one component (e.g., the processor 120 or the sensor module 176) of the electronic device 101.”); a processor coupled with the memory (Fig 3 elements 320 and 33) configured to; inputting, into a machine learning model ([0038]: “An artificial intelligence model may be generated by machine learning"), a set of previous oxygen saturation measurements associated with a user ([0124]: “an embodiment may identify (e.g., select, determine, or change) a specified measurement period and a specified reference range as an oxygen saturation measurement period and a reference oxygen saturation, based on the medical records related to oxygen saturation.”) and a set of conditions associated with the set of previous oxygen saturation measurements ([0080]: “identify a user state (e.g., a current user state)”); determining in accordance with an output of a machine learning model ([0104]: “the measurement module 426 may be implemented as an oxygen saturation measurement algorithm,” and [0038]: “The auxiliary processor 123 may control at least some of functions or states… instead of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or together with the main processor 121 while the main processor 121 is in an active state (e.g., executing an application).”, and [0038]: “the auxiliary processor 123 (e.g., the neural processing unit) may include a hardware structure specified for artificial intelligence model processing. An artificial intelligence model may be generated by machine learning.”, wherein the processor may be implemented as the machine learning model), a condition to trigger an oxygen saturation measurement for a user associated with the wearable device ([0083]: “may identify whether the user state is the state requiring an oxygen saturation check by identifying whether the medical records include information about a disease or treatment related to oxygen saturation.”) based at least in part on inputting the set of previous oxygen saturation measurements ([0124]) and the set of conditions into the machine learning model ([0080]) wherein the condition corresponds to a physical state of the wearable device, a physiological state of the user, or both ([0080]: “user states related to a medical treatment state, a health state, or an activity of the user”), and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable device, application data, physiological data from the wearable device, or any combination thereof ([0045]: “The sensor module 176 may detect… an environmental state (e.g., a state of a user) external to the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state.”); transmitting, via a first wireless communication link ([0054]: “least one antenna appropriate for a communication scheme used in the communication network, such as the first network 198 or the second network 199, may be selected”), a control signal to the wearable device to perform the oxygen saturation measurement for the user associated with the wearable device based at least in part on the condition being met ([0167]: “processor (e.g., the processor 120) of the machine (e.g., the electronic device 101) may invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor”); receiving via a second wireless communication link ([0054]: “the second network 199, may be selected”), a measure of oxygen saturation associated with the user from the wearable device based at least in part on the condition and the transmitted control signal ([0011]: “measure oxygen saturation based on the specified oxygen saturation measurement period by using the biosensor, identify whether the measured oxygen saturation satisfies the specified reference oxygen saturation range,”); and causing a graphical user interface of a user device to display an indication of the measure of the oxygen saturation for the user ([0011]: “and display the measured oxygen saturation and information indicating whether the measured oxygen saturation satisfies the specified reference oxygen saturation range on the display.”).
Seo fails to disclose inputting, into a machine learning model a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements.
Smit discloses inputting, into a machine learning model, a set of previous oxygen saturation measurements associated with a user, a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements ([0111]: “calibrate oxygen saturation prediction model 124 based on a history of accuracy of oxygen saturation prediction model 124.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Seo to include inputting a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements as disclosed by Smit in order to improve the accuracy of the machine learning model.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Smit in further view of Noh et al. (US 20170042433 A1), hereinafter Noh.
Regarding claim 18, Seo as modified by Smit discloses the method of claim 1 but fails to disclose determining a first timestamp of a previously performed oxygen saturation calibration operation associated with the user device, the wearable device, or both, wherein determining the condition comprises: determining that a duration between the first timestamp of the previously performed oxygen saturation calibration operation and a second timestamp associated with the determining of the condition to enable the oxygen saturation measurement satisfies a threshold.
Noh discloses a method including determining a first timestamp of a previously performed blood pressure calibration operation ([0030]: “predetermined amount of time elapsing since a last calibration”)associated with the user device, the wearable device, or both, wherein determining the condition comprises: determining that a duration between the first timestamp of the previously performed blood pressure calibration operation and a second timestamp associated with the determining of the condition to enable the blood pressure measurement satisfies a threshold ([0080]: “when a predetermined amount of time has elapsed since the previous calibration was performed, the blood pressure estimating apparatus determines to calibrate the blood pressure estimation model using the first calibration method. When a degradation of the signal quality of the biosignal is not large or when an insignificant amount of time has elapsed since the previous calibration was performed, the blood pressure estimating apparatus determines to calibrate the blood pressure estimation model using the second calibration method.”).
As Noh discloses that the measured biosignal may be a blood oxygen saturation ([0013]), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Seo to include the determining a first timestamp of a previously performed oxygen saturation calibration operation as disclosed by Noh in order to improve the accuracy of the measured biosignal (Noh [0045]: “compensate for a decrease in accuracy of the blood pressure estimation model due to a lapse of time or a change in an environment in which measurement is performed”).
Response to Arguments
Applicant's arguments filed with respect to the rejection of claims 1-20 under 35 U.S.C. § 101 have been fully considered but they are not persuasive. Communicating and receiving information is a mental process (see MPEP 2106.05 (g)). Furthermore, wireless communication links are well known in the art (see rejection above), and machine learning models are generic computer structures (see above). As such the rejection is maintained.
Applicant’s arguments, see pages 15-18 of applicant’s remarks, filed 01/02/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 103 and 35 U.S.C. § 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 (see above).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791