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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/06/2026 has been entered.
Response to Arguments
Applicant’s arguments, see “Applicant Arguments/Remarks”, filed 03/06/2026, with respect to objections to the claims, have been fully considered and are persuasive. The objections to the claims have been withdrawn.
Applicant’s arguments, see “Applicant Arguments/Remarks”, filed 03/06/2026, with respect to the rejection under U.S.C. 103 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 Therefore, the rejection has been withdrawn.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 Yang and Eom.
Applicant's arguments filed 03/06/2026 regarding the rejections under U.S.C. 112(a) and 101 have been fully considered but they are not persuasive.
Arguments to U.S.C. 112(a):
Applicant argues that their claims provide the necessary written description as their claims do not require the construction of a learning model, but the usage of a learning model, and that one of ordinary skill would be aware of what that comprises. This argument is unpersuasive. While one of ordinary skill could construct any learning model that has inputs and outputs a triglyceride level of a user, one of ordinary skill would not be aware of what Applicant’s learning model is/comprises. One of ordinary skill would not be aware of what weight Applicant is applying to the various types of user information listed in Para. 0039, and how those values are expected to map with the input bio-resistance value. This is further compounded in Applicant’s cited Para. 0042, as the further training is not disclosed in any specific manner that would allow one of ordinary skill to also further train the learning models to provide a personalized model for each user, though the Examiner notes these limitations are not currently found in the claims. As such, one of ordinary skill would not be aware that Applicant had possession of said learning model at the time of filing, and thus Applicant’s disclosure does not provide the requisite written description.
Applicant’s arguments to U.S.C. 101:
Applicant argues that the usage of the newly amended imaging system provides a practical application for the claims as it improves the accuracy of the measurement of the triglyceride levels. This argument is unpersuasive. This addition is merely a routine and conventional post-solution activity, as elaborated further in the rejection under U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Regarding Claims 1, 10, and 17, Applicant’s disclosure does not describe the usage of the learning model in a way that one of ordinary skill would be apprised of how the learning model worked. While the Specification/Claims describe certain inputs and outputs, one of ordinary skill would not be aware of how those inputs were organized to provide the outputs, or how any of the training steps occurred. This is further shown in Claims 6 and 14, as the limitation “wherein the learning model comprises a linear and/or non-linear machine learning mapping function” fails to provide the necessary steps as to how the learning model functions. Any algorithm or computer model claimed must be described in sufficient detail so one of ordinary skill in the art is capable of using the invention. The Applicant’s Specification and Claims merely define the invention in functional language that states the desired result, but does not describe in detail as to how the result is achieved with the given inputs (see MPEP 2161.01).
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 1-20 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Step 1: Independent Claims 1, 10, and 17 recite an apparatus, method, and electronic device. Thus, they are directed to statutory categories of invention.
Step 2: Independent Claims 1, 10, and 17 recite the following claim limitations:
to extract a bio-resistance value in a predetermined frequency band from the measured bio-impedance
input user information and the extracted bio-resistance value to a learning model
measure a triglyceride level based on an output value of the learning model
wherein the learning model is pre-trained to output a triglyceride level based on an input of a bio-resistance value of a user and user information.
These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind, i.e. using pen and paper. A person reasonably could receive measurements, extract values from the measurements, input the information into a learning model, and use the output to determine a triglyceride level only using their mind/pen and paper. Thus, the claims recite limitations which fall within the ‘mental processes’ grouping of abstract ideas.
Step 2A, Prong 2:
Claims 1, 10, and 17 recite the following limitations:
an impedance sensor configured to measure bio-impedance comprising two electrodes on the side surface of the main body and spaced apart from each other
a device/apparatus wearable on the wrist and comprising a display and the aforementioned impedance sensor
the display configured to display a first image indicating a position of the impedance sensor to which the object of the user is to be placed to measure the bio-impedance, and a second image pointing a direction toward the impedance sensor
Claims 1 and 17 recite the following limitations:
a processor
Claim 17 recites the following limitation:
a memory
Electronically receiving a plurality of measurements over an unspecified time span is merely insignificant pre-solution activity (See MPEP 2106.05(g)).
The recitation of the processors, wearable device/apparatus, display, and memory are merely reciting the computer components at a high-level of generality, merely used to carry out the steps of the method. In other words, the computer components are being used as a tool to carry out the method (See MPEP 2106.05(f)).
The impedance sensor is recited at a high level of generality and are merely used in their intended manner to gather data. The sensor is being used as a tool to carry out the data acquisition. Even when viewed as a whole in combination with the other elements, the sensor fails to add significantly more to the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
For the bioimpedance device, the usage of an electrode system to obtain bioimpedance information is merely insignificant pre-solution activity, and is further well-understood, routine and conventional as shown in US20180049696 to Eom (Para. 0046) and US10100323468 to Yang (abstract).
For the display steps, using a display to provide image-based positioning guidance to improve accuracy in the measurement is well-understood, routine, and conventional in the art and merely post-solution activity. US20180049696 to Eom teaches this in Fig. 8b, Para. 0081, and US20160106337 to Jung in Para. 0053 as examples of said guidance in bioimpedance devices.
For the "electrically receiving..." step that was considered insignificant extra-solution activity in Step 2A Prong Two, it has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The following evidence supports such a determination:
electronically receiving a plurality of measurements of physiological variables for a patient, the plurality of measurements being acquired over a time span
(See MPEP 2106.05(d) II. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
For these reasons, there is no inventive concept. The claim is not patent eligible. Even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent Claims:
Claims 2-3, 5, 9, 11-12, and 16 merely recite limitations that further specify information used to input information into the learning model, none of the limitations providing information or complexity that would provide significantly more to the abstract idea.
Claims 6, 14, and 20 merely recite further electronic components that are recited at a high level of generality, and therefore do not provide significantly more to the abstract idea.
Claims 7-8, 15, and 18 merely recite limitations directed to basic alerts/prompts to the user of the device. This is insignificant post-solution activity that is also determined to be well-understood, routine and conventional (See MPEP 2106.05(d) II. OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Even when viewed as a whole in combination with the independent claims, the aforementioned dependent claims fail to add significantly more to the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20200323468 awarded to Yang et al, in view of U.S. Patent Publication 20180049696 awarded to Eom et al.
Regarding Claims 1, 10, and 17, Yang teaches an apparatus, electronic device, and method for measuring a triglyceride level (Para. 0012), the apparatus comprising: a main body to be worn on the wrist of a user (Fig. 7, main body 710, Para. 0036) an impedance sensor configured to measure bio-impedance of a user (Para. 0043, “The impedance measurement device 120 may apply a current to the first electrode 111 and the second electrode 112, and may measure bio-impedance by measuring a voltage applied to the first electrode 111 and the second electrode 112”); and a processor configured to extract a bio-resistance value (Para. 0058, “For example, the equivalent circuit analyzer 210 may obtain parameters, related to the physical properties of the fluid, from elements constituting a circuit 44 of the remaining blood, which is obtained by eliminating the already known parasitic capacitance (Cs) and the polarization effect (CPEe) from the modeled equivalent circuit 43. For example, the equivalent circuit analyzer 210 may extract, as parameters, plasma capacitance (Cp), plasma resistance (Rp), cytoplasm resistance (Ri), an amplitude of the CPE of a cell membrane (CPEm) (denoted by C in the above Equation 1), a characteristic value (denoted by a in the above Equation 1), and the like”) in a predetermined frequency band from the measured bio-impedance (Para. 0044, “The impedance measurement device 120 may obtain impedance spectrum data by measuring a plurality of impedances based on changing frequencies of the input current within a predetermined frequency range (e.g., a frequency range of 1 kilohertz (kHz) to hundreds of megahertz (MHz))”), configured to input user information and the extracted bio-resistance value to a learning model (Para. 0080, “Further, based on modeling the equivalent circuit, the apparatuses 1 and 5 for analyzing an in vivo component may extract one or more parameters from the equivalent circuit. For example, as the parasitic component parameter or the polarization effect parameter in the equivalent circuit of all the sensors is already known, the apparatuses 1 and 5 for analyzing an in vivo component may extract parameters, related to the physical properties of pure blood, from the rest of the equivalent circuit elements. For example, the parameters may include plasma capacitance, plasma resistance, cytoplasm resistance, and values indicative of an amplitude or a slope of the CPE of a cell membrane, and the like”), and configured to measure a triglyceride level based on an output value of the learning model, wherein the model is pre-trained to output a triglyceride level based on an input of a bio-resistance value of a user and user-information (Para. 0081, “The apparatuses 1 and 5 for analyzing an in vivo component may analyze an in vivo component based on the modeling result in operation 640. The apparatuses 1 and 5 for analyzing an in vivo component may obtain an estimated in vivo component value, such as blood glucose, cholesterol, triglyceride, protein, uric acid, and the like, based on the parameters extracted in operation 630”) and a display provided on the front surface of the main body (display 714, Fig. 7) and configured to display the measured triglyceride level (Para. 0065) and a contact guide for the patient (Para. 0090). Yang further teaches calibration of the individual parameters of the model (Para. 0060), Yang does not teach wherein the first electrode and the second electrode being on a side surface of the main body and spaced apart each other, or wherein the display is configured to display a first image indicating a position of the impedance sensor to which the object of the user is to be placed to measure the bio-impedance, and a second image pointing a direction toward the impedance sensor.
However, in the art of impedance measurements (Para. 0045), Eom teaches the usage of a bioimpedance device with two electrodes on the same side (electrodes 211 and 212, Fig. 2a) that uses a dynamic image-based system to direct patients to the correct testing position and maintaining the correct position for the needed amount of time (Figs. 6 and 8b, Paras. 0074 and 0081).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yang by Eom, i.e. by using the image-based direction system of Eom in the system of Yang, for the predictable purpose of improving device usage and accuracy as taught in Eom above.
Regarding Claims 2 and 11, Yang modified by Eom makes obvious the inventions above. Yang further teaches wherein the processor is configured to obtain the user information, including at least one of age or gender (Para. 0067, “The storage 520 may store reference information for analyzing an in vivo component, an impedance measurement result, an analysis result of an in vivo component, and the like. In this case, the reference information may include user characteristic information such as a user's age, sex, health condition, and the like, as well as a parameter value at a reference time, an in vivo component analysis model, and the like”), from the user via a user interface (Para. 0071, “The input interface 540 may include a component that permits the apparatus 5 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone)”).
Regarding Claims 3 and 12, Yang modified by Eom makes obvious the inventions above. Yang further teaches wherein the processor is configured to obtain the user information, including at least one of age or gender from an application installed in the apparatus for measuring the triglyceride level or from an application installed in an external electronic device (Para. 0071, “The storage 520 may store reference information for analyzing an in vivo component, an impedance measurement result, an analysis result of an in vivo component, and the like. In this case, the reference information may include user characteristic information such as a user's age, sex, health condition, and the like, as well as a parameter value at a reference time, an in vivo component analysis model, and the like”).
Regarding Claims 4 and 13, Yang modified by Eom makes obvious the inventions above. Yang does not teach wherein the predetermined frequency band has a frequency of 5 kHz.
However, Yang does teach the usage of a frequency in a range including 5 kHz (Para. 0044, “The impedance measurement device 120 may obtain impedance spectrum data by measuring a plurality of impedances based on changing frequencies of the input current within a predetermined frequency range (e.g., a frequency range of 1 kilohertz (kHz) to hundreds of megahertz (MHz))”).
The specification discloses the appropriate ranges that apply to the claimed invention in [0037]. However, the specification does not disclose that the specifically claimed range(s) of “5 kHz” is for any particular purpose or to solve any stated problem that distinguishes it from the other ranges disclosed. The specification therefore lacks disclosure of the criticality required by the Courts in providing patentability to the claimed range(s).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yang, i.e. by operating the frequency of Yang at 5 kHz, as 5 kHz falls within the frequency of Yang, and as it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art [In re Aller, 105 USPQ 233].
Regarding Claim 5, Yang modified by Eom makes obvious the apparatus of Claim 1. Yang further teaches a memory configured to store the learning model (Para. 0068).
Regarding Claims 8, 15, and 18, Yang modified by Eom makes obvious the inventions above. Yang further teaches wherein based on the measured triglyceride level, the processor is configured to provide the user with health-related information including at least one of warning (Para. 0065, “Based on a user input that selects an analysis result of an in vivo component at a particular time in a graph, the output interface 510 may output the information used for analyzing the in vivo component at the particular time and/or other detailed additional information in the first area of the display. In this case, if an estimated in vivo component value is outside of a normal range, the output interface 510 may provide the user with information, indicating that the estimated value is abnormal, by highlighting an abnormal value in red, and the like, or by displaying the abnormal value along with a normal range”).
Regarding Claims 9 and 16, Yang modified by Eom makes obvious the inventions above. Yang further teaches wherein the processor is further configured to collect health-related data including at least one of an underlying condition or a triglyceride level measured at a previous time, and provide the health-related information by using the measured triglyceride level and the collected health data (Para. 0067, “In this case, the reference information may include user characteristic information such as a user's age, sex, health condition, and the like, as well as a parameter value at a reference time, an in vivo component analysis model, and the like”).
Regarding Claim 20, Yang modified by Eom makes obvious the electronic device of Claim 19. Yang further teaches the device further comprising a communication interface configured to receive the bio-impedance, measured by the impedance sensor, from another electronic device (Para. 0069, “The communication interface 530 may communicate with an external device to transmit and receive various data related to analysis of an in vivo component. The external device may include an information processing device such as a smartphone, a tablet PC, a desktop computer, a laptop computer, and the like”).
Regarding Claims 6 and 14, Yang modified by Eommakes obvious the inventions above. Yang further teaches wherein the learning model comprises a non-linear machine learning mapping function (Para. 0062, the Examiner notes that a neural network is form of non-linear mapping).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20200323468 awarded to Yang et al, in view of U.S. Patent Publication 20180049696 awarded to Eom et al as applied to claim 1 above, and further in view of U.S. Patent Publication 20200077961 awarded to Choi et al.
Regarding Claim 7, Yang modified by Eom makes obvious the apparatus of Claim 1. Yang does not teach wherein the processor is configured to output information for guiding the user to measure the triglyceride level at a predetermined time.
However, in the art of triglyceride monitoring (Para. 0014), Choi teaches “guiding the user on a time to measure the target component based on a result of monitoring the change in the value of the target component” (Para. 0015). Choi further teaches that when a blood glucose event occurs, the processor may provide the user with guide information about an action to be taken according to the blood glucose event (Para. 0073). For example, when a hypoglycemic state is predicted, the processor may generate guide information, such as “Did you have a meal?” (Para. 0073). The guide information is not limited to the above example, and may be generated as, for example, “Please check your existing health care,” “Please have a small amount of meal several times, rather than a large amount at a time,” or the like, according to various situations where a blood glucose event, such as hyperglycemia, hypoglycemia, or glucose metabolic abnormality, occurs (Para. 0073).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yang by Choi, i.e. by adding the monitoring prompts of Choi to the device of Yang, for the predictable purpose of improving the monitoring device based on changes in blood glucose state of a user.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 20160106337 to Jung teaches an image-based guidance system for positioning fingers on a bio-impedance sensor (Para. 0053).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jess Mullins whose telephone number is (571)-272-8977. The examiner can normally be reached between the hours of 9:00 a.m. to 5:00 p.m. PST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Unsu Jung, can be reached at (571)-272-8506. The fax number for the organization where this application or proceeding is assigned is (571)-273-8300.
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/JLM/
Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792
Prosecution Insights