DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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 .
Priority
This application claims the priority date of foreign applications KR10-2024-0082182 dated June 24, 2024 and KR10-2024-0102550 dated August 1, 2024 and continuation of PCT/KR2020/002530 dated February 24, 2025.
Status of Claims
Claims 1-15 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 recites the limitation “the display” in line 13. There is insufficient antecedent basis for this limitation in the claim because claim 14 is an independent claim and the terms are not previously referenced therein. Claim 15 depends directly on claim 14 and is therefore rejected due to its dependency on claim 14. Examiner is interpreting “the display” as “a display”. Appropriate correction is requested.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-13 are drawn to an electronic device, which is within the four statutory categories (i.e., machine). Claims 14-15 are to a method for providing information on a medication, which is within the four statutory categories (i.e., process).
Claim 1 recites an electronic device comprising:
a display;
at least one processor comprising processing circuitry; and
memory storing instructions, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to:
receive information on a medication, based on a first input, obtain a medication category related to the medication, based on the information on the medication, receive a second input indicating information on a time at which the medication was taken, obtain biometric information of a user, based on the biometric information and the information on the time at which the medication was taken, obtain a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication, and provide, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor.
Claims 14-15 recite a method for providing information on a medication, the method comprising:
receiving information on a medication, based on a first input;
obtaining a medication category related to the medication, based on the information on the medication;
receiving a second input indicating information on a time at which the medication was taken;
obtaining biometric information of a user; based on the biometric information and the information on the time at which the medication was taken, obtaining a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication; and
providing, through a display, a first visual object indicating the first score according to the first factor and the second score according to the second factor.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity. The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below. If a claim limitation, under its broadest reasonable interpretation, is managing personal behavior or interactions between people but for the recitation of generic computer components, then it fails within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Dependent claims 2-13 and 15 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The dependent claims recite additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 14.
The additional elements from claims 1 and 14 include:
a display (apply it, MPEP 2106.05(f)).
The additional elements from claim 1 include:
an electronic device (apply it, MPEP 2106.05(f)).
at least one processor comprising processing circuitry (apply it, MPEP 2106.05(f)).
memory storing instructions, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (apply it, MPEP 2106.05(f)).
The dependent claims include other limitations including
a smart phone (apply it, MPEP 2106.05(f)).
an external device (apply it, MPEP 2106.05(f)).
a wearable device (apply it, MPEP 2106.05(f)).
Furthermore, claims 1-15 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “a display”, “an electronic device”, “at least one processor”, “processing circuitry”, “memory storing instructions”, “a smart phone”, “an external device”, and “a wearable device”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see, Specification Paragraphs [22]-[24], [29], [44], [46], [51], [68], [126], [149]. (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Specification Paragraphs [22]-[24], [29], [44], [46], [51], [68], [126], [149] disclose that the additional elements (i.e., a display, an electronic device, at least one processor, processing circuitry, memory storing instructions, a smart phone, an external device, and a wearable device) comprise a plurality of different types of generic computing systems that are configured to perform generic computer that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., providing information on medication).
Dependent claims 2-13 and 15 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves providing information on medication or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1-15 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Claims 1-3, 6-7, and 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (U.S. Pub. No. 2024/0379207 A1) in view of Iyer (U.S. Pub. No. 2023/0134811 A1).
Regarding claim 1, Lee discloses electronic device comprising (Paragraph [0116] discusses an electronic device.):
a display (Paragraph [0142] discusses a terminal may display.);
at least one processor comprising processing circuitry (Paragraphs [0119]-[0122] discuss at least one processor.); and
memory storing instructions to (Paragraph [0119] discusses one or more instructions stored in a storage medium (e.g., an internal memory or an external memory) readable by a machine (e.g., an electronic device).),
wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses one or more instructions stored in a storage medium (e.g., an internal memory or an external memory) readable by a machine (e.g., an electronic device), the processor (e.g., processor) of the machine (e.g., the electronic device) may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
receive information on a medication, based on a first input (Examiner notes that the prior art does not specifically reference “first” or “second”, however, the limitation is interpreted in accordance with the Specification.) (Paragraphs [0157]-[0158] discuss a user handles a medication-related object and obtain from the classification model a category associated with the medication, which represents the type of medication, the medication target, etc.),
obtain a medication category related to the medication, based on the information on the medication (Paragraphs [0157]-[0158] discuss a classification model identifies a category associated with the medication, which represents the type of medication, the medication target, etc.),
receive a second input indicating information on a time at which the medication was taken (Paragraphs [0027], [0141], and [0205] discuss a server that determines whether the user has correctly taken the drug based not only on the image but also on the value sensed from various sensors can be provided to improve the convenience for the health management of the user and store the medication history when taken.),
obtain biometric information of a user (Paragraph [0395] and FIG. 64 discuss the wearable device may obtain information about cardiovascular health such as blood pressure, heart rate, heart rhythm, and oxygen saturation and/or diet-related information such as blood sugar, body weight, and BMI.),
based on the biometric information and the information on the time at which the medication was taken, obtain information according to a first factor applicable to a plurality of medication categories and second information according to a second factor applicable to the obtained medication category related to the medication (Paragraphs [0157]-[0158], [0395], [0399], [0402]-[0405], [0414], and FIGS. 54 and 64 discuss obtain the first value of the specific lifelog (e.g., blood pressure) identified using the sensor of the wearable device before and after the time ta and th of the medication taken by the user and based on the identified category associated with the medication acquire information about sleep health such as sleep pattern, sleep quality and/or information about emotion such as depression, stress, blood pressure, blood sugar, weight, heart rate, etc.).
Lee does not explicitly disclose:
obtain a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication; and
provide, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor.
Iyer teaches:
obtain a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication (Paragraphs [0006], [0055]-[0057], and FIG. 7 discuss determining initial user total behavior scores based on the initial user activity behavior scores, carbohydrate behavior scores, and medicine behavior scores; applying the clustering algorithm to the initial user total behavior scores, activity behavior scores, carbohydrate behavior score, medicine behavior scores, and initial user inputs by the plurality of initial users such that the initial user inputs include at least one of metabolic inputs and symptom inputs. The initial user inputs may include metabolic inputs including at least one of blood glucose entries, blood pressure entries, weight entries, labs entries, and screenings entries.); and
provide, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor (Paragraphs [0006], [0055]-[0057], and FIG. 7 discuss determining initial user total behavior scores based on the initial user activity behavior scores, carbohydrate behavior scores, and medicine behavior scores; applying the clustering algorithm to the initial user total behavior scores, activity behavior scores, carbohydrate behavior score, medicine behavior scores, and initial user inputs by the plurality of initial users such that the initial user inputs include at least one of metabolic inputs and symptom inputs. The initial user inputs may include metabolic inputs including at least one of blood glucose entries, blood pressure entries, weight entries, labs entries, and screenings entries.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include obtain a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication and provide, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Regarding claims 2 and 15, Lee discloses wherein the first factor comprises at least one of activity information, sleep information, condition information, and calorie information (Paragraphs [0399] and [0414] discuss the wearable device may acquire information about sleep health such as sleep pattern, sleep quality, etc. and/or information about emotion such as depression, stress, blood pressure, etc.), and
wherein the second factor comprises at least one of weight information, blood sugar information, blood pressure information, heart rate information, sleep information (Paragraphs [0395], [0399], [0414], and FIG. 64 discuss the wearable device may acquire information about sleep health such as sleep pattern, sleep quality, etc. and/or information about emotion such as depression, stress, blood pressure, blood sugar, weight, heart rate, etc.).
Lee does not explicitly disclose:
self-diagnosis information.
Iyer teaches:
self-diagnosis information (Paragraphs [0066] discuss self-reporting and self-monitoring activity.); and
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include self-diagnosis information, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Regarding claim 3, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
determine, based on the medication being classified as a first category, an effect onset time from the time at which the medication was taken (Paragraphs [0158], [0452], and [0455] discuss the classification model to identify a category of the medication and side effects for the disease based on dose time, for example, the second dose time point may be a time point after a considerable time has elapsed so that the efficacy of the first dose is sufficiently expressed after the first dose.),
receive, based on the effect onset time being satisfied, the information from the user (Paragraphs [0086]-[0087], [0158], [0170], [0455], and FIG. 57 discuss receive side effects for the disease based on dose time, for example, the second dose time point may be a time point after a considerable time has elapsed so that the efficacy of the first dose is sufficiently expressed after the first dose, and can include handwriting input.), and
provide the first visual object including the information as the second factor (Paragraphs [0367] discuss the local device (e.g., the user terminal and/or the wearable device) may provide an input interface for inputting information about the medication by hand based on receiving a user's input (e.g., an alarm screen touch, handwritten input menu touch) associated with the alarm.).
Lee does not explicitly disclose:
self-diagnosis information.
Iyer teaches:
self-diagnosis information (Paragraphs [0066] discuss self-reporting and self-monitoring activity.); and
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include self-diagnosis information, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Regarding claim 6, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
determine, based on the medication category being related to insomnia, a deep sleep time based on the biometric information (Paragraphs [0157]-[0158], [0399], [0402], [0415], [0418], [0430], [0435], FIGS. 57, 64, and 66 discuss a classification model identifies a category associated with the medication, which represents the type of medication, the medication target, etc. and identifying medication correlation of the medication target performed over time, and the value of the specific lifelog information about sleep health such as sleep pattern, sleep quality, insomnia, etc. measured over time.), and
provide the sleep information including the deep sleep time as the second factor (Paragraphs [0414]-[0415], [0430], and FIGS. 68-69 discuss provide sleep information over time, quality, lack of sleep, deep sleep.).
Regarding claim 7, Lee discloses wherein the memory stores a dosage history of a medication previously taken by the user and analysis data on the previously taken medication (Paragraphs [0205], [0383], and FIGS. 57, 64 and 66 discuss main body may store the user medication history, the medication history may be a graph related to the medication by time zone and effect of medication on biometric data.), and
wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses one or more instructions stored in a storage medium readable by a machine, the processor of the machine may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
receive an input indicating information on a time at which other medication was taken (Paragraphs [0157] and [0383] discuss obtain and store medication history of each medication type, including specific time medication is performed.),
determine analysis data of the other medication included in the medication category containing the medication based on the dosage history of the medication in response to additional inputs (Paragraphs [0011], [0142], [0157], [0383], and FIG. 3-4 discuss analyzes a user's medicine dose-related image and obtain and store medication history of each medication type, including specific time medication is performed, include information on whether to perform the medication action and/or information on the constituent components of the diet, but are not limited to the illustrated and/or described examples, and may include various types of information based on the analysis results of the health management behavior.),
generate a second visual object based on at least one of effect and side effect from the analysis data of the other medication (Paragraphs [0105], [0388], [0453]-[0455], FIG. 79 discuss providing a dose according to side effects after a dose for various medication, and symptom degree may be determined based on at least one of the degree of occurrence of side effects, the number of occurrences and the duration of side effects, and the health management system may determine the symptom degree for each disease.), and
provide the second visual object through the display (Paragraphs [0453]-[0456] and FIG. 79 discuss graphic associated with medication determination result information displayed on a local device.).
Regarding claim 9, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
provide information on a medication included in a medication category according to symptom entered by the user based on at least one of effect and side effect using analysis data of medications learned by an artificial intelligence model (Paragraphs [0105], [0157]-[0159], [0388], [0453]-[0455], FIGS. 69 and 79 discuss a classification model identifies a category associated with the medication, which represents the type of medication, the medication target (Examiner interprets medication target as symptom.), etc. and providing a dose according to side effects after a dose, and symptom degree may be determined based on at least one of the degree of occurrence of side effects, the number of occurrences and the duration of side effects, and the health management system may determine the symptom degree for each disease.).
Regarding claim 10, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
receive information on side effects of the medication (Paragraph [0388] discusses the health management system may register a health management action in operation and register a side effect object associated with the health management action together in operation.),
store biometric information related to the side effects (Paragraphs [0225] and [0372] discuss acquire and store the medication condition for a specific medication target of the user, information on individual characteristics, information on medication conditions, acquire other biometric information associated with the taking a medication performance.),
obtain the biometric information of the user periodically (Paragraphs [0225] and [0329]-[0330] discuss wearable device acquires biometric information associated with the taking a medication performance.), and
provide, based on the obtained biometric information of the user being within a specified range from the biometric information related to the side effects, a notification (Paragraphs [0105], [0321], [0388], [0453]-[0455], FIG. 79 discuss the reference value for the PPG sensor may be a rate, and the reference range may be equal to or greater than the obtained heart rate; and providing a dose according to side effects after a dose, and symptom degree may be determined based on at least one of the degree of occurrence of side effects, the number of occurrences and the duration of side effects, and the health management system may determine the symptom degree for each disease.).).
Regarding claim 11, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
obtain information based on the biometric information obtained after an effect onset time of the taken medication or a dosage time of the taken medication (Paragraphs [0105], [0388], [0453]-[0455], FIGS. 69 and 79 discuss providing a dose according to side effects after a dose, and symptom degree may be determined based on at least one of the degree of occurrence of side effects, the number of occurrences and the duration of side effects, and the health management system may determine the symptom degree for each disease.).
Lee does not explicitly disclose:
obtain the most recently updated first and second scores.
Iyer teaches:
obtain the most recently updated first and second scores (Paragraphs [0131], and FIG. 7 discuss the predictive value scores may be utilized to identify updated behavior score categories.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include obtain the most recently updated first and second scores, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Regarding claim 12, Lee discloses wherein the electronic device comprises a smartphone (Paragraph [0116] discusses electronic device may include a smartphone.), and
wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
obtain the biometric information from an external device (Paragraphs [0154], [0402]-[0405], and FIG. 54 discuss obtain from the external electronic device, e.g., the wearable device the first value of the specific lifelog (e.g., blood pressure) identified using the sensor of the wearable device before and after the time ta and th of the medication taken by the user.).
Regarding claim 13, Lee discloses wherein the electronic device comprises a wearable device Paragraph [0154] discusses the external electronic device, e.g., the wearable device.), and
wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses one or more instructions stored in a storage medium (e.g., an internal memory or an external memory) readable by a machine (e.g., an electronic device), the processor (e.g., processor) of the machine (e.g., the electronic device) may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
provide, based on data comprising at least one of the first visual object, information related to the first factor, and information related to the second factor being more than a specified number, the data to an external device (Paragraphs [0321], [0399], [0402]-[0405], [0414], and FIGS. 54, 57, 64, 66, 69, 71 discuss the wearable device may acquire information about sleep health such as sleep pattern, sleep quality, etc. and/or information about emotion such as depression, stress, blood pressure, blood sugar, weight, heart rate, etc. and providing to a display information related to the medication and blood sugar and sleep and percentage of improvement, for example, the reference value for the PPG sensor may be a heart rate, and the reference range may be equal to or greater than the obtained heart rate.), and
Regarding claim 14, Lee discloses method for providing information on a medication, the method comprising:
receiving information on a medication, based on a first input (Paragraphs [0157]-[0158] discuss a user handles a medication-related object and obtain from the classification model a category associated with the medication, which represents the type of medication, the medication target, etc.);
obtaining a medication category related to the medication, based on the information on the medication (Paragraphs [0157]-[0158] discuss a classification model identifies a category associated with the medication, which represents the type of medication, the medication target, etc.);
receiving a second input indicating information on a time at which the medication was taken (Paragraphs [0027], [0141], and [0205] discuss a server that determines whether the user has correctly taken the drug based not only on the image but also on the value sensed from various sensors can be provided to improve the convenience for the health management of the user and store the medication history when taken.);
obtaining biometric information of a user (Paragraph [0395] and FIG. 64 discuss the wearable device may obtain information about cardiovascular health such as blood pressure, heart rate, heart rhythm, and oxygen saturation and/or diet-related information such as blood sugar, body weight, and BMI.);
based on the biometric information and the information on the time at which the medication was taken, obtain information according to a first factor applicable to a plurality of medication categories and second information according to a second factor applicable to the obtained medication category related to the medication (Paragraphs [0157]-[0158], [0395], [0399], [0402]-[0405], [0414], and FIGS. 54 and 64 discuss obtain the first value of the specific lifelog (e.g., blood pressure) identified using the sensor of the wearable device before and after the time ta and th of the medication taken by the user and based on the identified category associated with the medication acquire information about sleep health such as sleep pattern, sleep quality and/or information about emotion such as depression, stress, blood pressure, blood sugar, weight, heart rate, etc.).
Lee does not explicitly disclose:
obtaining a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication; and
providing, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor.
Iyer teaches:
obtaining a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication (Paragraphs [0006], [0055]-[0057], and FIG. 7 discuss determining initial user total behavior scores based on the initial user activity behavior scores, carbohydrate behavior scores, and medicine behavior scores; applying the clustering algorithm to the initial user total behavior scores, activity behavior scores, carbohydrate behavior score, medicine behavior scores, and initial user inputs by the plurality of initial users such that the initial user inputs include at least one of metabolic inputs and symptom inputs. The initial user inputs may include metabolic inputs including at least one of blood glucose entries, blood pressure entries, weight entries, labs entries, and screenings entries.); and
providing, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor (Paragraphs [0006], [0055]-[0057], and FIG. 7 discuss determining initial user total behavior scores based on the initial user activity behavior scores, carbohydrate behavior scores, and medicine behavior scores; applying the clustering algorithm to the initial user total behavior scores, activity behavior scores, carbohydrate behavior score, medicine behavior scores, and initial user inputs by the plurality of initial users such that the initial user inputs include at least one of metabolic inputs and symptom inputs. The initial user inputs may include metabolic inputs including at least one of blood glucose entries, blood pressure entries, weight entries, labs entries, and screenings entries.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include obtaining a first score according to a first factor applicable to a plurality of medication categories and a second score according to a second factor applicable to the obtained medication category related to the medication and providing, through the display, a first visual object indicating the first score according to the first factor and the second score according to the second factor, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Iyer and in further view of Bonutti (U.S. Pub. No. 2021/0365815 A1).
Regarding claim 4, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
receive, based on the medication category being related to symptom relief, the information including a degree of symptom relief (Paragraphs [0026], [0158], [0452], and [0455] discuss the classification model to identify a category of the medication and side effects for the disease based on dose time, for example, the second dose time point may be a time point after a considerable time has elapsed so that the efficacy of the first dose is sufficiently expressed after the first dose and uses a correlation between the amount of drug consumption and the value of disease symptoms obtained using the sensor can be provided to adjust the amount of desire for disease improvement.).
Lee does not explicitly disclose:
the medication category being related to pain relief, the information including a degree of pain symptom relief; and
provide the self-diagnosis information as the second score according to the second factor.
Iyer teaches:
provide the self-diagnosis information as the second score according to the second factor (Paragraphs [0006], [0055]-[0057], [0066], and FIG. 7 discuss self-reporting and self-monitoring activity and determining initial user total behavior scores based on the initial user activity behavior scores, carbohydrate behavior scores, and medicine behavior scores; applying the clustering algorithm to the initial user total behavior scores, activity behavior scores, carbohydrate behavior score, medicine behavior scores, and initial user inputs by the plurality of initial users such that the initial user inputs include at least one of metabolic inputs and symptom inputs. The initial user inputs may include metabolic inputs including at least one of blood glucose entries, blood pressure entries, weight entries, labs entries, and screenings entries.); and
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include self-diagnosis information, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Bonutti teaches:
the medication category being related to pain relief, the information including a degree of pain symptom relief (Paragraphs [0112], [0183] discuss to determine when a patient's pain has increased. By predicting pain levels, AI system then varies the dosing so that the patient's discomfort is minimized.); and
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include the medication category being related to pain relief, the information including a degree of pain symptom relief, as taught by Bonutti, in order to improve health services, the accuracy of medical diagnosis, manage treatments, provide real time monitoring of patients, and integrate the different health providers and health services together. (Bonutti Paragraph [0003]).
Regarding claim 5, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
determine, based on the medication category being related to weight loss, a reduced weight based on the biometric information (Paragraphs [0157]-[0158] discuss a classification model identifies a category associated with the medication, which represents the type of medication, the medication target, etc.); and
provide the weight information including the reduced weight according to the second factor (Paragraphs [0395] and [0454] discuss wearable device obtain diet-related information such as blood sugar, body weight, and BMI and determine symptom degree for a disease by including sensors (e.g., body fat detection sensors, sensor capsules, sleepiness detection sensors, heart rate detection sensors, and the like) that may sense various symptoms and output them as a quantitative value.).
Lee does not explicitly disclose:
the medication category being related to weight loss, a reduced weight based on the biometric information; and
provide the second score according to the second factor.
Bonutti teaches:
the medication category being related to weight loss, a reduced weight based on the biometric information (Paragraph [0159] discusses if a person is trying to maximize weight loss and taking a medication to increase metabolism, the application can indicate when the person should take the medication for maximum effect, based on the information from the biosensors and the recommended dosage to indicate the best time of the day to take the medication.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include the medication category being related to weight loss, a reduced weight based on the biometric information, as taught by Bonutti, in order to improve health services, the accuracy of medical diagnosis, manage treatments, provide real time monitoring of patients, and integrate the different health providers and health services together. (Bonutti Paragraph [0003]).
Iyer teaches:
provide the second score according to the second factor (Paragraphs [0006], [0055]-[0057], and FIG. 7 discuss determining initial user total behavior scores based on the initial user activity behavior scores, carbohydrate behavior scores, and medicine behavior scores; applying the clustering algorithm to the initial user total behavior scores, activity behavior scores, carbohydrate behavior score, medicine behavior scores, and initial user inputs by the plurality of initial users such that the initial user inputs include at least one of metabolic inputs and symptom inputs. The initial user inputs may include metabolic inputs including at least one of blood glucose entries, blood pressure entries, weight entries, labs entries, and screenings entries.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include, provide the second score according to the second factor, as taught by Iyer, in order to provide ample data to effectively provide treatment options. (Iyer Paragraphs [0003]-[0004]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Iyer and in further view of Bhathal (U.S. Pub. No. 2013/0238360 A1).
Regarding claim 8, Lee discloses wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Paragraph [0119] discusses the electronic device may invoke at least one instruction among the one or more instructions stored from the storage medium and execute it.):
provide, as a third visual object, at least one of analysis data of patients taking the medication and analysis data of another person with a specified correlation or higher (Paragraphs [0432], [0438], FIGS. 71-72 discuss the system determines the recommended dose by comparing the calculated first medication correlation with a preset first reference correlation (w). Here, the preset first reference correlation (w) may be an average medication correlation of patients with a corresponding disease.).
Lee does not explicitly disclose:
provide, as a third visual object, at least one of analysis data of a family member taking the medication and analysis data of another person with a specified correlation or higher.
Bhathal teaches:
provide, as a third visual object, at least one of analysis data of a family member taking the medication (Paragraph [0031] discusses displaying the family medical history made up of family medications and indications, as well as efficacies and reactions for all members of a user's family network.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Lee to include provide, as a third visual object, at least one of analysis data of a family member taking the medication, as taught by Bhathal, in order allow an individual have their medical information combined with their family historical medical information in order to determine medical risks, and/or assist in diagnoses. (Bhathal Paragraph [0012])
Conclusion
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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