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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 21-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ternes et al. (US 2018/0153460).
Regarding claim 21, Ternes discloses a processor-implemented method comprising:
receiving body movement information (via epileptic monitoring device 222, Fig. 2 & para [0008]);
determining, based on the body movement information, a body motion or a pattern of body motions indicative of an illness, disease, or health condition of a user (e.g., the IDM 112 senses physiological and functional signals in a patient, and predicts an impending epileptic event, para [0047]; confirm or reject the detection of the epileptic event, para [0053]);
identifying a physiological characteristic response of the user associated with the body motion or the pattern of body motions, paras [0008, 0065-0067, 0072]; and
automatically delivering medication to the user based on the identified physiological characteristic response (e.g., see step 550 in Fig. 5 & para [0081]; para [0049], ... the antiepileptic therapy is delivered in a closed-loop fashion, where one or more therapy parameters may be adjusted or drug dosage titrated according to an efficacy of the antiepileptic therapy delivered. The therapy efficacy may be assessed based on sensor feedback, such as the physiological or functional signals sensed during or after the epilepsy event).
Regarding claim 22, further comprising: automatically delivering the medication to the user in a first mode of operation (e.g., see step 550 in Fig. 5 & para [0081]); and
transitioning from the first mode of operation to a second mode of operation in response to identifying the physiological characteristic response, wherein automatically delivering the medication to the user based on the identified physiological characteristic response includes operating in the second mode of operation (e.g., para [0049 & 0070], the antiepileptic therapy may be delivered in a closed-loop fashion, where one or more therapy parameters may be adjusted or drug dosage titrated according to an efficacy of the antiepileptic therapy delivered. The therapy efficacy may be assessed based on sensor feedback, such as the physiological or functional signals sensed during or after the epilepsy event; also see paras [0075, 0081].
Note: the program in the IMD 12 delivers antiepileptic therapy in a closed-loop fashion, para [0054]. Therefore, a second mode of operation is considered as adjusting the drug based on the sensor feedback after performing the first mode of operation.
Regarding claim 23, further comprising: determining a movement-correlated therapy control algorithm (e.g., classifying an epileptic seizure into different categories, paras [0072 & 0075]) by changing a therapy control algorithm based on the identified physiological characteristic response, wherein automatically delivering the medication to the user in the first mode of operation includes automatically delivering the medication to the user in accordance with the therapy control algorithm, (e.g., They controller 425 may dynamically control the system behavior based on the epilepsy detection, e.g., a presence of an epileptic even, or additionally based on the classification of the epileptic seizure types as generated by the epileptic seizure classifier 326) and wherein automatically delivering the medication to the user in the second mode of operation includes automatically delivering the medication to the user in accordance with the movement-correlated therapy control algorithm, see paras [0049, 0070, 0075, 0079 & 0081].
Regarding claim 24, further comprising: obtaining a confirmation from the user confirming a transition of from the first mode of operation to the second mode of operation, wherein transitioning from the first mode of operation to the second mode of operation in response to identifying the physiological characteristic response is further based on obtaining the confirmation (e.g., para [0045], the antiepileptic therapy may be delivered in a closed-loop fashion, where one or more therapy parameters may be adjusted or drug dosage titrated according to an efficacy of the antiepileptic therapy delivered. The therapy efficacy may be assessed based on sensor feedback, such as the physiological or functional signals sensed during or after the epilepsy event).
Regarding claim 25, wherein determining the body motion or the pattern of body motions comprises: searching a movement correlation database populated with movement-correlated physiological response entries that include body motions or patterns of body motions associated with particular diseases, illnesses, or health conditions that influence the physiological characteristic response of the user (e.g., classifying an epileptic seizure into different categories and managing the patient with the classified epileptic seizure..., para [0072], also see para [0075].
Regarding claim 26, wherein searching the movement correlation database comprises matching the body movement information with the body motion or the pattern of body motions in the movement correlation database, paras [0072, 0075].
Regarding claim 27, wherein the received body movement information comprises descriptive data associated with at least one of: a type of body movement; intensity of movement; duration of movement; frequency of movement events; repetitions of movement events; body part(s) in motion; movement start time; or movement end time (e.g., epilepsy seizures or limb movements, para [0008]; the epileptic seizure classifier 326 may classify the detected epileptic event... The classification is based on intensity, duration or frequency of detected epileptic event, para [0073].
Regarding claim 28, wherein the received body movement information comprises user status data generated by at least one ancillary system that monitors the user (e.g., via electrode 116, sensor circuit 210 or one or more sensors to detect epileptic event).
Regarding claim 30, further comprising: providing an insight message to the user, the insight message including a warning about the disease, the illness, or the health condition indicated by the body motion or the pattern of body motions, para [0052].
Regarding claims 31-38, these claims are rejected using the same analysis as noted above with regard to the claims 21-28.
Regarding claim 40, this claim is rejected using the same analysis as noted above with regard to the claims 21-22.
Claims 21-40 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chase (US 2021/0100951).
Regarding claim 21, Chase discloses a processor-implemented method, in Figs. 1-5 comprising:
receiving body movement information (e.g., motion and orientation data, step 32 in Fig. 3, also para [0075]);
determining, based on the body movement information (e.g., foods, exercise, sleep, awake or walking), a body motion or a pattern of body motions indicative of an illness, disease, or health condition of a user (e.g., diabetic patient with high or low blood glucose condition, or Parkinson’s disease, para [0070]);
For example: the para [0079] states that: ... If the controller 14 determines that the motion data and/or orientation data is similar to a stored template, the controller 14 determines that the motion and orientation detected is associated with the ingestion of food and proceeds to the identified profile template method.
The para [0083] states that: ... when the motion and/or orientation data is determined as being similar to an exercise profile at block 38 (FIG. 3). At block 62, the controller 14 compares the motion data and/or orientation data for similarity with a particular exercise profile. At block 64, if the controller 14 determines the data does correspond to a particular exercise profile, the controller 14 proceeds to block 66 where the controller 14 checks for a reported blood glucose level.
The para [0085] states that: ... when the motion and/or orientation data is determined as being similar to a sleep profile at block 38 (FIG. 3) in accordance with embodiments of the present disclosure. At block 80, the controller 14 determines a sleep state is detected. The controller 14 proceeds to block 82, where the controller 14 continues to monitor the motion data and/or orientation data for a determination of a sleep to awake transition, which may be based on a time-weighted evaluation of the data. ... If the controller 14, determines that a sleep to awake transition has occurred (e.g. by the motion data indicating that the user is moving or walking, or the orientation data indicating that the orientation the IPAS 10 has changed), then the controller 14 proceeds to block 84 to check for a reported blood glucose level of the user.
identifying a physiological characteristic response of the user associated with the body motion or the pattern of body motions (by checking blood glucose level, in step 50 in Fig. 4, or step 66 in Fig. 5, or step 84 in Fig. 6); and
automatically delivering medication to the user based on the identified physiological characteristic response (see steps A, B & C in Figs. 3-6).
Regarding claim 22, further comprising: automatically delivering the medication to the user in a first mode of operation (para [0041], the insulin pump is generally able to deliver different preset basal insulin infusion rates based upon fixed, predetermined time schedules, para [0041]. In this case, the preset basal insulin infusion rate is equivalent to the first mode of operation. In addition, the system includes a programming already set up by the first time for a first mode of operation to automatically deliver the medication to a user), and
transitioning from the first mode of operation to a second mode of operation in response to identifying the physiological characteristic response (e.g. in Figs. 307, the system is auto recognizing in each state, for example: food ingestion, exercise, or sleep state and adjust the bolus based on the blood glucose level);
wherein automatically delivering the medication to the user based on the identified physiological characteristic response includes operating in the second mode of operation (e.g., the system is configured to check the blood glucose level in each state, as shown in steps 50-58, 66-74 & 84-88 in Figs. 4-6).
Regarding claim 24, further comprising: obtaining a confirmation from the user confirming a transition of from the first mode of operation to the second mode of operation (by checking blood glucose in steps 54, 70 & 84 in Figs. 4-6, and adjust the delivery rate or provide the second mode of operation in steps 56 & 58, or 72 & 74, or 88 in Figs. 4-6), wherein transitioning from the first mode of operation to the second mode of operation in response to identifying the physiological characteristic response (in steps 54, 70 or 84 in Figs. 4-6) is further based on obtaining the confirmation.
Regarding claims 25-26, wherein determining the body motion or the pattern of body motions comprises: searching a movement correlation database populated (via a template match found in the system such as food, exercise, sleep) with movement-correlated physiological response entries that include body motions or patterns of body motions associated with particular diseases, illnesses, or health conditions (e.g. either high or low blood glucose level) that influence the physiological characteristic response of the user; wherein searching the movement correlation database comprises matching the body movement information with the body motion or the pattern of body motions in the movement correlation database (e.g., during food ingestion time, intensity of movement, i.e. exercise, moving during awake or sleep).
For example: the para [0079] states that: ... If the controller 14 determines that the motion data and/or orientation data is similar to a stored template, the controller 14 determines that the motion and orientation detected is associated with the ingestion of food and proceeds to the identified profile template method.
The para [0083] states that: ... when the motion and/or orientation data is determined as being similar to an exercise profile at block 38 (FIG. 3). At block 62, the controller 14 compares the motion data and/or orientation data for similarity with a particular exercise profile. At block 64, if the controller 14 determines the data does correspond to a particular exercise profile, the controller 14 proceeds to block 66 where the controller 14 checks for a reported blood glucose level.
The para [0085] states that: ... when the motion and/or orientation data is determined as being similar to a sleep profile at block 38 (FIG. 3) in accordance with embodiments of the present disclosure. At block 80, the controller 14 determines a sleep state is detected. The controller 14 proceeds to block 82, where the controller 14 continues to monitor the motion data and/or orientation data for a determination of a sleep to awake transition, which may be based on a time-weighted evaluation of the data. ... If the controller 14, determines that a sleep to awake transition has occurred (e.g. by the motion data indicating that the user is moving or walking, or the orientation data indicating that the orientation the IPAS 10 has changed), then the controller 14 proceeds to block 84 to check for a reported blood glucose level of the user.
Regarding claim 27, wherein the received body movement information comprises descriptive data associated with at least one of: a type of body movement (during food ingestion time or walking during awake); intensity of movement (exercise); duration of movement (during food ingestion time, or walking during awake); frequency of movement events; repetitions of movement events; body part(s) in motion.
Regarding claim 28, wherein the received body movement information comprises user status data generated by at least one ancillary system that monitors the user (e.g., accelerometer sensor, or gyroscopic pitch sensor).
Regarding claim 29, wherein: the medication comprises insulin; and the physiological characteristic response is a glucose response of the user (e.g., the blood glucose level is changed and depended on different states such as: food ingestion, exercise or sleep).
Regarding claim 30, further comprising: providing an insight message to the user(e.g., an emergency call or message sent to the user and/or location information, para [0027], providing a warning alarm, para [0054], or flash light, para [0075]), the insight message including a warning about the disease, the illness, or the health condition (e.g. either blood glucose at dangerous level of high or low blood glucose) indicated by the body motion or the pattern of body motions.
Regarding claims 31-39, these claims are rejected using the same analysis as noted above with regard to the claims 21-29.
Regarding claim 40, this claim is rejected using the same analysis as noted above with regard to the claims 21-22.
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.
Claims 29 & 39 are rejected under 35 U.S.C. 103 as being unpatentable over Ternes et al. (US 2018/0153460) in view of Cinar et al. (US 2016/0354543).
Chase (US 2021/0100951).
. Regarding claims 29 & 39, Ternes discloses all the claimed subject matter as required except for the medication comprises insulin, as required in the claims 29 & 30. It is noted that Ternes discloses that using the infusion pump for drug delivery and it is well-known in the art that using an infusion pump for delivering insulin.
Cinar discloses a method and modules or process for recursive model identification, hypoglycemia or hyperglycemia, plasma insulin concentration, assessment of physical activity for determining a presence of major factors or body movement, e.g., meals physical activity, acute stress, sleep..., para [0009] & abstract). It is noted that hypoglycemia or hyperglycemia causes dizziness, unconsciousness, and seizures if untreated diabetic coma or death. Cinar further discloses that automatically delivery medication, i.e. insulin to the use based on the identified physiological characteristic responses, i.e., a blood glucose concentration response of the user such as seizure in hypoglycemia or hyperglycemia.
It would have been obvious to one of ordinary skill in the art, prior to the effective filling date of the claimed invention to modify the device of Ternes with obtaining module for detecting body movement information, i.e. seizure in hypoglycemia or hyperglycemia and delivering insulin when the patient in serious health condition (seizure), as taught by Cinar, in order to deliver the insulin for controlling blood glucose level in diabetic patient.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,938,301. Although the conflicting claims are not identical, they are not patentably distinct from each other because they are not structurally distinguishable from the claims in the patent.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUYNH-NHU HOANG VU whose telephone number is (571)272-3228. The examiner can normally be reached on M-F 7:30 am-4:00 pm.
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/Quynh-Nhu H. Vu/
Quynh-Nhu H Vu
Primary Examiner, Art Unit 3783
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