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 .
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12/097,011. Although the claims at issue are not identical, they are not patentably distinct from each other because it is clear that all of the elements of claims 1-20 of the present application are to be found in claims 1-17 of Patent No. 12/097,011. The difference between the claims lies in the fact that the patent claims anticipate all of the claims of the present application, with differences including only more specific elements of the present application through the use of the “consisting of” language in the patent claims 1 and 13, compared to the “comprising” language in the present application claims 1 and 15, as shown below. Thus, the invention of the patent claims are in effect a “species” of the “generic” invention of the present application’s claims 1-20. It has been held that the generic invention is "anticipated" by the "species". See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since claims 1-20 are anticipated by claims 1-17 of the patent, it is not patentably distinct from the patent.
Present Application 18/487,268
U.S. Patent No. 12/097,011
Claim 1 - A thermal device for monitoring core body temperature in a subject, comprising:
a patch made of a flexible, foldable substrate that when folded forms a top layer that is a thermal zone and a bottom layer having an adhesive disposed thereon, said patch removably attachable to the skin, said patch comprising:
on the thermal zone:
an annular copper ring circumferentially disposed around a thermally conducting material and electrically isolated therefrom;
a pair of copper semi-circular components disposed within the annular copper ring and electrically isolated therewithin; said thermally conducting material disposed beneath the pair of copper semi-circular components;
a thermal sensing component comprising a plurality of thermal sensors disposed within the thermal zone on the top layer and operably connected thereon; and
a first insulating material disposed in a covering relationship on the top layer of the patch;
a second insulating material disposed in a covering relationship on the bottom layer of the patch and comprising a central opening therethrough sized to secure the thermally conducting material therein; and
means for communicating data acquired via the thermal sensing component to a machine learning algorithm configured to predict the core body temperature in the subject.
Claim 1 - A thermal device for monitoring core body temperature in a subject, consisting of:
a patch made of a flexible, foldable substrate that when folded forms a top layer that is a thermal zone and a bottom layer having an adhesive disposed thereon, said patch removably attachable to the skin, said patch consisting of:
on the thermal zone:
an annular copper ring circumferentially disposed around a thermally conducting material and electrically isolated therefrom;
a pair of copper semi-circular components disposed within the annular copper ring and electrically isolated therewithin; said thermally conducting material disposed beneath the pair of copper semi-circular components;
a thermal sensing component comprising a plurality of thermal sensors disposed within the thermal zone on the top layer and operably connected thereon; and
a first insulating material disposed in a covering relationship on the top layer of the patch;
a second insulating material disposed in a covering relationship on the bottom layer of the patch and comprising a central opening therethrough sized to secure the thermally conducting material therein; and
a wireless connection to communicate data acquired via the thermal sensing component to a machine learning algorithm configured to predict the core body temperature in the subject.
Claim 2 - The thermal device of claim 1, wherein the thermally conducting material is a low-density polyethylene formed as a thermal plug.
Claim 2 - The thermal device of claim 1, wherein the thermally conducting material is a low-density polyethylene formed as a thermal plug.
Claim 3 - The thermal device of claim 1, wherein the plurality of thermal sensors comprises:
a first thermal sensor disposed between the pair of copper semi-circular components;
a second thermal sensor disposed on the flexible, folded substrate radially beyond the edge of the annular copper ring;
a third thermal sensor disposed on the flexible, folded substrate between the annular copper ring and the pair of copper semi-circular components or disposed on the annular copper ring; and
a fourth thermal sensor disposed in a section of the flexible, folded substrate proximate to the first thermal sensor.
Claim 3 - The thermal device of claim 1, wherein the plurality of thermal sensors comprises:
a first thermal sensor disposed between the pair of copper semi-circular components;
a second thermal sensor disposed on the flexible, folded substrate radially beyond the edge of the annular copper ring;
a third thermal sensor disposed on the flexible, folded substrate between the annular copper ring and the pair of copper semi-circular components or disposed on the annular copper ring; and
a fourth thermal sensor disposed in a section of the flexible, folded substrate proximate to the first thermal sensor.
Claim 4 - The thermal device of claim 2, wherein the first insulating material is a thermal insulating foam and the second insulating material on the bottom layer is a flexible insulating foam disposed to cover sections formed by the plurality of thermal sensors to define a thermal spatial gradient across the patch.
Claim 4 - The thermal device of claim 1, wherein the first insulating material is a thermal insulating foam and the second insulating material on the bottom layer is a flexible insulating foam disposed to cover sections formed by the plurality of thermal sensors to define a thermal spatial gradient across the patch.
Claim 5 - A system for predicting core body temperature in a subject, comprising:
the patch of claim 1; and
said machine learning algorithm tangibly stored on an electronic device having at least a memory and a processor, said machine learning algorithm configured to receive input from at least a plurality of thermal sensors contained on the thermal component disposed on the patch and to output at least the predicted core body temperature.
Claim 5 - A system for predicting core body temperature in a subject, consisting of:
the patch of claim 1; and
said machine learning algorithm tangibly stored on an electronic device having at least a memory and a processor, said machine learning algorithm configured to receive input from at least a plurality of thermal sensors contained on the thermal component disposed on the patch and from at least one environmental context sensor and to output at least the predicted core body temperature.
Claim 6 - The system of claim 5, further comprising at least one environmental context sensor configured to provide contextual information, said machine learning algorithm configured to receive input therefrom.
Claim 6 - The system of claim 5, wherein the at least one environmental context sensor is configured to provide contextual information.
Claim 7 - The system of claim 6, wherein the input from the at least one environmental contextual sensor comprises room temperature or ambient air velocity or a combination thereof.
Claim 7 - The system of claim 6, wherein the input from the at least one environmental contextual sensor comprises a room temperature or an ambient air velocity or a combination thereof.
Claim 8 - The system of claim 7, wherein the input from the at least one environmental contextual sensor further comprises an indication that the patch is covered or is uncovered after placement on the subject.
Claim 8 - The system of claim 7, wherein the input from the at least one environmental contextual sensor further comprises an indication that the patch is covered or is uncovered after placement on the subject.
Claim 9 - The system of claim 8, wherein the input from the at least one environmental contextual sensor further comprises at least one of the sex of the patient, a body mass index or time of a menstrual cycle.
Claim 9 - The system of claim 8, wherein the input from the at least one environmental contextual sensor further comprises at least one of the sex of the patient, a body mass index or time of a menstrual cycle.
Claim 10 - A method for predicting a core body temperature of a patient in need thereof, comprising:
a) adhering the patch comprising the system of claim 5 via the adhesive disposed thereon to the patient;
b) transmitting data acquired by the plurality of thermal sensors as input into the machine learning algorithm comprising the system over a period of time;
c) analyzing the data to predict the core body temperature; and
d) outputting the core body temperature.
Claim 10 - A method for predicting a core body temperature of a patient in need thereof, comprising:
a) adhering the patch in the system of claim 5 via the adhesive disposed thereon to the patient;
b) transmitting data acquired by the plurality of thermal sensors as input into the machine learning algorithm comprising the system over a period of time;
d) analyzing the data to predict the core body temperature; and
e) outputting the core body temperature.
Claim 11 - The method of claim 10, further comprising:
e) transmitting into the machine learning algorithm contextual data acquired by at least one environmental contextual sensor.
Claim 10 - c) transmitting into the machine learning algorithm contextual data acquired by at least one environmental contextual sensor.
Claim 12 - The method of claim 11, further comprising repeating steps b) to e) at least once over a period of about 24 hours.
Claim 10 – f) repeating steps b) to e) at least once over a period of about 24 hours.
Claim 13 - The method of claim 11, wherein the contextual data is ambient data comprising room temperature or ambient air velocity or is patient data comprising sex, body mass index or time of a menstrual cycle or a combination of the ambient data and the patient data.
Claim 11 - The method of claim 10, wherein the contextual data is ambient data comprising room temperature or ambient air velocity or is patient data comprising sex, body mass index or time of a menstrual cycle or a combination of the ambient data and the patient data.
Claim 14 - The method of claim 13, wherein the contextual data further comprises a status of the patch as covered or not covered.
Claim 12 - The method of claim 13, wherein the contextual data further comprises a status of the patch as covered or not covered.
Claim 15 - A single-use temperature measurement device for measuring core body temperature of a subject, comprising:
a flexible, folded substrate comprising a thermal zone on a top surface thereof;
an electrically isolated annular copper ring disposed on the top surface of the flexible, folded substrate to surround the thermal zone;
a first semi-circular copper component and a second semi-circular copper component both disposed on the top surface of the flexible, folded substrate inside the electrically isolated annular copper ring and both electrically isolated therewithin;
a plurality of temperature sensors disposed within the thermal zone on the flexible, folded substrate and operably connected thereto;
a top insulator disposed over the top surface of the flexible, folded substrate;
a bottom flexible insulator formed with a central opening therethrough and disposed on a bottom surface of the flexible, folded substrate to cover sections thereon formed by the plurality of temperature sensors;
a thermal plug disposed beneath the first semi-circular copper component and the second semi-circular copper component and within the central opening through the bottom flexible insulator;
an adhesive disposed on the bottom surface of the flexible, folded substrate to removably secure to the subject; and
means for connecting to a machine learning algorithm.
Claim 13 - A temperature measurement system for measuring core body temperature of a subject, consisting of: a patch consisting of:
a flexible, folded substrate with a thermal zone on a top surface thereof;
an electrically isolated annular copper ring disposed on the top surface of the flexible, folded substrate to surround the thermal zone;
a first semi-circular copper component and a second semi-circular copper component both disposed on the top surface of the flexible, folded substrate inside the electrically isolated annular copper ring and both electrically isolated therewithin;
a plurality of temperature sensors disposed within the thermal zone on the flexible, folded substrate and operably connected thereto;
a top insulator disposed over the top surface of the flexible, folded substrate;
a bottom flexible insulator formed with a central opening therethrough and disposed on a bottom surface of the flexible, folded substrate to cover sections thereon formed by the plurality of temperature sensors;
a thermal plug disposed beneath the first semi-circular copper component and the second semi-circular copper component and within the central opening through the bottom flexible insulator;
an adhesive disposed on the bottom surface of the flexible, folded substrate to removably secure to the subject; and
a machine learning algorithm tangibly store on an electronic device in ireless connection with the patch and having at least a memory and a processor, said machine learning algorithm configured to receive and analyze input data from the plurality of temperature sensors disposed on the temperature measurement device and from an environmental context sensor and to output at least the predicted core body temperature.
Claim 16 - The single-use temperature measurement device of claim 15, wherein the plurality of temperature sensors comprises:
a first temperature sensor disposed in the thermal conducting zone between the first copper semi-circle and the second copper semi-circle;
a second temperature sensor disposed on the flexible, folded substrate radially beyond the edge of the annular copper ring;
a third temperature sensor disposed on the flexible, folded substrate between the annular copper ring and the copper circle or disposed on the annular copper ring; and
a fourth temperature sensor disposed in a section of the flexible, folded substrate proximate to the first thermal sensor.
Claim 14 - The temperature measurement system of claim 15, wherein the plurality of temperature sensors comprises:
a first temperature sensor disposed in the thermal conducting zone between the first copper semi-circle and the second copper semi-circle;
a second temperature sensor disposed on the flexible, folded substrate radially beyond the edge of the annular copper ring;
a third temperature sensor disposed on the flexible, folded substrate between the annular copper ring and the copper circle or disposed on the annular copper ring; and
a fourth temperature sensor disposed in a section of the flexible, folded substrate proximate to the first thermal sensor.
Claim 17 - The single-use temperature measurement device of claim 15, wherein said thermal plug is made from a low-density polyethylene.
Claim 15 - The temperature measurement system of claim 15, wherein said thermal plug is made from a low-density polyethylene.
Claim 18 - The single-use temperature measurement device of claim 16, wherein said device is constructed for a single use of about 24 hours.
Claim 16 - The temperature measurement system of claim 16, wherein said device is constructed for a single use of about 24 hours.
Claim 19 - A system for measuring core body temperature in a subject, comprising:
the single-use temperature measurement device of claim 16; and
a machine learning algorithm tangibly stored on an electronic device having at least a memory and a processor, said machine learning algorithm configured to receive and analyze input data from the plurality of temperature sensors disposed on the temperature measurement device and from an environmental context sensor and to output at least the predicted core body temperature.
Claim 13 - a machine learning algorithm tangibly store on an electronic device in ireless connection with the patch and having at least a memory and a processor, said machine learning algorithm configured to receive and analyze input data from the plurality of temperature sensors disposed on the temperature measurement device and from an environmental context sensor and to output at least the predicted core body temperature.
Claim 20 - The system of claim 16, wherein the contextual data comprises ambient temperature, ambient air velocity or whether the patch is covered or is uncovered when on the subject or comprises the sex of the subject, body mass index of the subject or time of a menstrual cycle or a combination thereof.
Claim 17 – The temperature measurement system of claim 16, wherein the contextual data input into the machine learning algorithm is an ambient temperature, an ambient air velocity, or whether the patch is covered or is uncovered when on the subject, or is the sex of the subject, a body mass index of the subject or a time of a menstrual cycle or a combination thereof.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Objections
Claim 19 is objected to because of the following informalities: “a machine learning algorithm” should read “the machine learning algorithm” to follow proper antecedent basis. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210290072 A1 (Forrest et al.) in view of US 20210100454 A1 (Gannon et al.).
Regarding claim 1, Forrest teaches a thermal device for monitoring core body temperature in a subject (Abstract, Figs. 1-10B), comprising:
a patch made of a flexible, foldable substrate that when folded forms a top layer that is a thermal zone and a bottom layer having an adhesive disposed thereon, said patch removably attachable to the skin ([0063] “Substrate 25 can comprise a material configured to allow for removable securement of the wearable device 10 to the user's skin. For example, the substrate 25 can be coated with a high tack, medical-grade adhesive, which when in contact with the subject's skin, is suitable for long-term monitoring”; [0070] “The housing 40 can be rigid or alternatively, flexible. The housing 40 can be made of and/or include thermoplastics and/or thermosetting polymers.”), said patch comprising:
on the thermal zone:
an annular copper ring circumferentially disposed around a thermally conducting material and electrically isolated therefrom ([0098] “the one or more openings 159 include (for example, are filled with) a thermally conductive material, such as gold and/or copper,” (The copper is disposed in a ring around the opening); “the one or more openings 159 (and/or an array formed by a plurality of the openings 159) can align with the temperature sensor 150a, the thermal paste 173, the probe 140 (for example, an axis extending through a height of the probe 140), the slot 132 of the mounting frame 130, and/or the opening 55 of substrate 50”);
a pair of copper semi-circular components disposed within the annular copper ring and electrically isolated therewithin; said thermally conducting material disposed beneath the pair of copper semi-circular components ([0100] “The thermally conductive pad 155 can be metallic. For example, the thermally conductive pad 155 can include gold and/or copper.”);
a thermal sensing component comprising a plurality of thermal sensors disposed within the thermal zone on the top layer and operably connected thereon ([0052] “The wearable device 10 can include a probe that acts as a conduit to transmit thermal energy from the subject to and/or toward one or more temperature sensors 16”); and
a first insulating material disposed in a covering relationship on the top layer of the patch ([0069] “Any or all of substrates 25, 50, 20 can be made of a material that can provide thermal insulation”; [0075] “the housing 40 (such as the top portion 41a of the housing 40) to prevent the temperature sensor 150a from being influenced by a temperature of the housing 40”);
a second insulating material disposed in a covering relationship on the bottom layer of the patch and comprising a central opening therethrough sized to secure the thermally conducting material therein ([0069] “Any or all of substrates 25, 50, 20 can be made of a material that can provide thermal insulation”; “the substrates 25, 50, 20 can insulate the skin surface around the opening 55”); and
means for communicating data acquired via the thermal sensing component to predict the core body temperature in the subject ([0049-0050]; [0013] “The wearable device can further comprise a wireless transceiver coupled to the circuit board and configured to wirelessly transmit one or more signals responsive to the determined body temperature over a wireless communication protocol.”; [0103] “allow the probe 140 to transmit thermal energy indicative of the subject's core body temperature”; [0124]).
Forrest does not explicitly teach a machine learning algorithm configured to predict the core body temperature in the subject.
However,
Gannon teaches a machine learning algorithm configured to predict the core body temperature in the subject ([0005] “the temperature data being collected from a patient during a collection time period; inputting the received temperature data to a machine learning system, the machine learning system being trained to output prediction temperature data for a future time period).
It would have been obvious for one of ordinary skill in the art to have modified the thermal device taught by Forrest to include a machine learning algorithm. Forrest recites "For example, the processor 11 can be configured to determine a core body temperature of a user based on thermal energy obtained by one or more temperature sensors 16 of the wearable device 10. The wireless transceiver 13 can be configured to wirelessly transmit the processed physiological information (and/or unprocessed physiological information) to a separate computing device, such as a patient monitor, a mobile device (for example, an iOS or Android enabled smartphone, tablet, laptop), a server or other computing or processing device for display and/or further processing, among other things" [0050], which teaches the function of the algorithm as recited in the claim. One would have been motivated to explicitly recite a machine learning algorithm because temperature data from a patch device input into a machine learning model is be able to identify and diagnose disease based on the body temperature value, as suggested by Gannon [0020].
Regarding claim 2, Forrest teaches the thermal device of claim 1, wherein the thermally conducting material is a low-density polyethylene formed as a thermal plug ([0060] “Substrates 20, 50 can be made of foam material such as white polyethylene”).
Regarding claim 3, Forrest teaches the thermal device of claim 1, wherein the plurality of thermal sensors ([0049] “The one or more temperature sensors 16 can be, for example, any of temperature sensors 150a, 150b, 150c”) comprises:
a first thermal sensor disposed between the pair of copper semi-circular components ([0054] “one or more temperature sensors coupled to the circuit board 105 (such as temperature sensors 150a, 150b”));
a second thermal sensor disposed on the flexible, folded substrate radially beyond the edge of the annular copper ring ([0098] “the probe 140 (for example, an axis extending through a height of the probe 140)”);
a third thermal sensor disposed on the flexible, folded substrate between the annular copper ring and the pair of copper semi-circular components or disposed on the annular copper ring ([0098] “the one or more openings 159 (and/or an array formed by a plurality of the openings 159) can align with the temperature sensor 150a”); and
a fourth thermal sensor disposed in a section of the flexible, folded substrate proximate to the first thermal sensor ([0113] “FIGS. 8A-8D also illustrate a flexible circuit 230 and a temperature sensor 150c which can be coupled to a portion of the flexible circuit 230”).
Regarding claim 4, Forrest teaches the thermal device of claim 2, wherein the first insulating material is a thermal insulating foam ([0060] “Substrates 20, 50 can be made of foam material”; [0069] “thermal insulation properties. In some configurations, the substrates 20 and/or 50 are made of thermally insulating materials including polyurethane foam, polystyrene foam, neoprene foam, neoprene rubber, polyester (Mylar), polytetrafluoroethylene (PTFE), silicone foam”)
and the second insulating material on the bottom layer is a flexible insulating foam disposed to cover sections formed by the plurality of thermal sensors to define a thermal spatial gradient across the patch ([0060] “Substrates 20, 50 can be made of foam material”; [0069] “In the human body, there is a natural heat flux between the body core and the skin surface because the body core temperature is typically at a higher temperature than that of the skin surface. Thus, heat flows from the body core to the skin. By insulating the skin surface at and around the opening 55 and/or the probe 140 (or probe 240)—thereby preventing heat from escaping—the temperature gradient between the body core and the skin surface will decrease”).
Regarding claim 5, Forrest teaches a system for predicting core body temperature in a subject, comprising:
the patch of claim 1; and
said algorithm tangibly stored on an electronic device having at least a memory and a processor ([0133] “Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium or device (e.g., solid state storage devices, disk drives, etc.).”),
said algorithm configured to receive input from at least a plurality of thermal sensors contained on the thermal component disposed on the patch and to output at least the predicted core body temperature ([0049] “the processor 11 can compare temperature data from more than one temperature sensor 16”; [0050] “The processor 11 of the wearable device 10 can be configured to process obtained physiological information. For example, the processor 11 can be configured to determine a core body temperature of a user based on thermal energy obtained by one or more temperature sensors 16 of the wearable device 10”).
Forrest does not explicitly teach said machine learning algorithm.
However,
Gannon teaches said machine learning ([0005] “the temperature data being collected from a patient during a collection time period; inputting the received temperature data to a machine learning system, the machine learning system being trained to output prediction temperature data for a future time period”).
It would have been obvious for one of ordinary skill in the art to have modified the thermal device taught by Forrest to include a machine learning algorithm. Forrest recites "For example, the processor 11 can be configured to determine a core body temperature of a user based on thermal energy obtained by one or more temperature sensors 16 of the wearable device 10. The wireless transceiver 13 can be configured to wirelessly transmit the processed physiological information (and/or unprocessed physiological information) to a separate computing device, such as a patient monitor, a mobile device (for example, an iOS or Android enabled smartphone, tablet, laptop), a server or other computing or processing device for display and/or further processing, among other things" [0050], which teaches the function of the algorithm as recited in the claim. One would have been motivated to explicitly recite a machine learning algorithm because temperature data from a patch device input into a machine learning model is be able to identify and diagnose disease based on the body temperature value, as suggested by Gannon [0020].
Regarding claim 6, Forrest teaches the system of claim 5, further comprising at least one environmental context sensor configured to provide contextual information, said machine learning algorithm configured to receive input therefrom ([0012] “The wearable device can further comprise a second temperature sensor coupled to the circuit board and spaced away from the first temperature sensor by a first distance, the second temperature sensor configured to measure an ambient temperature outside an interior of the housing.”).
Forrest does not explicitly teach said machine learning algorithm.
However,
Gannon teaches said machine learning ([0005] “the temperature data being collected from a patient during a collection time period; inputting the received temperature data to a machine learning system, the machine learning system being trained to output prediction temperature data for a future time period”).
It would have been obvious for one of ordinary skill in the art to have modified the thermal device taught by Forrest to include a machine learning algorithm. Forrest recites "For example, the processor 11 can be configured to determine a core body temperature of a user based on thermal energy obtained by one or more temperature sensors 16 of the wearable device 10. The wireless transceiver 13 can be configured to wirelessly transmit the processed physiological information (and/or unprocessed physiological information) to a separate computing device, such as a patient monitor, a mobile device (for example, an iOS or Android enabled smartphone, tablet, laptop), a server or other computing or processing device for display and/or further processing, among other things" [0050], which teaches the function of the algorithm as recited in the claim. One would have been motivated to explicitly recite a machine learning algorithm because temperature data from a patch device input into a machine learning model is be able to identify and diagnose disease based on the body temperature value, as suggested by Gannon [0020].
Regarding claim 7, Forrest teaches the system of claim 6, wherein the input from the at least one environmental contextual sensor comprises room temperature or ambient air velocity or a combination thereof ([0012] “The wearable device can further comprise a second temperature sensor coupled to the circuit board and spaced away from the first temperature sensor by a first distance, the second temperature sensor configured to measure an ambient temperature outside an interior of the housing.”).
Regarding claim 8, Forrest teaches the system of claim 7, wherein the input from the at least one environmental contextual sensor further comprises an indication that the patch is covered or is uncovered after placement on the subject ([0072] “the wearable device 10 can include an emitter 133 configured to emit light of one or more wavelengths to indicate a status of the wearable device 10”; [0077] “he housing 40 can include one or more indicators configured to assist in the positioning and/or placement of the battery isolator 18 with respect to the housing 40 during assembly of the wearable device 10”; [0107] “Temperature sensor 150b can be used to measure an ambient temperature, for example, a temperature outside the interior of the housing 40.”; [0108] “the temperature sensor 150b is surrounded by a material in order to isolate the temperature sensor 150b from nearby electrical components and/or to prevent the temperature sensor 150b from being thermally influenced by the temperature of the interior of the housing 40 so that the temperature sensor 150b can better measure ambient temperatures outside the housing 40.; [0108] “The temperature sensor 150b can be configured to generate one or more signals based on received thermal energy, whether from the interior of the housing 40 or from ambient (for example, via the thermal putty 120).”).
Regarding claim 9, Forrest teaches the system of claim 8, wherein the input from the at least one environmental contextual sensor further comprises at least one of the sex of the patient, a body mass index or time of a menstrual cycle ([0012] “The wearable device can further comprise a second temperature sensor coupled to the circuit board and spaced away from the first temperature sensor by a first distance, the second temperature sensor configured to measure an ambient temperature outside an interior of the housing.”).
Regarding claim 10, Forrest teaches a method for predicting a core body temperature of a patient in need thereof, comprising:
a) adhering the patch comprising the system of claim 5 via the adhesive disposed thereon to the patient ([0063] “a material configured to allow for removable securement of the wearable device 10 to the user's skin. For example, the substrate 25 can be coated with a high tack, medical-grade adhesive, which when in contact with the subject's skin, is suitable for long-term monitoring”);
b) transmitting data acquired by the plurality of thermal sensors as input into the system over a period of time ([0049] “the processor 11 can compare temperature data from more than one temperature sensor 16”);
c) analyzing the data to predict the core body temperature ([0043] “the wearable device 10 can continuously or periodically wirelessly transmit temperature data of the subject to a separate device”; [0049]); and
d) outputting the core body temperature ([0049] “As shown in FIG. 2I, the wearable device 10 can include one or more temperature sensors 16 that can continuously or periodically obtain temperature data of the subject. Advantageously, in some implementations, the processor 11 can compare temperature data from more than one temperature sensor 16 to more accurately determine core body temperature of the subject”).
Forrest does not explicitly teach the machine learning algorithm.
However,
Gannon teaches the machine learning ([0005] “the temperature data being collected from a patient during a collection time period; inputting the received temperature data to a machine learning system, the machine learning system being trained to output prediction temperature data for a future time period”).
It would have been obvious for one of ordinary skill in the art to have modified the thermal device taught by Forrest to include a machine learning algorithm. Forrest recites "For example, the processor 11 can be configured to determine a core body temperature of a user based on thermal energy obtained by one or more temperature sensors 16 of the wearable device 10. The wireless transceiver 13 can be configured to wirelessly transmit the processed physiological information (and/or unprocessed physiological information) to a separate computing device, such as a patient monitor, a mobile device (for example, an iOS or Android enabled smartphone, tablet, laptop), a server or other computing or processing device for display and/or further processing, among other things" [0050], which teaches the function of the algorithm as recited in the claim. One would have been motivated to explicitly recite a machine learning algorithm because temperature data from a patch device input into a machine learning model is be able to identify and diagnose disease based on the body temperature value, as suggested by Gannon [0020].
Regarding claim 11, Forrest teaches the method of claim 10, further comprising:
e) transmitting contextual data acquired by at least one environmental contextual sensor ([0012] “The wearable device can further comprise a second temperature sensor coupled to the circuit board and spaced away from the first temperature sensor by a first distance, the second temperature sensor configured to measure an ambient temperature outside an interior of the housing.”; [0049] “the processor 11 can compare temperature data from more than one temperature sensor 16”).
Forrest does not explicitly teach the machine learning algorithm.
However,
Gannon teaches the machine learning ([0005] “the temperature data being collected from a patient during a collection time period; inputting the received temperature data to a machine learning system, the machine learning system being trained to output prediction temperature data for a future time period”).
It would have been obvious for one of ordinary skill in the art to have modified the thermal device taught by Forrest to include a machine learning algorithm. Forrest recites "For example, the processor 11 can be configured to determine a core body temperature of a user based on thermal energy obtained by one or more temperature sensors 16 of the wearable device 10. The wireless transceiver 13 can be configured to wirelessly transmit the processed physiological information (and/or unprocessed physiological information) to a separate computing device, such as a patient monitor, a mobile device (for example, an iOS or Android enabled smartphone, tablet, laptop), a server or other computing or processing device for display and/or further processing, among other things" [0050], which teaches the function of the algorithm as recited in the claim. One would have been motivated to explicitly recite a machine learning algorithm because temperature data from a patch device input into a machine learning model is be able to identify and diagnose disease based on the body temperature value, as suggested by Gannon [0020].
Regarding claim 12, Forrest teaches the method of claim 11, further comprising repeating steps b) to e) at least once over a period of about 24 hours ([0043] “the wearable device 10 can continuously or periodically wirelessly transmit temperature data of the subject to a separate device”).
Regarding claim 13, Forrest teaches the method of claim 11, wherein the contextual data is ambient data comprising room temperature or ambient air velocity or is patient data comprising sex, body mass index or time of a menstrual cycle or a combination of the ambient data and the patient data ([0012] “The wearable device can further comprise a second temperature sensor coupled to the circuit board and spaced away from the first temperature sensor by a first distance, the second temperature sensor configured to measure an ambient temperature outside an interior of the housing.”).
Regarding claim 14, Forrest teaches the method of claim 13, wherein the contextual data further comprises a status of the patch as covered or not covered ([0072] “the wearable device 10 can include an emitter 133 configured to emit light of one or more wavelengths to indicate a status of the wearable device 10”; [0077] “he housing 40 can include one or more indicators configured to assist in the positioning and/or placement of the battery isolator 18 with respect to the housing 40 during assembly of the wearable device 10”; [0107] “Temperature sensor 150b can be used to measure an ambient temperature, for example, a temperature outside the interior of the housing 40.”; [0108] “the temperature sensor 150b is surrounded by a material in order to isolate the temperature sensor 150b from nearby electrical components and/or to prevent the temperature sensor 150b from being thermally influenced by the temperature of the interior of the housing 40 so that the temperature sensor 150b can better measure ambient temperatures outside the housing 40.; [0108] “The temperature sensor 150b can be configured to generate one or more signals based on received thermal energy, whether from the interior of the housing 40 or from ambient (for example, via the thermal putty 120).”).
Regarding claim 15, Forrest teaches a single-use temperature measurement device for measuring core body temperature of a subject (Abstract, Figs. 1-10B),, comprising:
a flexible, folded substrate comprising a thermal zone on a top surface thereof ([0063] “Substrate 25 can comprise a material configured to allow for removable securement of the wearable device 10 to the user's skin. For example, the substrate 25 can be coated with a high tack, medical-grade adhesive, which when in contact with the subject's skin, is suitable for long-term monitoring”; [0070] “The housing 40 can be rigid or alternatively, flexible. The housing 40 can be made of and/or include thermoplastics and/or thermosetting polymers.”);
an electrically isolated annular copper ring disposed on the top surface of the flexible, folded substrate to surround the thermal zone ([0098] “the one or more openings 159 include (for example, are filled with) a thermally conductive material, such as gold and/or copper,” (The copper is disposed in a ring around the opening); “the one or more openings 159 (and/or an array formed by a plurality of the openings 159) can align with the temperature sensor 150a, the thermal paste 173, the probe 140 (for example, an axis extending through a height of the probe 140), the slot 132 of the mounting frame 130, and/or the opening 55 of substrate 50”);
a first semi-circular copper component and a second semi-circular copper component both disposed on the top surface of the flexible, folded substrate inside the electrically isolated annular copper ring and both electrically isolated therewithin components ([0100] “The thermally conductive pad 155 can be metallic. For example, the thermally conductive pad 155 can include gold and/or cop