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 .
Status of Claims and Rejections
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Claims 1 and 13-20 have been amended.
Claims 1-20 are currently pending.
In light of the claim amendments, the Section 101 rejection has been withdrawn.
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.
Claims 1-20 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 1 recites “determining a first temperature reading of a first portion of the ear canal” and “determining a second temperature reading of a second portion of the ear canal that differs from the first portion.” Claim 1 subsequently recites “detecting at least a threshold temperature differential as between the first temperature and the second temperature.”
First, the subsequent temperature limitations lack the term “reading,” thereby making it unclear if, for example, “first temperature” is different from “first temperature reading.” Second, the phrase “at least a threshold temperature differential” is awkward as it is not clear if “at least” modifies “threshold” (i.e., the difference is at least greater than a threshold value) or if “at least” modifies the detection of a threshold temperature differential (e.g., detect a threshold temperature differential among other possible features that can be detected). Third, “as between” is awkward and further obfuscates the already unclear references to first and second temperatures. Lastly, “detecting” is an imprecise term for encompassing a mathematical calculation as described in Applicant’s disclosure.
The cumulative effect of the issues described above render claim 1 indefinite.
Claim 13 has similar claim recitations and is unclear for the same reasons as discussed above with respect to claim 1.
For the purpose of a compact prosecution, Examiner is interpreting the relevant claim language of claims 1 and 13 as follows:
…determining a first temperature reading of a first portion of the ear canal;
determining a second temperature reading of a second portion of the ear canal that differs from the first portion;
determining a temperature differential between the first temperature reading and the second temperature reading;
determining that the temperature differential exceeds a predetermined threshold…
In light of the above interpretation, the last recitation in claim 1 is being interpreted as follows: “…detecting, based at least in part on the determination that the temperature differential exceeds the predetermined threshold and the graphical representation, the signs of infection in the ear canal of the patient.”
In addition to the above, claim 13 recites “mapping a temperature distribution of temperatures of a tympanic membrane utilizing the sensor data and based at least in part on the at least the threshold temperature differential as detected….”
First, the “based at least in part on the at least the threshold…” is awkward and unclear. Second, the mapping limitation appears to condition the mapping on the threshold temperature differential being detected. However, Applicant’s disclosure does not describe triggering or otherwise conditioning the mapping of temperature distributions based on whether a temperature differential exceeds a threshold. Instead, the temperature distributions are mapped and then the temperature differential is compared to a threshold. (see, e.g., [0105] and [0106] of Applicant’s disclosure). This interpretation is consistent with claim 1, which appears to otherwise mirror claim 13.
For the above reasons, the last recitations in claim 13 are being interpreted as follows:
mapping a temperature distribution of temperatures of a tympanic membrane utilizing the sensor data;
detecting the acute otitis media based at least in part on the temperature distribution and on the determination that the temperature differential exceeds the predetermined threshold; and
displaying to the patient, using the display unit, an indication that the acute otitis media has been detected.
Claims 2-12 and 14-20 depend directly or indirectly from claims 1 and 13 and are therefore indefinite based on their dependency.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 6, 13, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”).
HARR teaches an electronic tympanic thermometer that includes a probe and an electromagnetic radiation sensor. (Abstract). “An electromagnetic radiation sensor at the probe senses electromagnetic radiation within the orifice of the subject. The electromagnetic radiation sensor generates data indicative of both the temperature of the subject and one or more anatomical images of the subject.” (Abstract). The electromagnetic radiation sensor can be an image sensor that “comprises an infrared (IR) image sensor for generating image data relating to the sensed IR radiation emitting from the inside of the patient's ear (e.g., IR radiation emitting from the patient's tympanic membrane).” ([0026]). “The thermometer is also useful to the practitioner for identifying possible ear infections.” ([0045]).
With respect to claim 1 (and in light of the Section 112(b) rejection), HARR teaches an ear infection detection device. The tympanic thermometer includes an image sensor that “comprises an infrared (IR) image sensor for generating image data relating to the sensed IR radiation emitting from the inside of the patient's ear (e.g., IR radiation emitting from the patient's tympanic membrane).” ([0026]). The device can generate “information…useful for providing evidence of infections or other medical conditions.” ([0039]). The ear infection detection device comprising:
one or more processors (“controller, including a processor” ([0006]; see also controller 132 at [0034]-[0035]);
a sensor configured to generate sensor data [i.e., temperature values] (“The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041]); See
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also [0044] in which an IR image may overlay a visual image. As such, the IR image must extend along two dimensions (i.e., not a single spot) and must necessarily include temperatures at multiple points along the membrane in order to indicate hotter and cooler areas.);
a housing configured to be held by a user (see, e.g., tympanic thermometer 20 and handle 21 in Figure 2 having a housing) and to contain the one or more processors ([0035]) and the sensor ([0032]-[0033]), the housing having a distal end configured for at least partial insertion into the ear canal (see, e.g., probe 22 in Figure 2);
non-transitory computer-readable media (see, e.g., [0035] - “controller 132” can be “programmed”; and “software” at [0047]; see also [0048]-[0050]), housed in the housing ([0035]), storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (see, e.g., [0047]-[0050]):
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receiving the sensor data from the sensor, the sensor data comprising temperature information of the ear canal of a patient ([0006]: “The infrared radiation temperature sensor is configured to generate temperature data indicative of the temperature of the subject.”);
calculating, based on the sensor data, temperature variations across the ear canal indicative of potential infection. “[A] temperature image indicating a temperature computed by the thermometer may be displayed on the display, such as superimposed over the image(s) of the inside of the subject's ear.” ([0025]). HARR necessarily determines at least first and second temperature readings in order to calculate the temperature variations shown in the heatmap-like image. See Figure 12 in which the IR image is essentially a two-dimensional heat map indicating hotter and cooler areas within the image. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12)”);
generating a graphical representation of the temperature variations, the graphical representation displaying temperature trends or hotspots associated with infection. The IR image is a graphical representation that displays a hotspot. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041])).
However, HARR does not explicitly teach that the infection is acute otitis media or that the device is also configured for detecting, based at least in part on the graphical representation, the signs of infection in the ear canal of the patient. Nevertheless, HARR does teach that “[t]he thermometer is also useful to the practitioner for identifying possible ear infections.” ([0045]) and that the device can generate “information…useful for providing evidence of infections or other medical conditions.” ([0039]). The useful information includes a temperature distribution. ([0041] and Figure 12).
In the same field of endeavor, HAIKOU teaches a “device for detecting abnormal part of auricle based on infrared data.” (p.1, line 10). HAIKOU notes that “[i]mproper care [of the auricle] may lead to various inflammations such as otitis media and auricular perichondritis.” (p.1, lines 23-24). HAIKOU teaches collecting infrared radiation from the middle ear using medical infrared thermal imaging. “Medical infrared thermal imaging passively receives the metabolic heat source of human tissue cells.” (p.5, lines 1-2). HAIKOU then analyzes medical images to determine “whether the bone structure of the auricle is normal and whether the soft tissue density of the auricle is normal, so as to diagnose the abnormality of the patient's auricle.” (i.e., diagnosing acute otitis media) (see p.2, lines 4-5 and lines 40-42).
More specifically, HAIKOU analyzes the “temperature distribution shape.” (p.5, lines 19-20). According to HAIKOU, “[t]he temperature distribution of a normal human body has certain stability and characteristics. The temperature of different parts of the body is different, forming different thermal fields. The far-infrared surface imaging of a normal person should show that the thermal structure of the body surface is uniform. The appearance of abnormal temperature zones indicates that the thermal structure of the patient's auricle is uneven and the patient's auricle is abnormal./ The abnormal temperature zone indicates a high temperature zone. The color of the abnormal temperature zone is represented by red, that is, red represents a high temperature zone. Yellow, green and blue represent low temperature zones.” (p.5, lines 33-41).
After identifying the particular part that is abnormal by analyzing the temperature distribution shape, HAIKOU teaches using additional medical imaging (e.g., CT) to diagnose the extend of the disease. “Combined with clinical practice, it can intelligently and accurately diagnose diseases and infer the nature and extent of diseases.” (p.5, lines 3-5).
Accordingly, HAIKOU teaches detecting, based at least in part on the sensor data and the graphical representation, the signs of infection in the ear canal of the patient.
It would have been obvious to one having ordinary skill in the art at the time of filing to configure HARR’s controller to analyze the temperature distribution for detecting acute otitis media, as taught by HAIKOU, and to display the temperature distribution in a manner that highlights the areas indicative of ear infections (i.e., the acute otitis media). One of ordinary skill in the art would have been motivated to include this analysis and highlight the areas because HARR suggests that the device can provide evidence of ear infections and acute otitis media is one such ear infection. Moreover, a doctor would expect the temperature distribution to be displayed in a manner that highlights the areas indicative of ear infections, which a heat map would show. There would have been a reasonable expectation of success as HARR already provides a temperature distribution and HAIKOU demonstrates that it is possible to analyze and identify the areas that are indicative of acute otitis media.
Alternative to HAIKOU, in the same field of endeavor, RUPPERSBERG teaches an ear inspection device that includes an infrared sensor unit (Title and Abstract) and that, in some embodiments, is capable of “diagnosing an ear disease,” which may include “reliably diagnosing e.g. an inflammation of the eardrum without the need of assistance of a skilled physician.” RUPPSBERGER teaches that “[l]ocal inflammations also lead to a raise in temperature at the site of inflammation.” ([0028]). “Inflammation of the eardrum may suggest e.g. an (bacterial/viral) infection.” ([0068]). One infection that RUPPERSBERG is particularly concerned about is acute otitis media (OM). ([0124]; see also claim 21).
To detect the inflammation, RUPPERSBERG teaches using a sensor array that is similar to NEWMAN. “The infrared sensor unit of the ear inspection device according to the present invention may comprise a plurality of infrared sensor elements for detecting infrared radiation from different regions of the ear.” ([0050]). The infrared sensor unit may comprise “an infrared camera configured for capturing images based on radiation in the infrared range from the subject's ear. This allows for obtaining a two-dimensional image of the temperature distribution in the area observed by the infrared camera.” ([0051]). RUPPERSBERG also describes technology enabling thermal images of sufficient resolution. “Therefore, wafer-level imaging technology allows obtaining images (of the temperature distribution and/or of light in the visual range) of “sufficient” resolution of the eardrum, e.g. images of 250 pixels×250 pixels, with a footprint of the camera (including a lens) of only about 1 mm×1 mm or even smaller.” ([0052]).
It would have been obvious to one having ordinary skill in the art at the time of filing to configure the NEWMAN device for detecting and displaying temperature distributions of the tympanic membrane. One having ordinary skill in the art would have been motivated to image and display the temperature distributions so that abnormal heat signatures could be identified, which would aid in the diagnosis of ear infections, including acute otitis media, as taught in RUPPERSBERG. There would have been a reasonable expectation of success as the NEWMAN device is already capable of detecting temperature distributions of the tympanic membrane using the IR sensor array and RUPPERSBERG teaches that elevated temperature is a sign of infection.
However, HARR does not explicitly teach that the IR image sensor is a grid array infrared sensor configured to generate sensor data, wherein the grid array infrared sensor is further configured to produce an electrical voltage corresponding to detected infrared radiation, the electrical voltage being used to calculate temperature values for detecting signs of infection in an ear canal. Nevertheless, it is clear that HARR’s sensor produces a two-dimensional IR image that is capable of showing a temperature distribution. (see, e.g., Figure 12) and HARR suggests that the sensor can be a thermopile. ([0004] and [0033]).
KIMURA teaches “a temperature sensor device using a thermopile…” (Abstract). “[A] thermopile is generally used for a high-accuracy infrared temperature sensor such as an aural thermometer….” ([0004]). “[T]he temperature sensor device of the present invention is used as an infrared sensor device and applied to a radiation thermometer. The device can measure the temperature of the object under test. And also it can be used as an image sensor to measure the temperature distribution and visualize its image. And also it can be used as an aural thermometer to measure tympanic temperature and display body temperature.” ([0052]). Figure 25 of KIMURA is shown here.
More specifically, KIMURA teaches a grid array infrared sensor configured to generate sensor data ([0239]: “[T]he image sensor having an light receiving array 70 wherein each infrared receiving parts 7 as pixels of the sensor are placed in the form of two-dimensional array on a x-y plane.”), wherein the grid array infrared sensor is further configured to produce an electrical voltage corresponding to detected infrared radiation, the electrical voltage being used to calculate temperature values for detecting signs of infection in an ear canal ([0003]: “A thermopile is a thermo-type sensor, and also a temperature difference sensor, which is configured to obtain greater thermoelectric power, which is a sensor output, for a given temperature difference ΔT by connecting a plurality of thermocouples in series.” NOTE: It is known that thermocouples in series generate a voltage signal. According to McGraw-Hill Dictionary of Scientific and Technical Terms, a “thermopile” is “[a]n array of thermocouples connected either in series to give higher voltage output or in parallel to give higher current output, used for measuring temperature….” (“thermopile.” McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed., 2003).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine KIMURA’s teachings of a thermopile sensor array with the HARR device (or substitute the generic thermopile of HARR with a thermopile sensor array as taught in KIMURA). HARR describes using an infrared image sensor that can produce an IR image and suggests that a thermopile can be used. (see, e.g., [0004] and [0033] of HARR). KIMURA teaches a thermopile sensor array (i.e., grid pattern) that is capable of producing an IR image “to measure the
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temperature distribution and visualize its image.” ([0052] of KIMURA). One of ordinary skill in the art could have combined the HARR device and the KIMURA thermopile sensor array using known methods (or substituted the generic thermopile of HARR with the thermopile sensor array of KIMURA using known methods) and each element would perform the same function as it does separately. Moreover, one of ordinary skill in the art would have recognized that the results of the combination/substitution were predictable.
HARR does not teach determining a first temperature reading of a first portion of the ear canal and determining a second temperature reading of a different second portion of the ear canal, detecting at least a threshold temperature differential between the first temperature and the second temperature and detecting, based at least in part on detecting the at least the threshold temperature differential, the signs of infection in the ear canal of the patient. Nonetheless, HARR and HAIKOU/RUPPERSBERG are clearly concerned with detecting infection within the ear canal based on the temperature data and temperature variations as discussed above.
DACOSTA teaches methods for thermal imaging a wound and determining an infection is present based on a temperature differential between different areas. (Abstract and claims 1 and 10). DACOSTA teaches that a temperature difference between different areas may indicate an infection. “As shown in the example of FIGS. 5A and 5B, an indication that the test point is warmer than the reference point may indicate the potential presence of inflammation or infection.” ([0047]). Notably, the test point and the reference point are two points within the same “target and surrounding area.” (see, e.g., [0008]-[0009]). “The example outputs are shown as color images output by a multi-modal imaging device in accordance with the present disclosure, where the output is a thermal map of the imaged target and surrounding area, with a user selected reference point and user selected test point applied to the thermal map and a temperature differential between the two points being indicated numerically as well as by a relative color scale….” ([0067]).
Accordingly, DACOSTA teaches determining a first temperature reading of a first portion of the ear canal and determining a second temperature reading of a second portion of the ear canal that differs from the first portion and detecting at least a threshold temperature differential between the first temperature and the second temperature.
Similar to HARR, DACOSTA teaches using a color scale to represent the different temperatures. “FIG. 4B is an example of an alternative embodiment of a relative color scale showing colors representing a difference in temperature between a reference point and a test point selected by a user on an image captured with the multi-modal imaging device of the present disclosure.” ([0025]). Also similar to HARR, the standard images and thermal images in DACOSTA may be overlapped in manner that conveys information about the temperature differences. “The indication of the temperature difference between the user selected
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reference point and the user selected test point can include an overlay on any one of the captured images, a thermal map, or other markings that indicate to a viewer the difference in temperature between the user selected reference point and the user selected test point.” ([0046]).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the HARR thermometer to determine a temperature differential between two different areas of the ear canal and, in order to detect signs of infection, determine that the temperature differential exceeds a threshold value. HARR and HAIKOU/RUPPERSBERG teach that temperature data can be used to diagnose an ear infection, (see, e.g., [0045] of HARR), and that differences in temperature can be indicative of infection. DACOSTA teaches that an excessive temperature differential between two points is indicative of an infection. One of ordinary skill in the art would have been motivated to (a) determine a temperature differential between two points or areas of the ear canal to determine whether an infection exists and (b) indicate whether that temperature differential is indicative of an infection by mapping the distribution while indicating the excessive temperature differential. There would have been a reasonable expectation of success as DACOSTA teaches that temperature differentials can be determined using thermal images and can identify infections.
With respect to claim 2, as discussed above, KIMURA teaches a grid array infrared sensor that includes multiple individual sensors arranged in a spatially distributed grid pattern. “[T]he image sensor having an light receiving array 70 wherein each infrared receiving parts 7 as pixels of the sensor are placed in the form of two-dimensional array on a x-y plane.” ([0239]). See also Figure 25. It would have been obvious to one having ordinary skill in the art at the time of filing to combine the KIMURA thermopile sensor array with the HARR device (or substitute the generic thermopile of HARR with the thermopile sensor array of KIMURA) for the same reasons as set forth above in claim 1.
With respect to claim 3, as discussed above, KIMURA teaches a grid array infrared sensor that includes multiple infrared sensors arranged in a x-y grid pattern. “[T]he image sensor having an light receiving array 70 wherein each infrared receiving parts 7 as pixels of the sensor are placed in the form of two-dimensional array on a x-y plane.” ([0239]). See also Figure 25. It would have been obvious to one having ordinary skill in the art at the time of filing to combine the KIMURA thermopile sensor array with the HARR device (or substitute the generic thermopile of HARR with the thermopile sensor array of KIMURA) for the same reasons as set forth above in claim 1.
NOTE: The claim limitation configured to mitigate precise positioning within the ear canal does not structurally define the sensor. (see MPEP 2114: “A claim containing a recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus if the prior art apparatus teaches all the structural limitations of the claim.”). KIMURA has the claimed x-y grid pattern (i.e., structural limitations) and is inherently capable of mitigating a need for precise positioning within the ear canal.
With respect to claim 5, HARR teaches wherein the acute otitis media ear infection detection device is a handheld device. (see Figure 2 of HARR; “Referring to FIGS. 1-5, tympanic thermometer 20 includes a handle 21 (FIGS. 1 and 2), and probe, generally indicated at 22, extending outward distally from the handle.” ([0029])).
With respect to claim 6, HARR teaches wherein the grid array infrared sensor is configured to adapt to variations in ambient temperature to maintain accuracy in infection detection. HARR teaches detecting the temperature of the “sensor can” that is warmed by a heat flux from the ear. “In the illustrated embodiment, heat from, for example, the ear of the subject is transferred from probe cover 32 to nozzle 100 to the base 126 of the can 102 via a path of heat flux HF. The path of heat flux heats the can 102 in order to reduce the temperature gradient across tip 116.” ([0036]). HARR then teaches detecting the temperature of the sensor can. “The reference temperature sensor 124 is adapted to detect the temperature of the base 126 of the sensor can 102”). HARR teaches that calibration/compensation is based on this reference temperature. “[T]he reference temperature data generated by the reference temperature sensor 124 is used (e.g., analyzed) by the controller 132 to adjust (e.g., calibrate and/or compensate) the temperature data generated by the temperature sensor 122 in order to compute the temperature of the subject.” ([0037]). Accordingly, HARR teaches that the sensor is configured to adapt to variations in ambient temperature to maintain accuracy in infection detection.
With respect to claim 13 (and in light of the Section 112(b) rejection), HARR teaches a handheld infrared temperature sensing device for diagnosing an infection. The tympanic thermometer includes “[an image sensor that “comprises an infrared (IR) image sensor for generating image data relating to the sensed IR radiation emitting from the inside of the patient's ear (e.g., IR radiation emitting from the patient's tympanic membrane).” ([0026]). The device can generate “information…useful for providing evidence of infections or other medical conditions.” ([0039]). The infrared temperature sensing device comprising:
one or more processors (“controller, including a processor” ([0006]);
a sensor configured to measure tympanic temperatures of a patient (“The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041]); See also [0044] in which an IR image may overlay a visual image. As such, the IR image must extend along two dimensions (i.e., not a single spot) and must necessarily include temperatures at multiple points along the membrane in order to indicate hotter and cooler areas.);
a housing configured to be held by a user (see, e.g., tympanic thermometer 20 and handle 21 in Figure 2 having a housing) and to contain the one or more processors ([0035]) and the sensor ([0032]-[0033]), the housing having a distal end configured for at least partial insertion into the ear canal (see, e.g., probe 22 in Figure 2);
a display unit (see Figure 6, “display 30”); and
non-transitory computer-readable media (see, e.g., [0035] - “controller 132” can be “programmed”; and “software” at [0047]; see also [0048]-[0050]), housed in the housing ([0035]), storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (see, e.g., [0047]-[0050]):
receiving sensor data from the sensor array ([0006]: “The infrared radiation temperature sensor is configured to generate temperature data indicative of the temperature of the subject.”);
mapping a temperature distribution of temperatures of a tympanic membrane utilizing the sensor data (As shown in Figure 12, the IR image is essentially a two-dimensions heat map indicating hotter and cooler areas within the image. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12)”) and based at least in part on the at least the temperature differential as detected (HARR necessarily determines differences in temperature in order to calculate the temperature variations shown in the heatmap-like image. See Figure 12 in which the IR image is essentially a two-dimensional heat map indicating hotter and cooler areas within the image.);
displaying, using the display unit and to the patient, an indication that the infection has been detected (Due to the heat-map nature of the IR image that is displayed, the areas of abnormal heat indicative of ear infections are highlighted. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041])).
However, HARR does not explicitly teach that the infection is acute otitis media or that the device is also configured for detecting the acute otitis media based at least in part on the temperature distribution. Nevertheless, HARR does teach that “[t]he thermometer is also useful to the practitioner for identifying possible ear infections.” ([0045]) and that the device can generate “information…useful for providing evidence of infections or other medical conditions.” ([0039]). The useful information includes a temperature distribution (i.e., temperature variations). ([0041] and Figure 12).
In the same field of endeavor, HAIKOU teaches a “device for detecting abnormal part of auricle based on infrared data.” (p.1, line 10). HAIKOU notes that “[i]mproper care [of the auricle] may lead to various inflammations such as otitis media and auricular perichondritis.” (p.1, lines 23-24). HAIKOU teaches collecting infrared radiation from the middle ear using medical infrared thermal imaging. “Medical infrared thermal imaging passively receives the metabolic heat source of human tissue cells.” (p.5, lines 1-2). HAIKOU then analyzes medical images to determine “whether the bone structure of the auricle is normal and whether the soft tissue density of the auricle is normal, so as to diagnose the abnormality of the patient's auricle.” (i.e., diagnosing acute otitis media) (see p.2, lines 4-5 and lines 40-42).
More specifically, HAIKOU analyzes the “temperature distribution shape.” (p.5, lines 19-20). According to HAIKOU, “[t]he temperature distribution of a normal human body has certain stability and characteristics. The temperature of different parts of the body is different, forming different thermal fields. The far-infrared surface imaging of a normal person should show that the thermal structure of the body surface is uniform. The appearance of abnormal temperature zones indicates that the thermal structure of the patient's auricle is uneven and the patient's auricle is abnormal./ The abnormal temperature zone indicates a high temperature zone. The color of the abnormal temperature zone is represented by red, that is, red represents a high temperature zone. Yellow, green and blue represent low temperature zones.” (p.5, lines 33-41).
After identifying the particular part that is abnormal by analyzing the temperature distribution shape, HAIKOU teaches using additional medical imaging (e.g., CT) to diagnose the extend of the disease. “Combined with clinical practice, it can intelligently and accurately diagnose diseases and infer the nature and extent of diseases.” (p.5, lines 3-5).
Accordingly, HAIKOU teaches detecting the acute otitis media based at least in part on the temperature distribution.
It would have been obvious to one having ordinary skill in the art at the time of filing to configure HARR’s controller to analyze the temperature distribution for detecting acute otitis media, as taught by HAIKOU, and to display the temperature distribution in a manner that highlights the areas indicative of ear infections (i.e., the acute otitis media). One of ordinary skill in the art would have been motivated to include this analysis and highlight the areas because HARR suggests that the device can provide evidence of ear infections and acute otitis media is one such ear infection. Moreover, a doctor would expect the temperature distribution to be displayed in a manner that highlights the areas indicative of ear infections, which a heat map would show. There would have been a reasonable expectation of success as HARR already provides a temperature distribution and HAIKOU demonstrates that it is possible to analyze and identify the areas that are indicative of acute otitis media.
Alternative to HAIKOU, in the same field of endeavor, RUPPERSBERG teaches an ear inspection device that includes an infrared sensor unit (Title and Abstract) and that, in some embodiments, is capable of “diagnosing an ear disease,” which may include “reliably diagnosing e.g. an inflammation of the eardrum without the need of assistance of a skilled physician.” RUPPSBERGER teaches that “[l]ocal inflammations also lead to a raise in temperature at the site of inflammation.” ([0028]). “Inflammation of the eardrum may suggest e.g. an (bacterial/viral) infection.” ([0068]). One infection that RUPPERSBERG is particularly concerned about is acute otitis media (OM). ([0124]; see also claim 21).
To detect the inflammation, RUPPERSBERG teaches using a sensor array that is similar to NEWMAN. “The infrared sensor unit of the ear inspection device according to the present invention may comprise a plurality of infrared sensor elements for detecting infrared radiation from different regions of the ear.” ([0050]). The infrared sensor unit may comprise “an infrared camera configured for capturing images based on radiation in the infrared range from the subject's ear. This allows for obtaining a two-dimensional image of the temperature distribution in the area observed by the infrared camera.” ([0051]). RUPPERSBERG also describes technology enabling thermal images of sufficient resolution. “Therefore, wafer-level imaging technology allows obtaining images (of the temperature distribution and/or of light in the visual range) of “sufficient” resolution of the eardrum, e.g. images of 250 pixels×250 pixels, with a footprint of the camera (including a lens) of only about 1 mm×1 mm or even smaller.” ([0052]).
It would have been obvious to one having ordinary skill in the art at the time of filing to configure the NEWMAN device for detecting and displaying temperature distributions of the tympanic membrane. One having ordinary skill in the art would have been motivated to image and display the temperature distributions so that abnormal heat signatures could be identified, which would aid in the diagnosis of ear infections, including acute otitis media, as taught in RUPPERSBERG. There would have been a reasonable expectation of success as the NEWMAN device is already capable of detecting temperature distributions of the tympanic membrane using the IR sensor array and RUPPERSBERG teaches that elevated temperature is a sign of infection.
However, neither HARR nor HAIKOU/RUPPERSBERG explicitly teach that the IR image sensor is a sensor array. Nevertheless, it is clear that HARR’s sensor produces a two-dimensional IR image that is capable of showing a temperature distribution. (see, e.g., Figure 12).
KIMURA teaches “a temperature sensor device using a thermopile…” (Abstract). “[A] thermopile is generally used for a high-accuracy infrared temperature sensor such as an aural thermometer….” ([0004]). “[T]he temperature sensor device of the present invention is used as an infrared sensor device and applied to a radiation thermometer. The device can measure the temperature of the object under test. And also it can be used as an image sensor to measure the temperature distribution and visualize its image. And also it can be used as an aural thermometer to measure tympanic temperature and display body temperature.” ([0052]). Figure 25 of KIMURA is shown here.
More specifically, KIMURA teaches a sensor array ([0239]: “[T]he image sensor having an light receiving array 70 wherein each infrared receiving parts 7 as pixels of the sensor are placed in the form of two-dimensional array on a x-y plane.”).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine KIMURA’s teachings of a sensor array with the HARR device (or substitute the generic thermopile of HARR with a thermopile sensor array as taught in KIMURA). HARR describes using an infrared image sensor that can produce an IR image and suggests that a thermopile can be used. (see, e.g., [0004] and [0033] of HARR). KIMURA teaches a thermopile sensor array that is capable of producing an IR image “to measure the temperature distribution and visualize its image.” ([0052] of KIMURA). One of ordinary skill in the art could have combined the HARR device and the KIMURA thermopile sensor array using known methods (or substituted the generic thermopile of HARR with the thermopile sensor array of KIMURA using known methods) and each element would perform the same function as it does separately. Moreover, one of ordinary skill in the art would have recognized that the results of the combination/substitution were predictable.
NOTE: The claim limitation that the indication is displayed to the patient does not structurally define the sensor. (see MPEP 2114: “A claim containing a recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus if the prior art apparatus teaches all the structural limitations of the claim.”). The prior art device has a display and is inherently capable of displaying to the patient or any other person.
Neither HARR nor HAIKOU/RUPPERSBERG teach determining a first temperature reading of a first portion of the ear canal or determining a second temperature reading of a second portion of the ear canal that differs from the first portion. However, HARR necessarily calculates temperature variations in order to display a heatmap. See Figure 12 in which the IR image is essentially a two-dimensional heat map indicating hotter and cooler areas within the image. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12)”). Moreover, HAIKOU/RUPPERSBERG uses temperature distributions to determine an ear infection.
Moreover, neither HARR nor HAIKOU/RUPPERSBERG teach detecting at least a threshold temperature differential between the first temperature and the second temperature and mapping a temperature distribution based at least in part on the at least the threshold temperature differential as detected. Nonetheless, HARR and HAIKOU/RUPPERSBERG are clearly concerned with detecting infection within the ear canal based on the temperature data and temperature variations as discussed above.
DACOSTA teaches methods for thermal imaging a wound and determining an infection is present based on a temperature differential between different areas. (Abstract and claims 1 and 10). DACOSTA teaches that a temperature difference between different areas may indicate an infection. “As shown in the example of FIGS. 5A and 5B, an indication that the test point is warmer than the reference point may indicate the potential presence of inflammation or infection.” ([0047]). Notably, the test point and the reference point are two points within the same “target and surrounding area.” (see, e.g., [0008]-[0009]). “The example outputs are shown as color images output by a multi-modal imaging device in accordance with the present disclosure, where the output is a thermal map of the imaged target and surrounding area, with a user selected reference point and user selected test point applied to the thermal map and a temperature differential between the two points being indicated numerically as well as by a relative color scale….” ([0067]).
Accordingly, DACOSTA teaches determining a first temperature reading of a first portion of the ear canal and determining a second temperature reading of a second portion of the ear canal that differs from the first portion and detecting at least a threshold temperature differential between the first temperature and the second temperature.
Similar to HARR, DACOSTA teaches using a color scale to represent the different temperatures. “FIG. 4B is an example of an alternative embodiment of a relative color scale showing colors representing a difference in temperature between a reference point and a test point selected by a user on an image captured with the multi-modal imaging device of the present disclosure.” ([0025]). Also similar to HARR, the standard images and thermal images in DACOSTA may be overlapped in manner that conveys information about the temperature differences. “The indication of the temperature difference between the user selected reference point and the user selected test point can include an overlay on any one of the captured images, a thermal map, or other markings that indicate to a viewer the difference in temperature between the user selected reference point and the user selected test point.” ([0046]).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the HARR thermometer to determine a temperature differential between two different areas of the ear canal and, in order to detect signs of infection, determine that the temperature differential exceeds a threshold value. HARR and HAIKOU/RUPPERSBERG teach that temperature data can be used to diagnose an ear infection, (see, e.g., [0045] of HARR), and that differences in temperature can be indicative of infection. DACOSTA teaches that an excessive temperature differential between two points is indicative of an infection. One of ordinary skill in the art would have been motivated to (a) determine a temperature differential between two points or areas of the ear canal to determine whether an infection exists and (b) indicate whether that temperature differential is indicative of an infection by mapping the distribution while indicating the excessive temperature differential. There would have been a reasonable expectation of success as DACOSTA teaches that temperature differentials can be determined using thermal images and can identify infections.
With respect to claim 15, as discussed above, KIMURA teaches a sensor array that includes multiple individual sensors arranged in a spatially distributed grid pattern. “[T]he image sensor having an light receiving array 70 wherein each infrared receiving parts 7 as pixels of the sensor are placed in the form of two-dimensional array on a x-y plane.” ([0239]). See also Figure 25. It would have been obvious to one having ordinary skill in the art at the time of filing to combine the KIMURA thermopile sensor array with the HARR device (or substitute the generic thermopile of HARR with the thermopile sensor array of KIMURA) for the same reasons as set forth above in claim 1.
With respect to claim 16, HARR teaches wherein the sensor array is configured to adapt to variations in ambient temperature to maintain accuracy in infection detection. HARR teaches detecting the temperature of the “sensor can” that is warmed by a heat flux from the ear. “In the illustrated embodiment, heat from, for example, the ear of the subject is transferred from probe cover 32 to nozzle 100 to the base 126 of the can 102 via a path of heat flux HF. The path of heat flux heats the can 102 in order to reduce the temperature gradient across tip 116.” ([0036]). HARR then teaches detecting the temperature of the sensor can. “The reference temperature sensor 124 is adapted to detect the temperature of the base 126 of the sensor can 102”). HARR teaches that calibration/compensation is based on this reference temperature. “[T]he reference temperature data generated by the reference temperature sensor 124 is used (e.g., analyzed) by the controller 132 to adjust (e.g., calibrate and/or compensate) the temperature data generated by the temperature sensor 122 in order to compute the temperature of the subject.” ([0037]). Accordingly, HARR teaches that the sensor is configured to adapt to variations in ambient temperature to maintain accuracy in infection detection.
Claims 4, 7, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 and claim 13 above, and further in view of U.S. Patent Appl. Publ. No. 2015/0065803 A1 (hereinafter “DOUGLAS”).
With respect to claim 4, HARR teaches a display unit (e.g., display 30 at [0035]) but does not explicitly teach a user interface configured to provide real-time infection detection readings. However, HARR does teach that thermal videos generated on the display “may be used to direct a user to the hottest part of the subject’s tympanic membrane….” ([0041]).
In the same field of endeavor, DOUGLAS teaches various embodiments for acquiring images of the tympanic membrane. ([0014]). “These apparatus and methods include or be configured for use with an otoscope that may be operated by a physician (or other medical specialist) and in particular by an untrained, e.g., non-medical specialist, such as a parent or even the patient. In some aspects, the methods and apparatuses described herein assist the subject in taking images, and particularly images of the TM, including confirming that a TM (or part/region of a TM) has been identified.” ([0166]). DOUGLAS’s embodiments may configure “for use with a home or clinical device that includes an otoscope (e.g., speculum, lens/lenses, and video/image capture capability).
DOUGLAS also teaches an “Otoscopic Exam Guidance System.” ([0296]-[0297]). In one example embodiment, “an otoscope system may include an otoscope component, including one or more lenses and a speculum for insertion into a patient's ear. The otoscope component may be coupled to a display device and one or more processors for receiving and analyzing images from the otoscope. In particular, the systems described herein may include an otoscope component that is coupled (directly or indirectly) with a mobile telecommunications device such as a smartphone; the smartphone acts as both the display device and as the processor for receiving, displaying and processing the image. The subject using/controlling the otoscope may be guided by the smartphone, including by observing the display screen of the smartphone, which may display the image of the ear (e.g., ear canal) when the otoscope is inserted into the patient's ear.”
DOUGLAS further teaches embodiments that are configured “for use with a home or clinical device that includes an otoscope (e.g., speculum, lens/lenses, and video/image capture capability). Images may be acquired until the method/apparatus indicates, e.g., visually or audibly, that an adequate image has been taken. The image(s) may then be stored, transmitted, and/or analyzed. For example, stored images may be transmitted to a medical provider for further analysis, or to a third-party analysis center.” ([0034]). After acquiring the images, “[t]he apparatus may also transmit one or more images and/or additional (patient-specific) information to a third party (e.g., database, electronic medical record, physician/health care provider, etc.).” ([0167]). Mobile devices could implement a software for performing the imaging and analysis. (see, e.g., [0446]).
It would have been obvious to one having ordinary skill in the art at the time of filing to incorporate a device for acquiring temperature variations, as taught in HARR, into a larger interconnected device/system, as taught in DOUGLAS, that includes a mobile device having a display unit and a user interface that is configured to provide real-time infection detection readings. One of ordinary skill in the art would have been motivated to include the mobile device as it would enable and guide a healthcare professional or patient in acquiring temperature distributions (i.e., IR images) of the tympanic membrane. More specifically, the temperature variations (i.e., heat-maps or the like) would be displayed to the user as the user acquired them. There would have been a reasonable expectation of success as DOUGLAS teaches that mobile devices with the appropriate software can be used as part of a system to acquire images of the tympanic membrane.
With respect to claim 7, HARR does not explicitly teach that the grid array infrared sensor is integrated into a multi-functional healthcare device operable by a healthcare provider or the patient.
NOTE: Applicant does not define “multi-functional healthcare device.” However, Applicant describes context and capabilities for a multi-functional device. For example, at paragraph [0088] Applicant provides: “The grid array infrared sensor may also be integrated into a multi-functional healthcare device, which may be designed for use by both healthcare providers and patients. This integration may allow the device to serve multiple diagnostic or monitoring purposes beyond ear infection detection…For patients, the device may offer the capability for home monitoring of ear health, potentially enabling earlier detection of infections or tracking the progress of treatment. The versatility of this multi-functional design may enhance the device's value in various healthcare contexts, from professional clinical use to telemedicine applications and patient self-monitoring.”
In the same field of endeavor, DOUGLAS teaches various embodiments for acquiring images of the tympanic membrane. ([0014]). DOUGLAS’s embodiments may configure “for use with a home or clinical device that includes an otoscope (e.g., speculum, lens/lenses, and video/image capture capability). Images may be acquired until the method/apparatus indicates, e.g., visually or audibly, that an adequate image has been taken. The image(s) may then be stored, transmitted, and/or analyzed. For example, stored images may be transmitted to a medical provider for further analysis, or to a third-party analysis center.” ([0034]). After acquiring the images, “[t]he apparatus may also transmit one or more images and/or additional (patient-specific) information to a third party (e.g., database, electronic medical record, physician/health care provider, etc.).” ([0167]). Mobile devices could implement a software for performing the imaging and analysis. (see, e.g., [0446]).
In one example embodiment, “an otoscope system may include an otoscope component, including one or more lenses and a speculum for insertion into a patient's ear. The otoscope component may be coupled to a display device and one or more processors for receiving and analyzing images from the otoscope. In particular, the systems described herein may include an otoscope component that is coupled (directly or indirectly) with a mobile telecommunications device such as a smartphone; the smartphone acts as both the display device and as the processor for receiving, displaying and processing the image. The subject using/controlling the otoscope may be guided by the smartphone, including by observing the display screen of the smartphone, which may display the image of the ear (e.g., ear canal) when the otoscope is inserted into the patient's ear.”
DOUGLAS also teaches tracking treatment progress. “Once a diagnosis is made and an intervention is prescribed, it is helpful to track the progression of the disease via continuous exams and to suggest altering interventions if the disease is progressing or otherwise not improving as rapidly as expected. Methods and software tools can be a powerful aid in this tracking.” ([0265]; see also [0266] describing a method for continuous monitoring). “By enabling more regular and frequent recorded images, the methods and apparatuses described herein may allow a new paradigm of image comparison and analysis. Previously, a user had no good way of seeing what an infection looks like over time. Now, he can submit his image to the systems described herein, find a similar case automatically, and see how that case resolved over a variety of time frames (minutes, hours, days, weeks, etc.). He can also see how similar cases fared with different interventions.” ([0347]).
It would have been obvious to one having ordinary skill in the art at the time of filing to integrate the sensor array into a multi-functional healthcare device configured to be utilized by a healthcare provider or the patient as taught in DOUGLAS. One of ordinary skill in the art would have been motivated to use a mobile device as a multi-functional healthcare device because it would not only enable and guide a healthcare professional or patient in acquiring temperature variations but would also enable continuously monitoring the ear and tracking progress after treatment. There would have been a reasonable expectation of success as DOUGLAS teaches that mobile devices with the appropriate software can be used as part of a system to acquire images of the tympanic membrane.
With respect to claim 17, HARR does not explicitly teach that the sensor array is integrated into a multi-functional healthcare device configured to be utilized by the patient.
NOTE: Applicant does not define “multi-functional healthcare device.” However, Applicant describes context and capabilities for a multi-functional device. For example, at paragraph [0088] Applicant provides: “The grid array infrared sensor may also be integrated into a multi-functional healthcare device, which may be designed for use by both healthcare providers and patients. This integration may allow the device to serve multiple diagnostic or monitoring purposes beyond ear infection detection…For patients, the device may offer the capability for home monitoring of ear health, potentially enabling earlier detection of infections or tracking the progress of treatment. The versatility of this multi-functional design may enhance the device's value in various healthcare contexts, from professional clinical use to telemedicine applications and patient self-monitoring.”
In the same field of endeavor, DOUGLAS teaches various embodiments for acquiring images of the tympanic membrane. ([0014]). DOUGLAS’s embodiments may configure “for use with a home or clinical device that includes an otoscope (e.g., speculum, lens/lenses, and video/image capture capability). Images may be acquired until the method/apparatus indicates, e.g., visually or audibly, that an adequate image has been taken. The image(s) may then be stored, transmitted, and/or analyzed. For example, stored images may be transmitted to a medical provider for further analysis, or to a third-party analysis center.” ([0034]). After acquiring the images, “[t]he apparatus may also transmit one or more images and/or additional (patient-specific) information to a third party (e.g., database, electronic medical record, physician/health care provider, etc.).” ([0167]). Mobile devices could implement a software for performing the imaging and analysis. (see, e.g., [0446]).
In one example embodiment, “an otoscope system may include an otoscope component, including one or more lenses and a speculum for insertion into a patient's ear. The otoscope component may be coupled to a display device and one or more processors for receiving and analyzing images from the otoscope. In particular, the systems described herein may include an otoscope component that is coupled (directly or indirectly) with a mobile telecommunications device such as a smartphone; the smartphone acts as both the display device and as the processor for receiving, displaying and processing the image. The subject using/controlling the otoscope may be guided by the smartphone, including by observing the display screen of the smartphone, which may display the image of the ear (e.g., ear canal) when the otoscope is inserted into the patient's ear.”
DOUGLAS also teaches tracking treatment progress. “Once a diagnosis is made and an intervention is prescribed, it is helpful to track the progression of the disease via continuous exams and to suggest altering interventions if the disease is progressing or otherwise not improving as rapidly as expected. Methods and software tools can be a powerful aid in this tracking.” ([0265]; see also [0266] describing a method for continuous monitoring). “By enabling more regular and frequent recorded images, the methods and apparatuses described herein may allow a new paradigm of image comparison and analysis. Previously, a user had no good way of seeing what an infection looks like over time. Now, he can submit his image to the systems described herein, find a similar case automatically, and see how that case resolved over a variety of time frames (minutes, hours, days, weeks, etc.). He can also see how similar cases fared with different interventions.” ([0347]).
It would have been obvious to one having ordinary skill in the art at the time of filing to integrate the sensor array into a multi-functional healthcare device configured to be utilized by the patient as taught in DOUGLAS. One of ordinary skill in the art would have been motivated to use a mobile device as a multi-functional healthcare device because it would not only enable and guide a patient in acquiring temperature variations but would also enable continuously monitoring the ear and tracking progress after treatment. There would have been a reasonable expectation of success as DOUGLAS teaches that mobile devices with the appropriate software can be used as part of a system to acquire images of the tympanic membrane.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 and claim 13 above, and further in view of D6T MEMS Thermal Sensors, A284-E1-03, (2019) (hereinafter “OMRON”).
With respect to claim 8, HARR does not explicitly teach that the operations further comprise: performing self-diagnostic operations configured to detect potential malfunctions of the grid array infrared sensor; detecting, based at least in part on performing the self-diagnostic operations, a malfunction of the grid array infrared sensor; and outputting an alert indicating that the malfunction has been detected.
OMRON is a user manual describes the usage procedures, precautions, and other information regarding D6T-series MEMS Thermal Sensors. (p. 1, Overview). Like KIMURA and HARR, OMRON teaches using a thermopile sensor. “The D6T series of MEMS Thermal Sensors consists of a small circuit board onto which a silicon lens, thermopile sensor, specialized analog circuit, and logic circuit for conversion to a digital temperature value are arranged.” (p.2, Structure).
Data signals from the thermal sensors include PEC or packet error checks. “PEC represents CRC-8 error check data. “This data is appended to the end of communication output. The user can use the PEC value to detect communication errors and improve data reliability.” Moreover, the thermal sensos can determine when a “timeout” has occurred and communicate that to the host system. “When the sensor determines that a communication timeout has occurred, a NACK is returned during a Write access operation. For Read access operations, the read value is set to FFFFh. Using PEC for data checking enables the system to determine that read values are in error. As such, we recommend using PEC data checking.”
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the grid array infrared sensor to perform self-diagnostic operations configured to detect potential malfunctions of the grid array infrared sensor, detect a malfunction of the grid array infrared sensor based on performing the self-diagnostic operations and output an alert indicating that the malfunction has been detected. One would have been motivated to modify the system to include a packet error check that alerts the host system that read values are in error, as taught in OMRON, in order to make one aware of data errors. There would have been a reasonable expectation of success as OMRON teaches thermopile sensors can utilize PECs.
With respect to claim 18, HARR does not explicitly teach that the operations further comprise: performing self-diagnostic operations configured to detect potential malfunctions of the sensor array; detecting, based at least in part on performing the self-diagnostic operations, a malfunction of the sensor array; and outputting an alert indicating that the malfunction has been detected.
OMRON is a user manual describes the usage procedures, precautions, and other information regarding D6T-series MEMS Thermal Sensors. (p. 1, Overview). Like KIMURA and HARR, OMRON teaches using a thermopile sensor. “The D6T series of MEMS Thermal Sensors consists of a small circuit board onto which a silicon lens, thermopile sensor, specialized analog circuit, and logic circuit for conversion to a digital temperature value are arranged.” (p.2, Structure).
Data signals from the thermal sensors include PEC or packet error checks. “PEC represents CRC-8 error check data. “This data is appended to the end of communication output. The user can use the PEC value to detect communication errors and improve data reliability.” Moreover, the thermal sensos can determine when a “timeout” has occurred and communicate that to the host system. “When the sensor determines that a communication timeout has occurred, a NACK is returned during a Write access operation. For Read access operations, the read value is set to FFFFh. Using PEC for data checking enables the system to determine that read values are in error. As such, we recommend using PEC data checking.”
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the grid array infrared sensor to perform self-diagnostic operations configured to detect potential malfunctions of the grid array infrared sensor, detect a malfunction of the grid array infrared sensor based on performing the self-diagnostic operations and output an alert indicating that the malfunction has been detected. One would have been motivated to modify the system to include a packet error check that alerts the host system that read values are in error, as taught in OMRON, in order to make one aware of data errors. There would have been a reasonable expectation of success as OMRON teaches thermopile sensors can utilize PECs.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 and claim 13 above, and further in view of Panasonic’s User Manual for Grid-EYE Evaluation Kit (document last modified 2018) (hereinafter “-----GRID-EYE”).
With respect to claim 9, HARR does not explicitly teach that wherein the grid array infrared sensor is tuned such that energy consumption is minimized and a battery life of a battery of the acute otitis media ear infection detection device is extended.
However, GRID-EYE teaches an evaluation kit that “combines the Panasonic’s state of the art Grid-EYE sensor, Panasonic ‘nanopower’ PAN1740 Bluetooth Smart module and a microcontroller on one PCB.” (p.4, 2.1). The Grid-EYE sensor is an infrared array sensor. GRID-EYE teaches that the sensor can operate in different selectable modes. (p.6, 2.2.1 Grid-EYE Sensor table). The table shows two “selectable” features that can control the amount of power consumed. First, the Grid-EYE sensor has selectable operation modes: Normal, Standby, Sleep. (Id). The table also shows that the different modes consume different amounts of power: 4.5mA for normal, 0.8mA for standby, and 0.2mA for sleep. (Id). Second, the Grid-EYE sensor has selectable frame rates: 1 frame/sec or 10 frames/sec.
It would have been obvious to one having ordinary skill in the art at the time of filing to enable different operating modes and/or select different frame rates so that energy consumption is minimized and a battery life of a battery of the device is extended, as taught in GRID-EYE. One of ordinary skill in the art would have, at the very least, configured the device to return to a standby or sleep mode after periods of time of non-use. Tympanic temperature checks do not occur continuously but intermittently and it would not be necessary to have the device continuously at Normal mode. There would have been a reasonable expectation of success as GRID-EYE teaches that the sensor has selectable modes that can reduce power consumption.
Claims 10, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 and claim 13 above, and further in view of U.S. Patent Appl. Publ. No. 2009/0182526 A1 (hereinafter “QUINN”).
With respect to claim 10, HARR teaches that the operations further comprise: performing thermal mapping of the ear canal and measuring temperature of the ear canal. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041]). However, HARR does not explicitly teach that compensating for presence of earwax or other occlusions based at least in part on the thermal mapping and measuring the temperature.
In the same field of endeavor, QUINN “relates to an Infrared (IR) thermometer including an IR detector configured to provide an IR emission data representative of a temperature of an area of tissue. The IR thermometer also includes one or more secondary sensors configured to provide an IR thermometer positioning data.” (Abstract). QUINN may use the positioning data to suggest “a direction to move the IR thermometer for a substantially optimal IR detector view of the area of tissue.” (Abstract).
QUINN describes various embodiments that use “optical ranging techniques” (see, e.g., [0075]-[0081]). These techniques may “intercept an obstruction 701. An algorithm running in software, such as on a microcomputer 203, can recognize some shorter ranges as obstructions. For example, where one or more ranging measurements are very different from each other, a partial obstruction is a possibility. On detection of an obstruction 701, it is believed that a version of an IR thermometer 100 using optical imaging capability can display an image of the auditory canal including obstruction 701 as shown in FIG. 7B.” ([0086]).
QUINN also teaches that “[a]ny of the aforementioned range determinations can be also be used to determine the amount (percentage) of the tympanic membrane in the field of view of the IR temperature sensor. The temperature read by the IR thermometer (based on one or more measured values from the temperature sensor) can then be adjusted to a value more indicative of the actual tympanic temperature based on the determination of what percentage of the tympanic membrane is in the field of view.” ([0079]).
It would have been obvious to one having ordinary skill in the art to modify the HARR device to compensate for presence of earwax or other occlusions based at least in part on the thermal mapping and measuring the temperature. One would have been motivated to account for the amount (percentage) of the tympanic membrane in the field of view of the IR temperature sensor, as taught by QUINN, because the goal is to acquire temperature readings of the tympanic membrane and not the ear canal walls or obstructions within the ear canal. There would have been a reasonable expectation of success as QUINN teaches techniques that can be used to identify a percentage of the tympanic membrane within the field of view.
With respect to claim 11, HARR does not teach further comprising a secondary sensor configured to detect physical obstructions on the ear canal of the patient.
In the same field of endeavor, QUINN “relates to an Infrared (IR) thermometer including an IR detector configured to provide an IR emission data representative of a temperature of an area of tissue. The IR thermometer also includes one or more secondary sensors configured to provide an IR thermometer positioning data.” (Abstract). QUINN may use the positioning data to suggest “a direction to move the IR thermometer for a substantially optimal IR detector view of the area of tissue.” (Abstract).
QUINN describes various embodiments that use “optical ranging techniques” (see, e.g., [0075]-[0081]). These techniques may “intercept an obstruction 701. An algorithm running in software, such as on a microcomputer 203, can recognize some shorter ranges as obstructions. For example, where one or more ranging measurements are very different from each other, a partial obstruction is a possibility. On detection of an obstruction 701, it is believed that a version of an IR thermometer 100 using optical imaging capability can display an image of the auditory canal including obstruction 701 as shown in FIG. 7B.” ([0086]).
It would have been obvious to one having ordinary skill in the art to modify the HARR device to include an additional sensor that detects physical obstructions on the ear canal of the patient. One would have been motivated to add another sensor to detect obstructions because the goal is to acquire temperature readings of the tympanic membrane, not obstructions within the ear canal. By knowing that an obstruction exists, the use could remove the obstruction or re-position the device. There would have been a reasonable expectation of success as QUINN teaches techniques can be used to identify obstructions.
With respect to claim 19, HARR does not teach further comprising a secondary sensor configured to detect physical obstructions on the ear canal of the patient.
In the same field of endeavor, QUINN “relates to an Infrared (IR) thermometer including an IR detector configured to provide an IR emission data representative of a temperature of an area of tissue. The IR thermometer also includes one or more secondary sensors configured to provide an IR thermometer positioning data.” (Abstract). QUINN may use the positioning data to suggest “a direction to move the IR thermometer for a substantially optimal IR detector view of the area of tissue.” (Abstract).
QUINN describes various embodiments that use “optical ranging techniques” (see, e.g., [0075]-[0081]). These techniques may “intercept an obstruction 701. An algorithm running in software, such as on a microcomputer 203, can recognize some shorter ranges as obstructions. For example, where one or more ranging measurements are very different from each other, a partial obstruction is a possibility. On detection of an obstruction 701, it is believed that a version of an IR thermometer 100 using optical imaging capability can display an image of the auditory canal including obstruction 701 as shown in FIG. 7B.” ([0086]).
It would have been obvious to one having ordinary skill in the art to modify the HARR device to include an additional sensor that detects physical obstructions on the ear canal of the patient. One would have been motivated to add another sensor to detect obstructions because the goal is to acquire temperature readings of the tympanic membrane, not obstructions within the ear canal. By knowing that an obstruction exists, the use could remove the obstruction or re-position the device. There would have been a reasonable expectation of success as QUINN teaches techniques can be used to identify obstructions.
Claims 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 above, and further in view of U.S. Patent No. 10,764,514 (hereinafter “HOEVENAAR”).
With respect to claim 12, HARR teaches identifying ear canal temperatures from the sensor data and generating a thermal map of the ear canal based at least in part on the ear canal temperatures, wherein the thermal map highlights areas of concern. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041]). However, HARR does not explicitly teach identifying infrared data from the sensor data or that the thermal map of the ear canal is based at least in part on the infrared data.
HOEVENAAR teaches systems and computer-implemented methods for controlling a gain state of a thermal camera having an infrared sensor. (Abstract and col. 10, lines 60-63). HOEVENAAR observes that thermal cameras are configured to operate in different gain states. “[A] thermal camera can be configured to operate in either a high-gain state or a low-gain state. The high-gain state may provide a first, high accuracy and support a narrow intra-scene range of temperatures, whereas, the low-gain state may provide a second, lower accuracy but support a higher intra-scene range of temperatures.” (Col. 1, lines 17-22).
Notably, HOEVENAAR describes sensor arrays similar to the ones described in HARR. “Infrared sensor 56 can include an array of thermal detectors and a readout component. As an example, infrared sensor can include an 80×60 array of thermal detectors that is configured to detect infrared radiation in a response wavelength band. Each thermal detector may have an element, such as a microbolometer, whose temperature fluctuates in response to incident flux…As another example, each thermal detector can include a thermopile sensor….” (Col. 10, line 66 to col. 11, line 8).
HOEVENAAR describes adjusting gain states if the sensors are saturated. “When the thermal camera operates in the high-gain state and a scene imaged by the thermal camera includes temperatures above 140° C., those temperatures may saturate sensors of the thermal camera. To view the temperatures above 140° C., the thermal camera can be switched to the low-gain state, so that the temperatures above 140° C. may be more accurately quantified and visualized.” Sensors are saturated when the intensity of IR radiation hitting the sensor is too high.
It would have been obvious to one having ordinary skill in the art to identify infrared data from the sensor data when evaluating the temperature measurements. If the IR sensor is saturated (i.e., the infrared radiation variation is too high), one skilled in the art would be motivated to configure the system to switch to a low gain state, based on the teachings of HOEVENAAR, and then acquire the heat variation. As such, the thermal map would be based, at least in part, on identifying the infrared data on the sensor data. There would be a reasonable expectation of success as HOEVENAAR teaches that thermopile sensors can be configured to adjust gain states if the sensors are saturated.
With respect to claim 20, HARR teaches identifying ear canal temperatures from the sensor data and generating a thermal map of the ear canal based at least in part on the ear canal temperatures, wherein the thermal map indicates areas of concern. “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).” ([0041]). However, HARR does not explicitly teach identifying infrared data from the sensor data or that the thermal map of the ear canal is based at least in part on the infrared data.
HOEVENAAR teaches systems and computer-implemented methods for controlling a gain state of a thermal camera having an infrared sensor. (Abstract and col. 10, lines 60-63). HOEVENAAR observes that thermal cameras are configured to operate in different gain states. “[A] thermal camera can be configured to operate in either a high-gain state or a low-gain state. The high-gain state may provide a first, high accuracy and support a narrow intra-scene range of temperatures, whereas, the low-gain state may provide a second, lower accuracy but support a higher intra-scene range of temperatures.” (Col. 1, lines 17-22).
Notably, HOEVENAAR describes sensor arrays similar to the ones described in HARR. “Infrared sensor 56 can include an array of thermal detectors and a readout component. As an example, infrared sensor can include an 80×60 array of thermal detectors that is configured to detect infrared radiation in a response wavelength band. Each thermal detector may have an element, such as a microbolometer, whose temperature fluctuates in response to incident flux…As another example, each thermal detector can include a thermopile sensor….” (Col. 10, line 66 to col. 11, line 8).
HOEVENAAR describes adjusting gain states if the sensors are saturated. “When the thermal camera operates in the high-gain state and a scene imaged by the thermal camera includes temperatures above 140° C., those temperatures may saturate sensors of the thermal camera. To view the temperatures above 140° C., the thermal camera can be switched to the low-gain state, so that the temperatures above 140° C. may be more accurately quantified and visualized.” Sensors are saturated when the intensity of IR radiation hitting the sensor is too high.
It would have been obvious to one having ordinary skill in the art to identify infrared data from the sensor data when evaluating the temperature measurements. If the IR sensor is saturated (i.e., the infrared radiation variation is too high), one skilled in the art would be motivated to configure the system to switch to a low gain state, based on the teachings of HOEVENAAR, and then acquire the heat variation. As such, the thermal map would be based, at least in part, on identifying the infrared data on the sensor data. There would be a reasonable expectation of success as HOEVENAAR teaches that thermopile sensors can be configured to adjust gain states if the sensors are saturated.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2014/0036953 A1 (hereinafter “KIMURA”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 and claim 13 above, and further in view of U.S. Patent Appl. Publ. No. 2015/0065803 A1 (hereinafter “DOUGLAS”) and Doğan, Hatice Hilal, et al. “Comparison of axillary and tympanic temperature measurements in children diagnosed with acute otitis media.” International Journal of Pediatrics 2016 (hereinafter “DOGAN”).
With respect to claim 14, HARR teaches that the sensor array is further configured to measure the tympanic temperatures from multiple regions of an ear of the patient simultaneously. (see, e.g., [0041]: “The IR image 170 (e.g., thermal video) generated on the display 30, as shown in FIGS. 11 and 12, may be used to direct a user to the hottest part of the subject's tympanic membrane (as indicated by the lightest shaded area in FIGS. 11 and 12).) However, HARR does not explicitly teach that the operations further comprise correlating temperature readings with axillary temperatures such that accuracy of acute otitis media diagnoses is improved.
In the same field of endeavor, DOUGLAS teaches various embodiments for diagnosing otitis media. ([0014]). DOUGLAS’s embodiments may be configured “for use with a home or clinical device that includes an otoscope (e.g., speculum, lens/lenses, and video/image capture capability). Images may be acquired until the method/apparatus indicates, e.g., visually or audibly, that an adequate image has been taken. The image(s) may then be stored, transmitted, and/or analyzed. For example, stored images may be transmitted to a medical provider for further analysis, or to a third-party analysis center.” ([0034]). After acquiring the images, “[t]he apparatus may also transmit one or more images and/or additional (patient-specific) information to a third party (e.g., database, electronic medical record, physician/health care provider, etc.).” ([0167]). Mobile devices could implement a software for performing the imaging and analysis. (see, e.g., [0446]).
DOUGLAS also teaches automatically diagnosing otitis media using machine-learning models and various clinical information of the patient. “Assessments of diagnosis, prognosis, etc., often benefit from general clinical information about the patient, in addition to image-based features. This clinical information may include, but is not limited to: Age, Race, Sex, Temperature, Height, Weight, Blood pressure, Current medications, Current diagnoses (i.e., pre-existing long or short-term conditions), Symptoms (e.g., pain, vomiting, urination, fever, trouble hearing, etc.), Medical history of any of the above….” ([0221]). Numerical clinical information is particularly suitable for machine-learning systems. “Numerical information (e.g., age, temperature, blood pressure) can take on scalar, real-numbered values (generally greater than 0). As such, they are suitable for directly being used as features in a machine learning system, which can operate with scalar values.” ([0223]). “Once exam features (including features from images, clinical information or other tests) are collected and combined (either solely in a current exam, or including information from past exams), these features can be used in a machine learning system to predict the diagnosis associated with a given exam (FIG. 14A).” ([0241]).
It would have been obvious to one having ordinary skill in the art at the time of filing to use a mobile device, as taught in DOUGLAS, as the handheld infrared temperature sensing device that communicates the tympanic images with a machine-learning model, wherein the machine-learning model not only considers those tympanic images but also other clinical information. One of ordinary skill in the art would have been motivated to use a mobile device for acquiring tympanic images for the convenience that it provides and because DOUGLAS teaches that it can guide the user in acquiring images. One of ordinary skill in the art would have been motivated to use a machine-learning model that uses other clinical information as it can more reliably and automatically diagnose acute otitis media. There would have been a reasonable expectation of success for each as DOUGLAS teaches that mobile devices can be configured to acquire and communicate tympanic images and machine-learning models can automatically diagnose acute otitis media with tympanic images and additional clinical information.
However, DOUGLAS does not explicitly teach the operations further comprise correlating temperature readings with axillary temperatures such that accuracy of acute otitis media diagnoses is improved.
DOGAN was a study conducted to determine if acute otitis media (AOM) affects the accuracy of tympanic temperature measurements. The study included patients with single-side AOM (i.e., only one ear was infected). (p.2, first full paragraph of left column). “Tympanic measurements were taken from both ears….” (p.2 , middle of left column). DOGAN concluded that, “[i]n patients with AOM, infected ears had higher temperatures than normal ears with a mean of 0.48±0.01 °C.” (Abstract). “We suggest that the higher tympanic temperatures, approximately 0.5 °C in our study, in infected ears may aid in diagnosis of patients with fever without a source in pediatric clinics.” (Abstract).
It would have been obvious to one having ordinary skill in the art at the time of filing to use the modified system, as described in DOUGLAS, to correlate temperature readings with axillary temperatures such that accuracy of acute otitis media diagnoses is improved, as taught in DOGAN. Particularly for situations where a child has a fever without a known source, one would have been motivated to acquire both ear temperatures along with the axillary temperature and input the information into a machine-learning model to determine if the child has acute otitis media. There would have been a reasonable expectation of success as DOGAN teaches that one can acquire measurements in each ear as well as an axillary temperature and DOUGLAS teaches that a machine-learning model can use such clinical information to determine acute otitis media.
RESPONSE TO APPLICANT’S ARGUMENTS
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. More specifically, Applicant argues that none of HARR, HAIKOU, and KIMURA teach (1) determining a first temperature reading of a first portion of the ear canal; (2) determining a second temperature reading of a second portion of the ear canal that differs from the first portion; and (3) detecting at least a threshold temperature differential as between the first temperature and the second temperature.
As explained above, while HARR and HAIKOU/RUPPERSBERG are clearly concerned with temperature variations and temperature distributions to identify ear infections, neither reference determines a difference in temperatures between two different portions of the ear canal. As explained above, Examiner is relying upon the newly cited DACOSTA for teaching that one having ordinary skill in the art would have been motivated to determine the temperature difference between two different portions of the ear canal in order to identify an ear infection.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20260083310-A1 teaches a probe that images the tympanic membrane and makes a diagnostic prediction regarding otitis media based on a laser speckle pattern. (see, e.g. [0004]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JASON P GROSS/Examiner, Art Unit 3797
/SERKAN AKAR/Primary Examiner, Art Unit 3797
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