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, 8, 14, 16, and 18 have been amended.
Claims 1-20 are currently pending.
In light of the claim amendments, the Section 101 rejection has been withdrawn.
Claim Objections
Claims 1, 8, and 16 are objected to because of the following informalities:
The mapping operations in claims 1 and 16 should more clearly distinguish the two limitations and switch the order such that the claim language reads as follows: “mapping a temperature distribution of temperatures of the tympanic membrane utilizing the sensor data, wherein the mapping is based at least in part on sensor data for which the occlusion area has been excluded or compensated for and wherein the mapping is performed when the coverage satisfies a coverage criterion ….”
Likewise, claim 8 should read as follows: “mapping, at the device, a temperature distribution of temperatures of the tympanic membrane utilizing the sensor data, wherein the mapping is based at least in part on sensor data for which the occlusion area has been excluded or compensated for and wherein the mapping is performed when the coverage satisfies a coverage criterion…”
Claim 16 should also be amended to read as follows: “A device for use by a healthcare professional….”
Claims 1, 6-8, and 16 recite or similarly recite “generating temperature measurement differentials as between the temperatures of the tympanic membrane and the surrounding temperatures…” The phrase “as between” is not typically used when describing the difference between two values. Please remove the “as.”
Appropriate correction is required.
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, 2, 5, 6, 8, 9, 14, 16, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) in view of a translation of CN118680525A (hereinafter “HAIKOU”) (previously cited in prior Office Action) or, alternatively, U.S. Patent Appl. Publ. No. 2015/0351637 A1 (hereinafter “RUPPERSBERG”), and U.S. Patent Appl. Publ. No. 2013/0296685 A1 (hereinafter “TSUBOI”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”).
With respect to claim 1, NEWMAN teaches an infrared temperature sensing device (see, e.g., Title and Abstract) for use by a healthcare professional (see, e.g., [0002], [0018], and [0033]: describing that “inflammations” can be identified), the infrared temperature sensing device comprising:
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one or more processors. Claim 1 recites “processing means for processing output signals from the sensor array…” and processing electronics with a microprocessor ([0079], [0083]).
a sensor array configured to measure tympanic temperatures across multiple points on a tympanic membrane of a patient. From the Abstract: “A plurality of miniature IR sensors disposed in a sensor array are aimed at a target area of interest, the array providing a thermal “image” of the target area.” See also [0062]. The tympanic membrane is described as the target. (see, e.g., Abstract and claim 54).
a display unit. See, e.g., [0015] and [0018] and the display outputs in Figures 7 and 8.
a housing configured to be held by the healthcare professional (see, e.g., Fig. 2A and [0060] describing “the portable examination instrument 24 includes an instrument head 36 which is attached, releasably or otherwise, to the top of a hand-grippable battery handle 40”) and to contain the one or more processors and the sensor array (see, e.g., Fig. 2A and [0079]: “IR sensor array 44” is part of “detector assembly 42,” which can also include the processing electronics), the housing having a distal end configured for at least partial insertion into an ear canal of the patient (see, e.g., Fig. 2A and [0064]: “frusto-conical insertion portion 78”); and
receiving sensor data from the sensor array. See, e.g, [0062] and [0079] and claim 1.
determining, from the sensor data, whether an occlusion area within the ear canal impacts the coverage of the sensor array. NEWMAN is concerned about “obstructions” within the field of view, ([0078]), that might affect “temperature profiles” of the tympanic membrane. ([0080]). The temperature profile is the cumulative information from the individual sensors of the sensor array. ([0012]). One advantage of NEWMAN’s device is that “the presence of…ear wax and other obstructions can quickly be identified and compensated for so as to more accurately identify and estimate the hottest temperature(s) of a defined target area.” ([0033]). Claim 28 of NEWMAN explicitly teaches extrapolating temperature information “if portions of said medical target are obstructed from the sensor array.” As such, NEWMAN teaches determining whether an occlusion area impacts the coverage of the sensor array.
mapping a temperature distribution of temperatures of the tympanic membrane utilizing the sensor data, wherein the mapping…is based at least in part on sensor data for which the occlusion area has been excluded or compensated. “Each of the individual elements 45 comprising the sensor array 44 senses infrared radiation of a portion of a target area, akin to individual pixels of an electronic imager, such as a CCDl….” ([0062]). NEWMAN also describes mapping the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8. As discussed above, NEWMAN compensates for obstructions by extrapolating temperatures. (see, e.g., [0078]-[0081]).
detecting surrounding temperatures of surrounding tissues in the ear canal. NEWMAN teaches that the process to acquire temperatures of the tympanic membrane may also acquired temperatures of the surrounding tissues. “A basic assumption made in known IR thermometers is that the TM is within an interrogated area and that the TM subtends a specific portion of this interrogated area. Therefore, the manufacturers of these instruments will add a compensation factor arithmetically to the reading of the thermometer to make up for the fact that the device is reading the ear canal wall in addition to the TM.” ([0003]; see also, e.g., [0078]).
displaying, using the display unit and to the healthcare professional, the temperature distribution. See Figs. 7 and 8. NEWMAN describes displaying the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8.
However, NEWMAN does not explicitly teach that the device is configured for detecting/diagnosing acute otitis media based at least in part on detecting an abnormal heat signature derived from the temperature distributions.
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 that acute otitis media can be diagnosed based, at least in part, on abnormal heat signatures (i.e., temperature distributions) in thermal imaging.
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 acute otitis media, as taught in HAIKOU. 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.
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.
NEWMAN does not explicitly teach that the device includes a non-transitory computer-readable media, housed in the housing, storing instructions that, when executed by the one or more processors, cause the one or more processors to perform specific operations. Nonetheless, NEWMAN does describe using processing electronics to process the IR signal data and the processing electronics are contained within the housing. (see, e.g., Fig. 2A and [0079]: “IR sensor array 44” is part of “detector assembly 42,” which can also include the processing electronics).
In the same field of endeavor, TSUBOI teaches “a type of clinical thermometer that measures a body temperature by measuring radiant heat emitted from an eardrum….” ([0002]). TSUBOI teaches that it is challenging to acquire measurements of the eardrum continuously or over a longer period of time due to the thermometer being incorrectly positioned. ([0004]-[0005]). To address these challenges, TSUBOI teaches “an eardrum recognition unit configured to recognize a position of an eardrum based on image information regarding the eardrum, a temperature measurement unit configured to acquire a temperature within an external ear canal including the eardrum, and a temperature processing unit configured to determine a temperature of the eardrum based on a recognition result of the eardrum recognition unit and a measured temperature of the temperature measurement unit.” ([0007]). TSUBOI teaches a computer program that can cause a computer to perform the operations of these processing units. ([0009], [0131], [0132]).
It would have been obvious to one having ordinary skill in the art at the time of filing to add a non-transitory computer-readable media to the NEWMAN device, housed in the housing and storing instructions, as recited. One of ordinary skill in the art could have added a non-transitory computer-readable medium within the housing using known methods. One of ordinary skill in the art would have recognized that the results of adding the computer-readable medium would be predictable.
NEWMAN does not explicitly teach that the device is configured to determine, from the sensor data, a coverage of the sensor array relative to the tympanic membrane. However, NEWMAN is concerned with analyzing a target area (see, e,g., [0015]-[0016], [0082]) to guide the user to identify hot spots (see, e.g., [0083]). NEWMAN also describes an “aperture stop” to insure “that the representative pixels of the sensor array 44 see energy emanating only from the target 100….” ([0071]). Moreover, NEWMAN does not explicitly teach that the mapping is performed when the coverage satisfies a coverage criterion. However, NEWMAN is concerned about “obstructions” within the field of view, ([0078]), that might affect “temperature profiles” of the tympanic membrane. ([0080]). NEWMAN suggests extrapolating or interpolating to estimate hot spots that are obstructed or not within the field of view. ([0080]-[0083]). Also, one having ordinary skill in the art would know that it is necessary to have sufficient coverage of the tympanic membrane in order to visualize the temperature distribution for diagnosing acute otitis media.
In the same field of endeavor, TSUBOI is primarily concerned with insuring that a tip of the thermometer is properly positioned before acquiring subsequent information. “The eardrum recognition unit 221 a determines whether a tip end of the temperature sensor unit 110 faces the eardrum 14, and controls the functional units based on the determination result.” ([0122]). To this end, TSUBOI teaches determining an “eardrum occupancy rate, which is a ratio of an area in which the eardrum is shown in a captured image.” ([0113]). “The eardrum recognition unit 221 a acquires a thermal image as image information including the eardrum 14 from the thermal image acquisition unit 218 and recognizes the position of the eardrum 14…The eardrum recognition unit 221 a performs the image recognition process on the acquired thermal image, and calculates a ratio occupied by the eardrum 14 (an eardrum occupancy rate) in the image..” ([0122]). “The temperature processing unit 223 determines that the temperature sensor unit 110 faces the eardrum 14 when the eardrum occupancy rate calculated by the eardrum recognition unit 221 is greater than or equal to a predetermined value, and determines that the measured temperature of the temperature measurement unit 216 is reliable.” ([0077]).
It would have been obvious to one having ordinary skill in the art at the time of filing to configure the NEWMAN device to determine, from the sensor data, a coverage of the sensor array relative to the tympanic membrane and map the temperature distribution when the coverage satisfies a coverage criterion. One having ordinary skill in the art would have been motivated to determine the occupancy rate (i.e., coverage) of the tympanic membrane, as taught in TSUBOI, and then map the temperature distribution of the tympanic membrane for the user (i.e., display a spatial thermal profile) when a coverage criterion has been satisfied. The mapping would only be performed once it is determined that the coverage is sufficient because, as taught in HAIKOU/RUPPERSBERG, ear infections like acute otitis media can be detected based on temperature distributions, and one having ordinary skill in the art would desire a sufficiently sized area to analyze the temperature distribution. There would have been a reasonable expectation of success as TSUBOI teaches that the occupancy rate can be determined using thermal images.
NEWMAN modified by HAIKOU/RUPPERSBERG teaches determining and displaying temperature distributions to reveal hot spots. As such, the modified device determines temperature measurement differentials between different areas or zones within the image. However, Applicant recites temperature distributions and temperature measurement differentials as separate features and it appears, based on Applicant’s disclosure, that the temperature measurement differential is a more explicit comparison between two points or areas. (see, e.g., [0187]). As such, NEWMAN and HAIKOU/RUPPERSBERG do not explicitly teach generating temperature measurement differentials as between the temperatures of the tympanic membrane and the surrounding temperatures and detecting, from the temperature measurement differentials (in addition to the temperature distributions), an abnormal heat signature caused by inflammation on the tympanic membrane.
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]).
DACOSTA also 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]). Thus, like HAIKOU/RUPPERSBERG and NEWMAN, DACOSTA teaches that visually displaying temperature differences can aid in diagnosing an infection. Nonetheless, DACOSTA separately teaches identifying a temperature differential between two different areas or regions.
Moreover, DACOSTA teaches that the device can analyze the data and output an indication of an infection. “The device may be further configured to analyze [image data including thermal data], correlate such data, and provide an output based on the correlation of the data, such as, for example, an indication of wound status, wound healing, wound infection, bacterial load, or other diagnostic information upon which an intervention strategy may be based.” ([0062]). Moreover, this decision-making includes considering a threshold temperature differential. “[A] temperature differential of 3° C or more is indicative of an elevated temperature which may indicate infection.” ([0063]). The temperature differential could be displayed with the thermal image. ([0078]).
Accordingly, DACOSTA teaches generating temperature measurement differentials between the temperatures of a target area and temperatures of a surrounding area and detecting the acute otitis media ear infection based at least in part on detecting the abnormal heat signature.
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the NEWMAN device to generate temperature measurement differentials between the temperatures of a target area (i.e., tympanic membrane) and a surrounding area (i.e., area immediately surrounding the tympanic membrane). HAIKOU/RUPPERSBERG teaches that temperature data can be used to diagnose ear infections like acute otitis media. Similarly, 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 configure the NEWMAN device to generate temperature measurement differentials between the temperatures of the tympanic membrane and the surrounding temperatures and to detect the acute otitis media ear infection based at least in part on detecting the abnormal heat signature. 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, NEWMAN teaches that the operations further comprise displaying, via the display unit, a visual representation of a coverage of the sensor array within an ear canal of the patient. “A feature of the described apparatus is that direct feedback is provided to the user as to whether or not the array is pointing at the intended target (e.g., the tympanic membrane). For example, by displaying real time false color representations of temperature ranges of the sensed area, the user can continue to aim the instrument until the ‘hot’ spot is optimally positioned near the center of the thermal image.” ([0018]). Thus, the hot spot surrounded by false colors teaches “a visual representation of a coverage of the sensor array.”
With respect to claim 5, as discussed above, the combination of NEWMAN and HAIKOU/RUPPERSBERG teach that the operations further comprise detecting, from the temperature distribution, an abnormal heat signature caused by inflammation on the tympanic membrane, wherein detecting the acute otitis media is based at least in part on detecting the abnormal heat signature.
It would have been obvious to one having ordinary skill in the art at the time of filing to configure the NEWMAN device detect, from the temperature distribution, an abnormal heat signature caused by inflammation on the tympanic membrane, wherein detecting the acute otitis media is based at least in part on detecting the abnormal heat signature. 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 acute otitis media. 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.
With respect to claim 6, as discussed above, the combination of NEWMAN, HAIKOU/RUPPERSBERG, and DACOSTA teach that the operations further comprise detecting surrounding temperatures of surrounding tissues in an ear canal; generating temperature measurement differentials as between the temperatures of the tympanic membrane and the surrounding temperatures; and wherein detecting the acute otitis media is based at least in part on the temperature measurement differentials.
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the NEWMAN device to generate temperature measurement differentials between the temperatures of a target area (i.e., tympanic membrane) and a surrounding area (i.e., area immediately surrounding the tympanic membrane). HAIKOU/RUPPERSBERG teaches that temperature data can be used to diagnose ear infections like acute otitis media. Similarly, 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 generate temperature measurement differentials between the temperatures of the tympanic membrane and the surrounding temperatures in order to detect, from these temperature measurement differentials, an abnormal heat signature caused by inflammation on the tympanic membrane. 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 8, NEWMAN teaches a method for detecting an ear infection (see, e.g., Title and Abstract and [0033]: describing that “inflammations” can be identified) comprising:
receiving, at a device operated by a healthcare professional (see, e.g., [0002], [0018], and [0033]), sensor data from a sensor array of the device (from the Abstract: “A plurality of miniature IR sensors disposed in a sensor array are aimed at a target area of interest, the array providing a thermal “image” of the target area.” See also [0062] and [0079] and claim 54), the device including a housing (see, e.g., Fig. 2A and [0060]) having a distal end configured for at least partial insertion into an ear canal of a patient (see, e.g., Fig. 2A and [0064]: “frusto-conical insertion portion 78”);
determining, from the sensor data, whether an occlusion area within the ear canal impacts the coverage of the sensor array. NEWMAN is concerned about “obstructions” within the field of view, ([0078]), that might affect “temperature profiles” of the tympanic membrane. ([0080]). The temperature profile is the cumulative information from the individual sensors of the sensor array. ([0012]). One advantage of NEWMAN’s device is that “the presence of…ear wax and other obstructions can quickly be identified and compensated for so as to more accurately identify and estimate the hottest temperature(s) of a defined target area.” ([0033]). Claim 28 of NEWMAN explicitly teaches extrapolating temperature information “if portions of said medical target are obstructed from the sensor array.” As such, NEWMAN teaches determining whether an occlusion area impacts the coverage of the sensor array.
mapping, at the device, a temperature distribution of temperatures of the tympanic membrane utilizing the sensor data, wherein the mapping…is based at least in part on sensor data for which the occlusion area has been excluded or compensated for. “Each of the individual elements 45 comprising the sensor array 44 senses infrared radiation of a portion of a target area, akin to individual pixels of an electronic imager, such as a CCDl….” ([0062]). NEWMAN also describes mapping the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8. As discussed above, NEWMAN compensates for obstructions by extrapolating temperatures. (see, e.g., [0078]-[0081]).
detecting surrounding temperatures of surrounding tissues in the ear canal. NEWMAN teaches that the process to acquire temperatures of the tympanic membrane may also acquired temperatures of the surrounding tissues. “A basic assumption made in known IR thermometers is that the TM is within an interrogated area and that the TM subtends a specific portion of this interrogated area. Therefore, the manufacturers of these instruments will add a compensation factor arithmetically to the reading of the thermometer to make up for the fact that the device is reading the ear canal wall in addition to the TM.” ([0003]; see also, e.g., [0078]).
displaying, using a display unit of the device and to the healthcare professional, the temperature distribution. See Figs. 7 and 8. NEWMAN describes displaying the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8.
However, NEWMAN does not explicitly teach that the device is configured for detecting/diagnosing acute otitis media based at least in part on detecting an abnormal heat signature derived from the temperature distributions.
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 that acute otitis media can be diagnosed based, at least in part, on abnormal heat signatures (i.e., temperature distributions) in thermal imaging.
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 acute otitis media, as taught in HAIKOU. 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.
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.
NEWMAN does not explicitly teach determining, from the sensor data, a coverage of the sensor array relative to the tympanic membrane. However, NEWMAN is concerned with analyzing a target area (see, e,g., [0015]-[0016], [0082]) to guide the user to identify hot spots (see, e.g., [0083]). NEWMAN also describes an “aperture stop” to insure “that the representative pixels of the sensor array 44 see energy emanating only from the target 100….” ([0071]). Moreover, NEWMAN does not explicitly teach that the mapping is performed when the coverage satisfies a coverage criterion. However, NEWMAN is concerned about “obstructions” within the field of view, ([0078]), that might affect “temperature profiles” of the tympanic membrane. ([0080]). NEWMAN suggests extrapolating or interpolating to estimate hot spots that are obstructed or not within the field of view. ([0080]-[0083]). Also, one having ordinary skill in the art would know that it is necessary to have sufficient coverage of the tympanic membrane in order to visualize the temperature distribution for diagnosing acute otitis media.
In the same field of endeavor, TSUBOI is primarily concerned with insuring that a tip of the thermometer is properly positioned before acquiring subsequent information. “The eardrum recognition unit 221 a determines whether a tip end of the temperature sensor unit 110 faces the eardrum 14, and controls the functional units based on the determination result.” ([0122]). To this end, TSUBOI teaches determining an “eardrum occupancy rate, which is a ratio of an area in which the eardrum is shown in a captured image.” ([0113]). “The eardrum recognition unit 221 a acquires a thermal image as image information including the eardrum 14 from the thermal image acquisition unit 218 and recognizes the position of the eardrum 14…The eardrum recognition unit 221 a performs the image recognition process on the acquired thermal image, and calculates a ratio occupied by the eardrum 14 (an eardrum occupancy rate) in the image..” ([0122]). “The temperature processing unit 223 determines that the temperature sensor unit 110 faces the eardrum 14 when the eardrum occupancy rate calculated by the eardrum recognition unit 221 is greater than or equal to a predetermined value, and determines that the measured temperature of the temperature measurement unit 216 is reliable.” ([0077]).
It would have been obvious to one having ordinary skill in the art at the time of filing to configure the NEWMAN device to determine, from the sensor data, a coverage of the sensor array relative to the tympanic membrane and map the temperature distribution when the coverage satisfies a coverage criterion. One having ordinary skill in the art would have been motivated to determine the occupancy rate (i.e., coverage) of the tympanic membrane, as taught in TSUBOI, and then map the temperature distribution of the tympanic membrane for the user (i.e., display a spatial thermal profile) when a coverage criterion has been satisfied. The mapping would only be performed once it is determined that the coverage is sufficient because, as taught in HAIKOU/RUPPERSBERG, ear infections like acute otitis media can be detected based on temperature distributions. There would have been a reasonable expectation of success as TSUBOI teaches that the occupancy rate can be determined using thermal images.
NEWMAN modified by HAIKOU/RUPPERSBERG teaches determining and displaying temperature distributions to reveal hot spots. As such, the modified device determines temperature measurement differentials between different areas or zones within the image. However, Applicant recites temperature distributions and temperature measurement differentials as separate features and it appears, based on Applicant’s disclosure, that the temperature measurement differential is a more explicit comparison between two points or areas. (see, e.g., [0187]). As such, NEWMAN and HAIKOU/RUPPERSBERG do not explicitly teach generating temperature measurement differentials between the temperatures of the tympanic membrane and the surrounding temperatures and detecting, from the temperature measurement differentials (in addition to the temperature distributions), an abnormal heat signature caused by inflammation on the tympanic membrane.
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]).
DACOSTA also 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]). Thus, like HAIKOU/RUPPERSBERG and NEWMAN, DACOSTA teaches that visually displaying temperature differences can aid in diagnosing an infection. Nonetheless, DACOSTA separately teaches identifying a temperature differential between two different areas or regions.
Moreover, DACOSTA teaches that the device can analyze the data and output an indication of an infection. “The device may be further configured to analyze [image data including thermal data], correlate such data, and provide an output based on the correlation of the data, such as, for example, an indication of wound status, wound healing, wound infection, bacterial load, or other diagnostic information upon which an intervention strategy may be based.” ([0062]). Moreover, this decision-making includes considering a threshold temperature differential. “[A] temperature differential of 3° C or more is indicative of an elevated temperature which may indicate infection.” ([0063]). The temperature differential could be displayed with the thermal image. ([0078]).
Accordingly, DACOSTA teaches generating temperature measurement differentials between the temperatures of a target area and temperatures of a surrounding area and detecting the acute otitis media ear infection based at least in part on detecting the abnormal heat signature.
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the NEWMAN device to generate temperature measurement differentials between the temperatures of a target area (i.e., tympanic membrane) and a surrounding area (i.e., area immediately surrounding the tympanic membrane). HAIKOU/RUPPERSBERG teaches that temperature data can be used to diagnose ear infections like acute otitis media. Similarly, 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 configure the NEWMAN device to generate temperature measurement differentials between the temperatures of the tympanic membrane and the surrounding temperatures and to detect the acute otitis media ear infection based at least in part on detecting the abnormal heat signature. 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 9, as discussed above, DACOSTA teaches generating temperature measurement differentials between the temperatures of the tympanic membrane and the surrounding temperatures and detecting, from the temperature measurement differentials, an abnormal heat signature caused by inflammation on the tympanic membrane. The temperature measurement differential determine if a difference is excessive and indicative of an infection. As such, the NEWMAN device, modified by DACOSTA, would necessarily detect infrared radiation corresponding to elevated temperatures indicative of infection in an ear canal, wherein detecting the acute otitis media ear infection is based at least in part on detecting the infrared radiation corresponding to the elevated temperatures.
With respect to claim 14, NEWMAN teaches calibrating the sensor array to generate calibrated temperature measurements (see, e.g., [0019], [0020], and [0071]-[0073]). As such, detecting the acute otitis media ear infection as described by DACOSTA would necessarily be performed by identifying, from the calibrated temperature measurements, a predefined increase in temperature in the ear canal. (see, e.g., claim 10 of DACOSTA and [0074]: “For example, in response to a determination that the temperature of the test area is higher than the temperature of the reference area by 3° C. or more, the processor may output an indication of the presence of a bacterial infection.”).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the NEWMAN device to detect an acute otitis media ear infection by identifying, using calibrated measurements, a predefined increase in temperature. HAIKOU/RUPPERSBERG teaches that temperature data can be used to diagnose ear infections like acute otitis media. Similarly, DACOSTA teaches using a predetermined threshold for indicating the presence of an infection. One of ordinary skill in the art would have been motivated to use calibrated measurements and a predefined threshold difference to more accurately determine the presence of an ear infection. 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 16, NEWMAN teaches a device (see, e.g., Title and Abstract) for use by a healthcare professional (see, e.g., [0002], [0018], and [0033]: describing that “inflammations” can be identified), comprising:
one or more processors. Claim 1 recites “processing means for processing output signals from the sensor array…” and processing electronics with a microprocessor ([0079], [0083]).
a sensor array configured to measure tympanic temperatures across multiple points on a tympanic membrane of a patient. From the Abstract: “A plurality of miniature IR sensors disposed in a sensor array are aimed at a target area of interest, the array providing a thermal “image” of the target area.” See also [0062]. The tympanic membrane is described as the target. (see, e.g., Abstract and claim 54).
a display unit. See, e.g., [0015] and [0018] and the display outputs in Figures 7 and 8.
a housing configured to be held by the healthcare professional (see, e.g., Fig. 2A and [0060] describing “the portable examination instrument 24 includes an instrument head 36 which is attached, releasably or otherwise, to the top of a hand-grippable battery handle 40”) and to contain the one or more processors and the sensor array (see, e.g., Fig. 2A and [0079]: “IR sensor array 44” is part of “detector assembly 42,” which can also include the processing electronics), the housing having a distal end configured for at least partial insertion into an ear canal of the patient (see, e.g., Fig. 2A and [0064]: “frusto-conical insertion portion 78”); and
receiving sensor data from the sensor array. See, e.g, [0062] and [0079] and claim 1.
determining, from the sensor data, whether an occlusion area within the ear canal impacts the coverage of the sensor array. NEWMAN is concerned about “obstructions” within the field of view, ([0078]), that might affect “temperature profiles” of the tympanic membrane. ([0080]). The temperature profile is the cumulative information from the individual sensors of the sensor array. ([0012]). One advantage of NEWMAN’s device is that “the presence of…ear wax and other obstructions can quickly be identified and compensated for so as to more accurately identify and estimate the hottest temperature(s) of a defined target area.” ([0033]). Claim 28 of NEWMAN explicitly teaches extrapolating temperature information “if portions of said medical target are obstructed from the sensor array.” As such, NEWMAN teaches determining whether an occlusion area impacts the coverage of the sensor array.
mapping a temperature distribution of temperatures of the tympanic membrane utilizing the sensor data, wherein the mapping…is based at least in part on sensor data for which the occlusion area has been excluded or compensated. “Each of the individual elements 45 comprising the sensor array 44 senses infrared radiation of a portion of a target area, akin to individual pixels of an electronic imager, such as a CCDl….” ([0062]). NEWMAN also describes mapping the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8. As discussed above, NEWMAN compensates for obstructions by extrapolating temperatures. (see, e.g., [0078]-[0081]).
detecting surrounding temperatures of surrounding tissues in the ear canal. NEWMAN teaches that the process to acquire temperatures of the tympanic membrane may also acquired temperatures of the surrounding tissues. “A basic assumption made in known IR thermometers is that the TM is within an interrogated area and that the TM subtends a specific portion of this interrogated area. Therefore, the manufacturers of these instruments will add a compensation factor arithmetically to the reading of the thermometer to make up for the fact that the device is reading the ear canal wall in addition to the TM.” ([0003]; see also, e.g., [0078]).
displaying, using the display unit and to the healthcare professional, the temperature distribution. See Figs. 7 and 8. NEWMAN describes displaying the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8.
However, NEWMAN does not explicitly teach that the device is configured for detecting/diagnosing acute otitis media based at least in part on detecting an abnormal heat signature derived from the temperature distributions.
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 that acute otitis media can be diagnosed based, at least in part, on abnormal heat signatures (i.e., temperature distributions) in thermal imaging.
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 acute otitis media, as taught in HAIKOU. 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.
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.
NEWMAN does not explicitly teach that the device includes a non-transitory computer-readable media, housed in the housing, storing instructions that, when executed by the one or more processors, cause the one or more processors to perform specific operations. Nonetheless, NEWMAN does describe using processing electronics to process the IR signal data and the processing electronics are contained within the housing. (see, e.g., Fig. 2A and [0079]: “IR sensor array 44” is part of “detector assembly 42,” which can also include the processing electronics).
In the same field of endeavor, TSUBOI teaches “a type of clinical thermometer that measures a body temperature by measuring radiant heat emitted from an eardrum….” ([0002]). TSUBOI teaches that it is challenging to acquire measurements of the eardrum continuously or over a longer period of time due to the thermometer being incorrectly positioned. ([0004]-[0005]). To address these challenges, TSUBOI teaches “an eardrum recognition unit configured to recognize a position of an eardrum based on image information regarding the eardrum, a temperature measurement unit configured to acquire a temperature within an external ear canal including the eardrum, and a temperature processing unit configured to determine a temperature of the eardrum based on a recognition result of the eardrum recognition unit and a measured temperature of the temperature measurement unit.” ([0007]). TSUBOI teaches a computer program that can cause a computer to perform the operations of these processing units. ([0009], [0131], [0132]).
It would have been obvious to one having ordinary skill in the art at the time of filing to add a non-transitory computer-readable media to the NEWMAN device, housed in the housing and storing instructions, as recited. One of ordinary skill in the art could have added a non-transitory computer-readable medium within the housing using known methods. One of ordinary skill in the art would have recognized that the results of adding the computer-readable medium would be predictable.
NEWMAN does not explicitly teach that the device is configured to determine, from the sensor data, a coverage of the sensor array relative to the tympanic membrane. However, NEWMAN is concerned with analyzing a target area (see, e,g., [0015]-[0016], [0082]) to guide the user to identify hot spots (see, e.g., [0083]). NEWMAN also describes an “aperture stop” to insure “that the representative pixels of the sensor array 44 see energy emanating only from the target 100….” ([0071]). Moreover, NEWMAN does not explicitly teach that the mapping is performed when the coverage satisfies a coverage criterion. However, NEWMAN is concerned about “obstructions” within the field of view, ([0078]), that might affect “temperature profiles” of the tympanic membrane. ([0080]). NEWMAN suggests extrapolating or interpolating to estimate hot spots that are obstructed or not within the field of view. ([0080]-[0083]). Also, one having ordinary skill in the art would know that it is necessary to have sufficient coverage of the tympanic membrane in order to visualize the temperature distribution for diagnosing acute otitis media.
In the same field of endeavor, TSUBOI is primarily concerned with insuring that a tip of the thermometer is properly positioned before acquiring subsequent information. “The eardrum recognition unit 221 a determines whether a tip end of the temperature sensor unit 110 faces the eardrum 14, and controls the functional units based on the determination result.” ([0122]). To this end, TSUBOI teaches determining an “eardrum occupancy rate, which is a ratio of an area in which the eardrum is shown in a captured image.” ([0113]). “The eardrum recognition unit 221 a acquires a thermal image as image information including the eardrum 14 from the thermal image acquisition unit 218 and recognizes the position of the eardrum 14…The eardrum recognition unit 221 a performs the image recognition process on the acquired thermal image, and calculates a ratio occupied by the eardrum 14 (an eardrum occupancy rate) in the image..” ([0122]). “The temperature processing unit 223 determines that the temperature sensor unit 110 faces the eardrum 14 when the eardrum occupancy rate calculated by the eardrum recognition unit 221 is greater than or equal to a predetermined value, and determines that the measured temperature of the temperature measurement unit 216 is reliable.” ([0077]).
It would have been obvious to one having ordinary skill in the art at the time of filing to configure the NEWMAN device to determine, from the sensor data, a coverage of the sensor array relative to the tympanic membrane and map the temperature distribution when the coverage satisfies a coverage criterion. One having ordinary skill in the art would have been motivated to determine the occupancy rate (i.e., coverage) of the tympanic membrane, as taught in TSUBOI, and then map the temperature distribution of the tympanic membrane for the user (i.e., display a spatial thermal profile) when a coverage criterion has been satisfied. The mapping would only be performed once it is determined that the coverage is sufficient because, as taught in HAIKOU/RUPPERSBERG, ear infections like acute otitis media can be detected based on temperature distributions. There would have been a reasonable expectation of success as TSUBOI teaches that the occupancy rate can be determined using thermal images.
NEWMAN modified by HAIKOU/RUPPERSBERG teaches determining and displaying temperature distributions to reveal hot spots. As such, the modified device determines temperature measurement differentials between different areas or zones within the image. However, Applicant recites temperature distributions and temperature measurement differentials as separate features and it appears, based on Applicant’s disclosure, that the temperature measurement differential is a more explicit comparison between two points or areas. (see, e.g., [0187]). As such, NEWMAN and HAIKOU/RUPPERSBERG do not explicitly teach generating temperature measurement differentials as between the temperatures of the tympanic membrane and the surrounding temperatures and detecting, from the temperature measurement differentials (in addition to the temperature distributions), an abnormal heat signature caused by inflammation on the tympanic membrane.
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]).
DACOSTA also 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]). Thus, like HAIKOU/RUPPERSBERG and NEWMAN, DACOSTA teaches that visually displaying temperature differences can aid in diagnosing an infection. Nonetheless, DACOSTA separately teaches identifying a temperature differential between two different areas or regions.
Moreover, DACOSTA teaches that the device can analyze the data and output an indication of an infection. “The device may be further configured to analyze [image data including thermal data], correlate such data, and provide an output based on the correlation of the data, such as, for example, an indication of wound status, wound healing, wound infection, bacterial load, or other diagnostic information upon which an intervention strategy may be based.” ([0062]). Moreover, this decision-making includes considering a threshold temperature differential. “[A] temperature differential of 3° C or more is indicative of an elevated temperature which may indicate infection.” ([0063]). The temperature differential could be displayed with the thermal image. ([0078]).
Accordingly, DACOSTA teaches generating temperature measurement differentials between the temperatures of a target area and temperatures of a surrounding area and detecting the acute otitis media ear infection based at least in part on detecting the abnormal heat signature.
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the NEWMAN device to generate temperature measurement differentials between the temperatures of a target area (i.e., tympanic membrane) and a surrounding area (i.e., area immediately surrounding the tympanic membrane). HAIKOU/RUPPERSBERG teaches that temperature data can be used to diagnose ear infections like acute otitis media. Similarly, 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 configure the NEWMAN device to generate temperature measurement differentials between the temperatures of the tympanic membrane and the surrounding temperatures and to detect the acute otitis media ear infection based at least in part on detecting the abnormal heat signature. 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 17, as discussed above, the NEWMAN device would display, using the display unit and to the healthcare professional, the temperature distribution such that areas of abnormal heat indicative of ear infections are highlighted. See Figs. 7 and 8. NEWMAN describes displaying the temperature distributions using a matrix or grid of numbers ([0074]) or more “visually perceivable forms, such as textures or false colors…leading the user to identify a ‘hot’ spot 122.” ([0075]). See Fig. 8.
With respect to claim 20, NEWMAN does not explicitly teach displaying, via the display unit, a color-coded alert system indicating presence or absence of the acute otitis media.
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]). “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 in which the user selected reference point has been set to zero on the scale and temperatures warmer (a positive temperature differential)….” ([0067]).
Moreover, DACOSTA teaches that the device can analyze the data and output an indication of an infection. “The device may be further configured to analyze [image data including thermal data], correlate such data, and provide an output based on the correlation of the data, such as, for example, an indication of wound status, wound healing, wound infection, bacterial load, or other diagnostic information upon which an intervention strategy may be based.” ([0062]). Moreover, this decision-making includes considering a threshold temperature differential. “[A] temperature differential of 3° C or more is indicative of an elevated temperature which may indicate infection.” ([0063]). The temperature differential could be displayed with the thermal image. ([0078]).
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It would have been obvious to one having ordinary skill in the art at the time of filing to display, via the display unit, a color-coded alert system indicating presence or absence of an infection (e.g., acute otitis media), as taught in DACOSTA. One would have been motivated to provide the color-coding scheme because using different colors, particularly when indicating caution or alert, is more readily understandable than text. There would have been a reasonable expectation of success as DACOSTA teaches that it can be applied to a handheld device acquiring measurements.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) 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. 6,001,066 (hereinafter “CANFIELD”).
With respect to claim 3, NEWMAN does not explicitly teach that the operations further comprise: receiving input data requesting activation of the sensor array; and activating the sensor array based at least in part on the input data.
CANFIELD teaches a two-piece portable, self-contained tympanic thermometer temperature measuring system includes a measuring unit and a base unit. (Abstract). Once the measuring unit has probe cover on a probe end, “[t]he clinician may then lift the measuring unit 22 (see FIG. 2B), insert the measuring unit probe end 30 (now covered with a probe cover) into the outer ear of a patient, and position the measuring unit relative to the outer ear so that the probe end is aimed at the patient's eardrum (FIG. 3C). Once the clinician has properly positioned the measuring unit 22, he or she may press a push button 36 on the measuring unit to cause system 20 to read the patient's temperature (FIG. 3C).”
It would have been obvious to modify the NEWMAN device so that the sensor array would be activated upon receiving input data requesting activation of the sensory array. One would be motivated to include this feature because the clinician could first determine when the device is properly positioned within the ear canal and then request the measurement. This workflow would increase the likelihood that more reliable measurements are acquired. There would be a reasonable expectation of success as CANFIELD teaches that the feature can be incorporated in a similar device.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 1 above, and further in view of MELEXIS Datasheet for MLX90640 32x24 IR array (2018) (hereinafter “MELEXIS”).
With respect to claim 4, NEWMAN does not explicitly teach wherein the sensor array is configured to operate within a predetermined temperature range.
MELEXIS describes a small, low cost IR array that is capable of “presence detection” and “person localization.” The operating temperature is “ -40°C ÷ 85°C.” (page 1, “1. Feature and Benefits” and also “2. Application Examples”). MELEXIS refers to the device as an “imager” (page 10) and the recommended measurement flow includes “image processing.” (page 13). MELEXIS is also capable of being used in an only “image mode.” (see page 54). The size of the device is also capable of being incorporated into a tympanic thermometer (see, e.g., Figures 28 and 29).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the MELEXIS infrared array with the NEWMAN device. NEWMAN describes using an IR sensor array and MELEXIS is capable of producing an IR image. The operating temperature of the sensor array is predetermined. One of ordinary skill in the art could have combined the NEWMAN device and the MELEXIS infrared array using known methods and, in combination, 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 were predictable.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) 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 Appl. Publ. No. 2013/0083823 A1 (hereinafter “HARR”).
With respect to claim 7, NEWMAN does not explicitly teach the recitations of claim 7.
In the same field of endeavor, 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]).
HARR teaches that the operations further comprise:
detecting ambient temperatures proximate to the tympanic membrane. 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.”
generating temperature measurement differentials as between the ambient temperatures and the temperatures of the tympanic membrane (HARR teaches 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]); and
wherein detecting the acute otitis media is based at least in part on the temperature measurement differentials. Because the temperature data is affected by the calibration/compensation, detecting the acute otitis media is necessarily based, at least in part, on the temperature measurement differentials. NOTE: Applicant does not define “ambient temperatures proximate to the tympanic membrane.” However, Applicant’s disclosure describes that the ambient temperatures may change when inserted into the ear canal. ([0167]).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the NEWMAN device to include detecting an ambient temperature near the tympanic membrane to generate temperature measurement differentials between the ambient temperature and the temperatures of the tympanic membrane. One of ordinary skill in the art would have been motivated to determine the ambient temperature (i.e., reference temperature) in order to calibrate the measurements as taught in HARR, thereby acquiring more accurate measurements. There would have been a reasonable expectation of success as HARR teaches that ambient temperatures proximate to the tympanic membrane may be acquired and used to obtain more accurate temperature measurements.
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 8 above, and further in view of U.S. Patent No. 10,764,514 (hereinafter “HOEVENAAR”).
With respect to claim 10, NEWMAN does not explicitly teach that the operations further comprise: tuning the sensor array to detect infrared radiation emitted from an inflamed tympanic membrane compared to tissues surrounding an ear canal.
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 NEWMAN. “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 different weight functions to control automatic gain-state switching and provides several examples for the maximum-weighted point. The maximum-weighted point could be a center pixel of plurality of pixels, a center pixel of a designated region of interest, which could have a row-and-column grid location and a predetermined shape or an arbitrary shape determined by the operator. (Col. 13, lines 22-35).
It would have been obvious to one having ordinary skill in the art at the time of filing to tune the sensor array to detect infrared radiation emitted from an inflamed tympanic membrane compared to tissues surrounding an ear canal. One having ordinary skill in the art would choose a weight function and/or gain state that positions the hot spot at the inflamed tympanic membrane so as to provide a more clearly defined temperature distribution of the tympanic membrane and surround tissues. There would have been a reasonable expectation of success as HOEVENAAR teaches that sensor arrays can be tuned.
With respect to claim 13, while the combination of NEWMAN and HAIKOU/RUPPERSBERG teach identifying a heat variation indicative of the acute otitis media ear infection, detecting the heat variation, and detecting the acute otitis media ear infection is based at least in part on detecting the heat variation, the cited art does not teach identifying an infrared radiation variation indicative of the acute otitis media ear infection, detecting, from the sensor data, the infrared radiation variation; and wherein detecting the acute otitis media ear infection is based at least in part on detecting the heat variation and the infrared radiation variation.
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 consider the infrared radiation variation 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 configure the system to switch to a low gain state, based on the teachings of HOEVENAAR, and then acquire the heat variation. Any detection of acute otitis media would be based, at least in part, on detecting the infrared radiation variation and detecting the heat variation.
Claims 11, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 8 above, and further in view of U.S. Patent Appl. Publ. No. 2015/0065803 A1 (hereinafter “DOUGLAS”).
With respect to claim 11, NEWMAN does not explicitly teach storing trend data indicating heat patterns historically caused by the acute otitis media ear infection and utilizing the trend data along with the sensor data to detect the acute otitis media ear infection.
DOUGLAS teaches “methods and apparatuses for assisting in the acquisition and analysis of images of the tympanic membrane to provide information that may be helpful in the understanding and management of disease, such ear infection (acute otitis media).” (Abstract). Generally, DOUGLAS teaches extracting features and determining the probability those features identify the tympanic membrane. ([0014]).
When predicting a diagnosis or prognosis, DOUGLAS teaches considering past examinations (i.e., trend data). “At the time of a given exam, relevant information for predicting the diagnosis or prognosis may come not only from the current exam, but also from the results of past exams. For example, at the time of a given exam, a patient who has a history of Acute Otitis Media (AOM) is at higher risk of being diagnosed with AOM than a patient with no history of AOM, independent of information gleaned during the current exam. It is therefore useful to combine information from the current and past exams when making a prediction of diagnosis or prognosis.” ([0232]: see also [0347]: “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.”).
It would have been obvious to one having ordinary skill in the art at the time of filing to store trend data indicating heat patterns (i.e., past temperature distributions from IR images) historically caused by the acute otitis media ear infection and utilizing the trend data along with the sensor data (i.e., current temperature distribution in IR image) to detect the acute otitis media ear infection, as taught in DOUGLAS. One of ordinary skill in the art would have been motivated to store trend data and utilize the trend data to provide a more reliable prediction of acute otitis media ear infection. There would have been a reasonable expectation of success as DOUGLAS teaches that images of the tympanic membrane can be analyzed with respect past images (of the user or other people).
With respect to claim 12, NEWMAN does not explicitly teach storing specific infrared signatures associated with infections in an ear canal and utilizing the specific infrared signatures along with the sensor data to detect the acute otitis media ear infection.
DOUGLAS teaches “methods and apparatuses for assisting in the acquisition and analysis of images of the tympanic membrane to provide information that may be helpful in the understanding and management of disease, such ear infection (acute otitis media).” (Abstract). Generally, DOUGLAS teaches extracting features and determining the probability those features identify the tympanic membrane. ([0014]). “Any appropriate features may be extracted… Features may include statistical mappings or transformations of the raw color information, such as averages, distributions, standard deviations, etc. For example, extracting a plurality of features for each of the subregions in the subset of subregions may comprise extracting a color lightness value, a first hue value and a second hue value for each subregion in the subset of subregions.” ([0028]). “In many cases it is useful to get information about an area of interest using multiple spectrums of light, such as infrared, ultraviolet and visible light.” ([0414]). DOUGLAS also suggests using an IR sensor. ([0417]).
Moreover, DOUGLAS states “it would be helpful to provide one or more images of a patient's inner ear/tympanic membrane and provide similar images (including time course images) from a database of such images, particularly where the database images are associated with related images and/or diagnosis/prognosis information.” ([0011]). Figure 33 (shown here) “illustrates one example of an access comparison screen for comparing an image of a patient's tympanic membrane to other (library) images.” ([0121]).
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It would have been obvious to one having ordinary skill in the art at the time of filing store specific infrared signatures associated with infections in an ear canal (i.e., past temperature distributions) and utilize the specific infrared signatures along with the sensor data (i.e., current temperature distribution) to detect the acute otitis media ear infection, as taught in DOUGLAS. One of ordinary skill in the art would have been motivated to store specific infrared signatures and utilize the specific infrared signatures to provide a more reliable prediction of acute otitis media ear infection. There would have been a reasonable expectation of success as DOUGLAS teaches that images of the tympanic membrane can be analyzed with respect past images (of the user or other people).
With respect to claim 15, NEWMAN does not explicitly teach sending notification data to another device operated by the healthcare professional, the notification data alerting the healthcare professional that the acute otitis media ear infection has been detected by the device.
DOUGLAS teaches 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). 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 send notification data from a first device operated by the healthcare professional (e.g., patient’s mobile device implementing software or an otoscope provided by the physician) to a second device operated by the healthcare professional (e.g., device at physician’s office), the notification data alerting the healthcare professional that the acute otitis media ear infection has been detected by the device. For situations in which the patient is responsible for acquiring images, as described in DOUGLAS, one of ordinary skill in the art would have been motivated to configure the system to automatically notify the physician responsible for providing healthcare to the patient. There would have been a reasonable expectation of success as DOUGLAS teaches that the workflow can include portable devices controlled by the patient (e.g., smartphone or otoscopes) that then communicate information remotely.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 8 above, and further in view of 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 18, the combined teachings of NEWMAN, HAIKOU/RUPPERSBERG, TSUBOI, and DACOSTA are only applied to one ear and do not teach applying a similar method to the other ear.
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 (1) acquire a first temperature reading from a first ear of a patient and a second temperature reading from the other ear of the patient using the same protocol for each ear as recited in claim 16, thereby using sensor data that excludes or compensates for obstructions, and (2) compare the temperature readings to detect a thermal asymmetry satisfying a threshold difference criterion (e.g., detecting that the difference is at least 0.5°C), as taught in DOGAN. One would have been motivated to acquire both ear temperatures because, for situations in which only one ear is infected, determining that the different in temperature is at least 0.5°C is an indication that supports the diagnosis of AOM. There would have been a reasonable expectation of success as DOGAN teaches that one can acquire measurements in each ear and compare them.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2002/0143257 A1 (hereinafter “NEWMAN”) 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. 2013/0296685 A1 (hereinafter “TSUBOI”) and U.S. Patent Appl. Publ. No. 2024/0366145 A1 (hereinafter “DACOSTA”) as applied to claim 16 above, and further in view of U.S. Patent Appl. Publ. No. 2015/0065803 A1 (hereinafter “DOUGLAS”) and Shim, Jae-Hyuk, et al. "Improving the accuracy of otitis media with effusion diagnosis in pediatric patients using deep learning." Bioengineering 10.11 (2023): 1337 (hereinafter “SHIM”).
With respect to claim 19, NEWMAN does not explicitly teach the claim limitations. However, DOUGLAS teaches storing trend data associated with ear infections of patients of various age ranges.
When predicting a diagnosis or prognosis, DOUGLAS teaches considering past examinations (i.e., trend data). “At the time of a given exam, relevant information for predicting the diagnosis or prognosis may come not only from the current exam, but also from the results of past exams. For example, at the time of a given exam, a patient who has a history of Acute Otitis Media (AOM) is at higher risk of being diagnosed with AOM than a patient with no history of AOM, independent of information gleaned during the current exam. It is therefore useful to combine information from the current and past exams when making a prediction of diagnosis or prognosis.” ([0232]: see also [0347]: “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.”).
DOUGLAS also teaches receiving input data indicating an age 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….” ([0221]). For training and using the machine-learning models, DOUGLAS teaches inputting “clinical information,” which can be numerical or categorical. ([0222]). “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. Some information may be transformed, e.g., logarithmically, to reflect the non-normal distributions of the measurement and the fact that the effect of changing values may vary based on the original value. For example, the difference in physiologies between a newborn (0-month-old) and a 12-month-old child are likely to be vastly greater than the difference in physiologies between a 30-year-old and a 31-year-old adult, despite the fact that the age difference is one year in both cases. As such, one might use the logarithm of age as a feature instead of age itself.”
It would have been obvious to one having ordinary skill in the art at the time of filing to store trend data associated with ear infections of patients of various age ranges and receiving input data indicating an age of the patient, as taught in DOUGLAS. One of ordinary skill in the art would have been motivated to store trend data and utilize the trend data to provide a more reliable prediction of acute otitis media ear infection. There would have been a reasonable expectation of success as DOUGLAS teaches that images of the tympanic membrane can be analyzed with respect past images (of the user and by other people).
While DOUGLAS teaches that age is one factor to improve analysis and uses the patient’s age as an input to the trained model, DOUGLAS does not determine a subset of trend data or detect acute otitis media based on that subset of trend data. However, it is well known that children and adults have different physiologies and that machine-learning models perform better when applied to input that are similar to its trained images.
For example, SHIM notes that it is important for trained models to differentiate between pediatric and adult patients. (p.2, second to last paragraph). “Clarifying the use of pediatric data is particularly important due to major differences in the orientation of the tympanic membranes of small children and adults.” (Id). “The dimensions of the ear canal expand from 4.5 X 7.7 mm in children aged 5–8 to 5.4 X 8.6 mm in adults aged over 18, and the membrane slopes from the posterosuperior to the anteroinferior direction, allowing for a larger size compared to the ear canal.” (Id). SHIM concludes “it is vital that the classification models are trained on pediatric tympanic membrane images, given the substantial anatomical differences from adult images….” (Id).
It would have been obvious to one having ordinary skill in the art at the time of filing to determine a subset of the trend data to utilize based at least in part on the age of the patient so that detecting the acute otitis media is based at least in part on the subset of the trend data. One of ordinary skill in the art would have been motivated to use a trained model more appropriate for the patient and to select either an adult or pediatric subset. There would have been a reasonable expectation of success as DOUGLAS and SHIM teach that machine-learning models can be trained to identify acute otitis media using images of all ages.
RESPONSE TO APPLICANT’S ARGUMENTS
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20250005746-A1 teaches a tympanum image processing apparatus that extracts, from a tympanum image, a tympanum outline of the tympanum image and earwax region of the tympanum image by using a first machine learning model, obtains, on the basis of the tympanum outline of the tympanum image, a target image of the entire tympanum, a tympanum outline of the target image, and earwax region of the target image, and generates a transformed image in which an abnormal region of the target image is changed to a normal region. (see, e.g. Abstract).
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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