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
Applicant's arguments, filed 10/30/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 10/30/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicants have amended claims 1, 9, 13, and 14.
Applicants have left claims 2-3, 6-8, 11, 15, and 16 as originally filed/previously presented.
Applicants have canceled/previously canceled claims 4-5, 10, and 12.
Claims 1-3, 6-9, 11, and 13-16 are the current claims hereby under examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections - Maintained and Newly Applied Necessitated by Applicant’s Amendments
Claims 1 and 14 are objected to because of the following informalities:
Regarding claim 1, line 18 recites “a user’s finger” however it appears it should read --the user’s finger-- (emphasis added).
Regarding claim 14, lines 3-4 recite “comprising artificial intelligence machine learning algorithm”, however it appears it should read --comprising an artificial intelligence machine learning algorithm-- (emphasis added).
Response to Arguments
Applicant’s amendments did not address the claim objection. Therefore, the Examiner cannot find a reason to withdraw the objection. Further, new objections are applied necessitated by Applicant’s amendments.
Claim Rejections - 35 USC § 103 - Maintained and Newly Applied Necessitated by Applicant’s Amendments
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-9, 11, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Merritt et al. (US 20100026995 A1) (cited in the IDS filed 01/14/2022) (previously cited), hereinafter referred to as Merritt, in view of Yosef Segman (US 20190069821 A1) (cited in the IDS filed 01/14/2022) (previously cited), hereinafter referred to as Segman, in view of Steve White (US 20090018420 A1) (previously cited), hereinafter referred to as White.
The claims are generally directed towards a noninvasive smart glucometer comprising: a near-infrared picture acquisition device, wherein the near-infrared picture acquisition device comprises a finger fixing device comprising a U-shaped groove configured for a user to put a fingertip, a near-infrared camera disposed on a bottom side of the finger fixing device, and a near-infrared light source disposed on a top side of the finger fixing device opposite to the bottom side, wherein the near-infrared camera detects transmitted light through the fingertip from the near-infrared light source; a capacitive touch-sensitive switch placed on a top side of the U-shaped groove and configured for the user to put the fingertip on a top side of the capacitive touch-sensitive switch to start an acquisition of near-infrared pictures of the fingertip, wherein both the top side of the U-shaped groove and the top side of the capacitive touch-sensitive switch are opposite to the bottom side of the finger fixing device; and a processor comprising a comparison and calibration module, wherein the comparison and calibration module is a noninvasive component, wherein the comparison and calibration module is configured to train on the processor by using a training engine being embedded into the comparison and calibration module, wherein the near-infrared picture acquisition device is used for acquiring near-infrared pictures of a user's finger, and the processor is used for processing, comparing and calibrating the near-infrared pictures based on at least one wavelength of the near-infrared light in real time and then directly outputting the user's blood glucose index.
Regarding claim 1, Merritt discloses a noninvasive smart glucometer (Abstract, Fig. 1, Fig. 2, para. [0007]) comprising:
a near-infrared picture acquisition device (Fig. 2A, element 200A, “monitoring device”, para. [0089]), wherein the near-infrared picture acquisition device comprises a finger fixing device comprising a U-shaped groove configured for a user to put a fingertip (Fig. 1, Fig. 2A, element 201a, Fig. 7A, element 710A, - tissue bed 710a is U shaped, para. [0059], “tissue shaper … concave surface can also provide more surface area from which light can be detected”, para. [0090], “sensor … conform to the shape, for example, of a patient’s finger”), a near-infrared detector disposed on a bottom side of the finger fixing device (Fig. 1, elements 106, Fig. 7A, elements 106 are on one side of element 701A, para. [0048], “detectors can detect optical radiation … near infrared”, para. [0075], “detectors capture and measure light from the measurement site … detectors can be implemented using one or more photodiodes, phototransistors, or the like …”, para. [0076-0083]), and a near-infrared light source disposed on a top side of the finger fixing device opposite to the bottom side (Fig. 1, element 104, Fig. 7A, elements 104 are on a second side of element 701A, opposite to elements 106, para. [0063-0070]), wherein the near-infrared detector detects transmitted light through the fingertip from the near-infrared light source (Fig. 1, Fig. 7A, para. [0058-0059], para. [0075]); and
a processor (Fig. 1, element 110, “signal processor”, para. [0083-0084]).
Merritt teaches using detectors to detect optical radiation, including near infrared to obtain a glucose measurement (para. [0048], para. [0075], para. [0076-0083]). However, Merritt does not explicitly disclose using a near-infrared camera, the processor comprising a comparison and calibration module, wherein the comparison and calibration module is a noninvasive component, wherein the comparison and calibration module is configured to train on the processor by using a training engine being embedded into the comparison and calibration module, wherein the near-infrared picture acquisition device is used for acquiring near-infrared pictures of a user's finger, and the processor is used for processing, comparing and calibrating the near-infrared pictures based on at least one wavelength of the near-infrared light in real time and then directly outputting the user's blood glucose index.
Segman teaches an analogous near-infrared picture acquisition device for monitoring blood glucose using a color image sensor (Abstract, Fig. 1A-B, para. [0008]). Segman further teaches a near-infrared camera (Fig. 1A, element 40, “image sensor”, para. [0024]). Segman further teaches the device comprises a processor comprising a comparison and calibration module (Fig. 4, Fig. 5, elements 50 and 55, para. [0024], “personal calibration …”, para. [0076-0083], “one or more processors programmed using program code …”), wherein the comparison and calibration module is a noninvasive component (Fig. 5, elements 50 and 55, para. [0077], “device 10 may have one or more processors …” - element 50 and 55 of element 30 are located within the “non-invasive component”), wherein the comparison and calibration module is configured to train on the processor by using a training engine being embedded into the comparison and calibration module (Fig. 2, Fig. 4, Fig. 5, elements 50 and 55, para. [0025-0071], “images converted into a vector … vector v is associated with a particular at least one invasive blood glucose measurement … learning matrix is formed by the one or more processors … adaptive machine learning … calibration completed … device is ready to perform non-invasive reading independently …”, para. [0076-0083]). Segman further teaches the near-infrared picture device is used for acquiring near-infrared pictures of a user's finger, and the processor is used for processing, comparing and calibrating the near-infrared pictures based on at least one wavelength of the near-infrared light in real time and then directly outputting the user's blood glucose index (para. [0022-0024], “microcontroller … calibrating the noninvasive component … algorithm executed in the DSP component …”, para. [0025-0071], “calibration completed … device is ready to perform non-invasive reading independently …”, para. [0076], “the non-invasive component including one or more color image sensors 40 configured to generate a series of images reflecting absorption of light (from a light source S) having traversed the tissue of the body part of the person”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the detectors and processor disclosed by Merritt to explicitly use a near-infrared camera to acquire near-infrared pictures to output a measurement result of the user’s blood glucose, and have the processor comprising a comparison and calibration module, wherein the comparison and calibration module is a noninvasive component, wherein the comparison and calibration module is configured to train on the processor by using a training engine being embedded into the comparison and calibration module, wherein the near-infrared picture acquisition device is used for acquiring near-infrared pictures of a user's finger, and the processor is used for processing, comparing and calibrating the near-infrared pictures based on at least one wavelength of the near-infrared light in real time and then directly outputting the user's blood glucose index, as taught by Segman. This is because Segman teaches a near-infrared camera obtaining images allows for richer information to be obtained as compared to discrete sensors, allowing for better glucose measurements (para. [0023-0024]). Additionally, one of ordinary skill in the art would recognize a near-infrared camera as a simple substitution of one light sensing technique for another. Segman further teaches a comparison and calibration module with a training engine to compare and calibration near-infrared pictures based on at least one wavelength allows for personal calibration to be performed for the user, and allowing the device to be used independently from an invasive component (para. [0024], para. [0071]).
However, modified Merritt does not explicitly disclose a capacitive touch-sensitive switch placed on a top side of the U-shaped groove and configured for the user to put the fingertip on a top side of the capacitive touch-sensitive switch to start an acquisition of near-infrared pictures of the fingertip, wherein both the top side of the U-shaped groove and the top side of the capacitive touch-sensitive switch are opposite to the bottom side of the finger fixing device.
White teaches of an analogous apparatus for spectroscopic evaluation of a subject’s body fluids (Abstract, Fig. 29, Fig. 30, para. [0058-0059]). White further teaches the apparatus includes a capacitive touch-sensitive switch configured for the user to put the fingertip on a top side of the capacitive touch-sensitive switch to start an acquisition of near-infrared pictures of the fingertip (para. [0074], “touch sensor may be used so that the device turns on as soon as a subject touches part of the device. A capacitive or resistive touch switch may be used to create a touch sensor”, para. [0131], “subject’s single finger …”, para. [0140], “top of a finger”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the glucometer disclosed by modified Merritt to additionally include a capacitive touch-sensitive switch configured for the user to put the fingertip on a top side of the capacitive touch-sensitive switch to start an acquisition of near-infrared pictures of the fingertip, as taught by White. This is because White teaches a capacitive touch-sensitive switch allows for the device to be activated and turned off when not in use (para. [0075]), which allows for the device to conserve power.
As to location of touch-sensitive switch in relation to the U-shaped groove and the bottom side of the finger fixing device, White teaches the location of the capacitive touch switch can be located just above a radiation source, and the switch can be positioned in other locations as appropriate for their specific function (para. [0075]). The location of the touch-sensitive switch will depend upon the available space within the glucometer and the specific function. As such, the location of touch-sensitive switch is a results-effective variables that would have been optimized through routine experimentation based on available space within the glucometer and design parameters of when acquisition is to be started in response to the fingertip. It would have been obvious to one of ordinary skill in the art at the time of invention to select the location of the touch-sensitive switch so as to be placed on a top side of the U-shaped groove, wherein both the top side of the U-shaped groove and the top side of the capacitive touch-sensitive switch are opposite to the bottom side of the finger fixing device.
Alternatively and/or additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the capacitive touch-sensitive switch taught by modified Merritt to explicitly be placed on a top side of the U-shape groove, wherein both the top side of the U-shaped groove and the top side of the capacitive touch-sensitive switch are opposite to the bottom side of the finger fixing device as the shifting of the switch would not modify the operation of the touch-sensitive switch in regards to starting an acquisition of near-infrared pictures when a fingertip is placed on top (see MPEP 2144.04, VI, C).
Regarding claim 2, modified Merritt discloses the noninvasive smart glucometer according to claim 1, further comprising an input-output device comprising a touch screen for the user's manipulation and displaying information (para. [0085], “user interface can provide an output … touch-screen display … user interface can be manipulated”).
Regarding claim 3, modified Merritt discloses the noninvasive smart glucometer according to claim 1, further comprising an external device and an interface communicating with the external device, the external device communicating with other components of the noninvasive smart glucometer in a wired and/or wireless way (Fig. 1, element 112, element 116, para. [0085-0086]).
Regarding claim 6, modified Merritt discloses the noninvasive smart glucometer according to claim 1, wherein the near-infrared light source comprises one or more sets of near-infrared lamps emitting near-infrared light with different wavelengths (para. [0063], “one or more sources of optical radiation …”, para. [0066], para. [0070]).
Regarding claim 7, modified Merritt discloses the noninvasive smart glucometer according to claim 1, wherein the near-infrared light source comprises three sets of near-infrared lamps emitting near-infrared light with different wavelengths (para. [0063], “one or more sources of optical radiation …”, para. [0066], “emitter can emit optical radiation at three or more wavelengths”, para. [0070]).
Regarding claim 8, modified Merritt discloses the noninvasive smart glucometer according to claim 7, wherein the near-infrared picture acquisition device sequentially acquires three sets of near-infrared pictures with different wavelengths (para. [0047-0048], para. [0066], para. [0075-0076], para. [0175], “sequence of pulses of light of around 905 nm, around 1200 nm, around 1300 nm, and around 1330 nm …”).
Regarding claim 9, modified Merritt discloses the noninvasive smart glucometer according to claim 1, wherein the at least one wavelength of near-infrared light emitted by the near-infrared light source ranges from 700 nm to 1800 nm (para. [0066-0068], para. [0174]).
Regarding claim 11, modified Merritt discloses the noninvasive smart glucometer according to claim 1, wherein the near-infrared camera and the finger fixing device are separated by transparent glass (Fig. 7A, 731 - elements 104 and 106 are separated by element 731, para. [0152], “glass layer”, para. [0159-0160], “glass layer 731 … transparent, electrically conductive materials can be used as the material 733”).
Regarding claim 13, modified Merritt discloses the noninvasive smart glucometer according to claim 1.
However, modified Merritt does not explicitly disclose wherein the processor comprises a picture processing module, wherein the picture processing module processes the acquired near-infrared pictures and then transmits the pictures to the comparison and calibration module for comparison and calibration, and the picture processing comprises associating the acquired near-infrared pictures with the at least one wavelength of near-infrared light at a time of shooting.
Segman further teaches a picture processing module (para. [0022]), wherein the picture processing module processes the acquired near-infrared pictures and then transmits the pictures to the comparison and calibration module for comparison and calibration (para. [0023-0024], para. [0076-0103]), and the picture processing comprises associating the acquired near-infrared pictures with the at least one wavelength of near-infrared light at a time of shooting (para. [0028], “constructing a vector … wavelength index …, para. [0076-0103], “convert the series of images into a vector … wherein the vector is associated with a particular at least one invasive blood glucose measurement …”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by modified Merritt to additionally include a picture processing module, wherein the picture processing module processes the acquired near-infrared pictures and then transmits the pictures to the comparison and calibration module for comparison and calibration, and the picture processing comprises associating the acquired near-infrared pictures with the at least one wavelength of near-infrared light at a time of shooting, as taught by Segman. This is because Segman teaches calibrating and comparison of infrared pictures allows for more accurate glucose level determination (para. [0024], para. [0077-0083]).
Regarding claim 14, modified Merritt discloses the noninvasive smart glucometer according to claim 13.
However, modified Merritt does not explicitly disclose wherein the training engine comprises artificial intelligence machine learning algorithm and is trained with training data, and the training data comprises a finger near-infrared light picture of a person to be acquired and a corresponding blood glucose index, and the at least one wavelength of the near-infrared light corresponding to the finger near-infrared light picture used in the training data is the same as the wavelength of the near-infrared light used by the finger near-infrared picture acquisition device.
Segman further teaches the training engine comprises artificial intelligence machine learning algorithm and is trained with training data, and the training data comprises a finger near-infrared light picture of a person to be acquired and a corresponding blood glucose index, and the at least one wavelength of the near-infrared light corresponding to the finger near-infrared light picture used in the training data is the same as the wavelength of the near-infrared light used by the finger near-infrared picture acquisition device (Fig. 4, Fig. 5, para. [0022], para. [0026-0061], “invasively measuring the blood glucose of the person using an invasive component … repeating to produce at least an additional invasive blood glucose measurement … within a proximity time … one or more color image sensors in the non-invasive component of the device generates a series of images reflecting absorption of light … converted into a vector … vector is associated with a particular at least one invasive blood glucose measurement … brain neural mechanism … adaptive learning machine associates groups of vectors to various glucose levels …”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by modified Merritt to additionally include applying artificial intelligence meaning learning algorithm and training data, as taught by Segman. This is because Segman teaches training an artificial intelligence machine learning algorithm allows for device to accurately determine and correlate an obtained image with a blood glucose value (para. [0044]).
Regarding claim 15, modified Merritt discloses the noninvasive smart glucometer according to claim 14.
However, modified Merritt does not explicitly disclose wherein the comparison and calibration module is set to be able to operate under a condition without a network.
Segman further teaches the comparison and calibration module is set to be able to operate under a condition without a network (Fig. 5, para. [0022], “medical and control subsystems are embedded in a single processor such as DSP or microcontroller …”, para. [0075], “color image sensor that is connected to one or more processors of a non-invasive component of device …” - the DSP or microcontroller is configured to perform the functions of the comparison and calibration module, therefore no network is required). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the module disclosed by modified Merritt to explicitly operate under a condition without a network, as taught by Segman. This is because one of ordinary skill in the art would recognize that operating without a network allows for the device to operate and obtain glucose readings in any location, increasing the usability.
Regarding claim 16, modified Merritt discloses the noninvasive smart glucometer according to claim 1, light from the near-infrared light source penetrates through the fingertip and is then detected by the near-infrared camera (Fig. 1, Fig. 7A, para. [0058-0059], para. [0075]).
Response to Arguments
Applicant's arguments filed 10/30/2025 have been fully considered but they are not persuasive.
Applicants have argued on pages 5-8 of Remarks, filed 10/30/2025, Segman does not disclose “a processor comprising a comparison and calibration module, wherein the comparison and calibration module is a noninvasive component, wherein the comparison and calibration module is configured to train on the processor by using a training engine being embedded into the comparison and calibration module”.
The Examiner respectfully disagrees. As recited in rejection above Segman teaches the device may have one or more processors 50 programmed with program code 55 (para. [0077]), and Segman teaches the noninvasive component 30 comprises a processor with program code (Fig. 5). That is, Segman teaches the device can comprise only one processor and program code, and the processor with the comparison and calibration module can be located within a noninvasive component.
Applicants have argued on pages 8-9 of Remarks, filed 10/30/2025, Segman does not disclose “wherein the processor is used for processing, comparing and calibrating the near-infrared pictures based on at least one wavelength of the near-infrared light in real time and then outputting the user’s blood glucose index”.
The Examiner respectfully disagrees. As recited in the rejection above, Segman teaches the processor processes, compares, and calibrates the near-infrared pictures based on at least one wavelength of the near-infrared light in real time, and then outputting the user’s blood glucose index (para. [0025-0071], “images converted into a vector … vector v is associated with a particular at least one invasive blood glucose measurement … learning matrix is formed by the one or more processors … adaptive machine learning … calibration completed … device is ready to perform non-invasive reading independently …”, para. [0076], “the non-invasive component including one or more color image sensors 40 configured to generate a series of images reflecting absorption of light (from a light source S) having traversed the tissue of the body part of the person”).
That is, the processor of Segman in real time processors, compares, and calibrates the near-infrared pictures based on at least one wavelength of the near-infrared light by creating the vectors/neural network. Further, Applicants arguments regarding “real time” are not commensurate in scope with the claimed invention.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE W KRETZER whose telephone number is (571)272-1907. The examiner can normally be reached Monday through Friday 8:30 AM to 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason M Sims can be reached at (571)272-7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.W.K./Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791