DETAILED ACTION
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restriction
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-15 and 28, drawn to a method for deriving a blood glucose level of a user, classified in A61B5/14532.
II. Claims 16-27, drawn to an apparatus for monitoring a blood glucose level of a user, classified in G06V2201/03.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as process and apparatus for its practice. The inventions are distinct if it can be shown that either: (1) the process as claimed can be practiced by another and materially different apparatus or by hand, or (2) the apparatus as claimed can be used to practice another and materially different process. (MPEP § 806.05(e)). In this case, the process as claimed can be practiced by another and materially different apparatus that does not comprise glucose level evaluation interface being receptive to an estimated blood glucose level generated in response to the one or more images.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
Each group of invention has separate classification and separate status in the art. Further, each group of invention requires a different field of search and different search terms and databases because each group of invention possesses a distinct element not possessed by the other groups of invention as set forth above.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with Shunsuke S. Sumitani on October 21, 2025 a provisional election was made without traverse to prosecute the invention of Group I, claims 1-15 and 28 (a method for deriving a blood glucose level of a user). Affirmation of this election must be made by applicant in replying to this Office action. Claims 16-27 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under U.S.C. 120, 121, or 365 is acknowledged. The prior-filed application (Provisional application No. 63/379693 filed on October 14, 2022) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 8, 2024 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation is:
“capturing one or more images of the user with a hyperspectral imaging device” in claim 1.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof:
[0025] The embodiments of the present disclosure contemplate the non-invasive and non-contact monitoring of blood glucose levels. With reference to FIG. 1, the blood glucose monitoring system 10 may be incorporated into a smartphone 12 or other portable electronic device that is expected to be carried by a user 14 while going about everyday activities. Although the embodiments of the system 10 will be disclosed in the context of such smartphone 12, it will be appreciated by those having ordinary skill in the art that other devices such as tablets, laptop computers, or dedicated blood glucose monitoring devices that incorporate the contemplated features of the system 10 may be substituted.
[0026] The non-invasive and non-contact blood glucose monitoring is envisioned to be possible based upon an analysis of image data captured of the user 14. In this regard, the smartphone 12, and the blood glucose monitoring system 10, may include or be connected to a camera/imaging device 16. FIG. 2 illustrates an exemplary output of a conventional imaging sensor that is sensitive only to the three primary colors of red, green, and blue, with a single array representative of the image field being generated for each primary color sensitivity. Specifically, there is a red-band array 18, a green-band array 20, and a blue-band array 22. A typical sensor array is fabricated on a single plane and comprised of photodetectors. A first subset of photodetectors may be located behind a red colored filter, a second subset of photodetectors may be located behind a green colored filter, and a third subset of photodetectors may be located behind a blue colored filter, with the various color filters being arranged in grouped patterns. This configuration is known as the Bayer-filter sensor, though other configurations for separating different color wavelengths before reaching the monochromatic photodetector are known in the art. The spatial resolution of a given sensor is understood to refer to the number of individual pixels in the sensor field.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112B
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 and 28 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, and all dependent claims thereof, recites “the images” in lines 2-3, which lack antecedent basis. Does this refer to “one or more images” previously recited in line 1?
Claim 2 recites:
“the electromagnetic spectrum band channels” (pluralized for the first time) in line 1, which lacks antecedent basis.
“the layered data sets” in lines 1-2, which lacks antecedent basis.
Claim 3 recites:
“the electromagnetic spectrum band channels” in line 1, which lacks antecedent basis.
“the layered data sets” in line 2, which lacks antecedent basis.
“between approximately 10 nanometers and approximately 0.1 millimeters with 1 nanometer channel steps” in lines 2-3, which is indefinite for reciting relative terminology. What is “approximately 10 nanometers and approximately 0.1 millimeters with 1 nanometer channel steps”?
Claim 5 recites “the body part” in line 1, which lacks antecedent basis. Does this refer to “a specific body part” previously introduced in claim 4?
Claim 6 recites “the layers data sets” in line 2, which lacks antecedent basis.
Claim 8 recites “the training images” in line 3, which lacks antecedent basis. Does this refer to “one or more training images” previously recited?
Claim 9 recites “the correlated pair” in line 2, which lacks antecedent basis.
Claim 13 recites “the convolutional neural network” in line 1, which lacks antecedent basis.
Claim 14 recites “the convolutional neural network” in line 1, which lacks antecedent basis.
Claim 15 recite “the convolutional neural network” in line 1, which lacks antecedent basis.
Claim 28 recites “the images” in lines 4-5, which lack antecedent basis. Does this refer to “one or more images” previously recited in line 4?
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a method for deriving a blood glucose level of a user. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“A method for deriving a blood glucose level of a user… feeding the predefined sets of image excerpts to a machine learning model trained on a plurality of correlated pairs of one or more training images associated with training blood glucose measurements; and generating, with the machine learning model, an estimated blood glucose level for the user corresponding to the one or more images thereof.”
These limitations describe a mathematical calculation. When given their broadest reasonable interpretation in light of the specification, the limitations identified above, including the highly generic machine learning model, correspond to mathematical relationships/calculations. Moreover, the plain meaning of “machine learning” is a series of mathematical calculations. See also 2024 AI SME Update, which held a similar claim construction was not patent eligible (see claim 2 of example 47, using a trained artificial neural network to analyze anomalies on input data was not patent eligible). The 2024 AI SME Update also sets forth that a trained machine learning model/engine amounts to a mental process (claim 2 of example 47) as nothing from the claims suggest that the limitations cannot be practically performed by a human, using simple pen/paper.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“…capturing one or more images of the user with a hyperspectral imaging device, the images being defined by a plurality of layered data sets each corresponding to an electromagnetic spectrum band channel; cropping the one or more images to predefined sets of image excerpts…”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant pre-solution extra-solution activity, e.g., mere data gathering steps necessary to perform the identified judicial exception. Examiner adds that “cropping the one or more images” is akin to prepping the image data for subsequent analysis (via generic machine learning model).
The additional limitations also do not add significantly more to the identified judicial exception because they relate to well-understood, routine, and conventional techniques for obtaining known types of data via generic hyperspectral imaging device. Moreover, the machine learning module is also recited at a high level of generality such that it does not equate to significantly more.
Independent claims 28 is also not patent eligible for substantially similar reasons.
Dependent claims 2-15 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea, recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above, and/or do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, claims 1-15 and 28 are not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-15 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Clemente (US PG Pub. No. 2024/0023838 A1) in view of Krause et al. (US PG Pub. No. 2021/0181093 A1) (hereinafter “Krause”).
With respect to claims 1 and 28, Clemente teaches a method for deriving a blood glucose level of a user (see title “Non-invasive blood glucose monitoring system), comprising: capturing one or more images of the user with an imaging device (par.0059 “medical diagnostics, spectroscopy-based techniques… and/or image processing… to achieve accurate, non-invasive blood glucose estimation”), the images being defined by a plurality of layered data sets each corresponding to an electromagnetic spectrum band channel (par.0107 “Image Measurement Datasets. Five datasets were created by extracting measurement data from the images. To create the dataset, each image in the dataset was split into four channels including red, green, blue, and grayscale (the image with color removed). Then, for each color channel, the channel's pixel center of mass, minimum, maximum, mean, median, standard deviation, and variance were calculated”); cropping the one or more images to predefined sets of image excerpts (par.0082 “Data captured using main bodies of the present disclosure must be prepared prior to processing via pre-processing…Data augmentation techniques including cropping, zooming, height and width shift, and horizontal flipping can be used, as well”); feeding the predefined sets of image excerpts to a machine learning model trained on a plurality of correlated pairs of one or more training images associated with training blood glucose measurements (par.0067 “machine learning models… to monitor blood glucose levels in real time. The machine learning model utilizes its learned knowledge to accurately estimate the glucose levels from spectroscopy images. The estimation is based on the correlation between the extracted image features and glucose concentrations”); and generating, with the machine learning model, an estimated blood glucose level for the user corresponding to the one or more images thereof (par.0067 “The machine learning model utilizes its learned knowledge to accurately estimate the glucose levels from spectroscopy images. The estimation is based on the correlation between the extracted image features and glucose concentrations. The device provides real-time glucose readings, displaying them on a user-friendly interface for easy interpretation”).
However, Clemente does not explicitly teach capturing images of the user with a hyperspectral imaging device.
Krause teaches capturing images of the user with a hyperspectral imaging device (par.0032 “The method is hence suitable, in an altogether outstanding way, for analysing spectroscopic data. Comparable data sets can be produced for analysis with different measuring devices… it is suitable particularly well for analyses of reflectance and transmission, preferably in the ultraviolet, visual and/or infrared spectrum, with mobile spectrometers, for example so-called low-cost spectrometers, and in hyper-spectral imaging. Application areas, given by way of example, are food scanners in the foodstuffs sphere, ground analyses in the agricultural sector, sorting plants for bulk goods and blood sugar analyses in the medical field”).
Therefore, it would have been prima facie obvious to person having ordinary skill in the art (“PHOSITA”) when the invention was filed to incorporate a hyperspectral imaging device in place of Clemente’s spectroscopy device as doing so would be a simple substitution that provides comparable data sets that is suitable particularly well for blood glucose analysis in the medical field, as suggested by Krause (par.0032).
With respect to claim 2, Clemente teaches the electromagnetic spectrum band channels of the layered data sets correspond to visible spectrum primary color bands of red, blue, and green (par.0107).
With respect to claim 3, Clemente teaches wherein one of the electromagnetic spectrum band channels of the layered data sets corresponds to a hyperspectral band channel between approximately 10 nanometers and approximately 0.1 millimeters with 1 nanometer channel steps (par.0106+). Although Clemente and Krause do not explicitly disclose the recited hyperspectral band channel ranges, further modification to incorporate this feature would have been prima facie obvious to PHOSITA when the invention was filed since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves
only routine skill in the art. In re Aller, 105 USPQ 233.
With respect to claim 4, Clemente teaches wherein the one or more images is of a specific body part of the user (par.0065).
With respect to claim 5, Clemente teaches wherein the body part of the user is selected from a group consisting of: a face, an wrist, and an arm (par.0065).
With respect to claim 6, Clemente teaches normalizing each of the layered data sets to a constrained minimum and maximum range according to the corresponding electromagnetic spectrum band channel (par.0069, 0085, 0107).
With respect to claim 7, Clemente teaches wherein a given one of the predefined sets of image excerpts is selected from a group consisting of: a central crop targeting main facial features, a top-left crop, a top-right crop, a bottom-left crop, a bottom-right crop, a mirrored central crop targeting main facial features, a mirrored top-left crop, a mirrored top-right crop, a mirrored bottom-left crop, and a mirrored bottom-right crop (par.0082, 0091, 0093).
With respect to claim 8, Clemente teaches capturing the one or more training images of a plurality of training users with the hyperspectral imaging device, the training images being defined by a plurality of layered data sets each corresponding an electromagnetic spectrum band channel; capturing the training blood glucose measurements of the training users concurrently with the capturing of the training images; and feeding one or more correlated pair of the training blood glucose measurement and the training images to the machine learning model (par.0066, 0068+).
With respect to claim 9, Clemente teaches training the machine learning model with the correlated pair of the training blood glucose measurement and the training images (par.0067).
With respect to claim 10, Clemente teaches wherein the machine learning model implements a neural architecture (par.0013).
With respect to claim 11, Clemente teaches wherein the neural architecture is a convolutional neural network (par.0013).
With respect to claim 12, Clemente teaches wherein the neural architecture is a vision transformer (par.0068-71, 0102).
With respect to claim 13, Clemente teaches wherein the convolutional neural network applies a regression model, with the estimated blood glucose level being generated as a numeric score value (par.0072-81).
With respect to claim 14, Clemente teaches wherein the convolutional neural network applies a classification model, with the estimated blood glucose level being generated as a class defined by sequential ranges of blood glucose concentrations (par.0070-87).
With respect to claim 15, Clemente teaches wherein the convolutional neural network applies a multi-task model including the application of a combination of a regression model and a classification model (par.0070-87).
Conclusion
No claim is allowed.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791