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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/19/2026 has been entered.
Claims Accounting
Applicant's arguments, filed 08/19/2026, have been fully considered.
The following rejections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 08/19/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 4, 7, and 9-10 have been amended.
Claims 11-16 are newly presented.
Claims 1, 4-7, and 9-16 are the current claims hereby under examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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, 4-7, and 9-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 7, 9, and 10, claim 1 recites “convert the observation data to a feature quantity of the edema using a machine learning model” in lines 7-8. The previous limitation of “acquire observation data, which is acquired when an edema is observed and includes an image having a plurality of pixels corresponding to the spectral reflectance of the skin imaged by the camera” describes the observation data, but only positively recites a condition for acquiring the observation data (when an edema is observed) and the contents of the observation data (a plurality of pixels corresponding to the spectral reflectance of the skin imaged by the camera). The limitation does not positively recite that the observation data comprises an image of an edema, and therefore it is unclear how the claim can complete the limitation of “convert the observation data to a feature quantity of the edema using a machine learning model”. Clarification is requested. Similar recitations present in claims 7, 9, and 10 and raise similar issues of indefiniteness.
For the purposes of examination, the claim is interpreted to recite “acquire observation data, which is acquired when an edema is observed and includes an image having a plurality of pixels corresponding to the spectral reflectance of the skin, including the edema, imaged by the camera”.
Regarding claim 14, the claim recites the limitation “the certainty factor” in line 4. There is insufficient antecedent basis for this limitation. Clarification is requested.
For the purposes of examination, claim 14 is interpreted to be dependent on claim 12.
All claims not explicitly addressed above are rejected under 35 U.S.C. 112(b) are rejected by virtue of their dependency on a rejected base claim.
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, 4-7, and 9-16 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.
Step 1
Regarding claim 1, the claim recites a camera and processing circuitry configured to complete series of functional limitations, including to convert observation data to a feature quantity of the target event using a machine learning model, to restore observation data from the feature quantity using a numerical simulation model, and to train a machine learning model. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
Step 2A, Prong One
The claim is then analyzed to determine whether it is directed to any judicial exception. The functional limitations of converting observation data to a feature quantity of the target event using a machine learning model, restoring observation data from the feature quantity using a numerical simulation model, and training the machine learning model based on a loss function wherein the loss function is a function to calculate a discrepancy set forth judicial exceptions. These functional limitations describe sets of mathematical calculations using formulas and/or equations. Thus, the claim is drawn to a Mathematical Concept, which is an Abstract Idea.
Step 2A, Prong Two
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites training the machine learning model, which is a judicial exception, as it is based on a loss function that calculates a discrepancy. Further, the training of the machine learning model does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the trained machine learning model, nor does the method use a particular machine to perform the Abstract Idea. It is noted that the processing circuitry is a generic computer component configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. Claim 1 further recites the generating and displaying an in-vivo component volume map. The generation and display of an in-vivo component volume map is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The display of in-vivo component volume map does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the displayed in-vivo component volume map, nor does the device use a particular machine to perform the Abstract Idea.
Step 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Ideas, the claim recites additional functional limitations of acquiring observational data and generating and displaying an in-vivo component volume map. The acquiring limitation is recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. The claim also recites the limitation of a camera configured image a spectral reflectance of a skin of a patient, which, as recited, is a generic camera capable of collecting spectral images used to complete the data gathering acquiring step. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the processing circuitry and camera do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
The generation and display of an in-vivo component volume map is considered insignificant extra-solution activity, as it comprises the display of the output of the machine learning model.
It is noted that the limitation related to the feature quantity are details of the operation of the machine learning model, which is identified as an Abstract Idea.
The inclusion of the camera with the processing circuitry does not constitute a particular machine, as it comprises a generic camera configured to perform the pre-solution data gathering step and a generic computer system configured to perform the judicial exception. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 7 and 9-10.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite functional limitations pertaining to the Abstract Ideas. Claims 4-5 are directed towards details of the loss function and claims 6 and 16 are directed towards details of the numerical simulation, both of which are identified as Abstract Ideas above. Claim 11 is directed towards details of the in-vivo component volume, a detail of the operation of the machine learning model, which is an Abstract idea. Claims 12-15 recite details pertaining to a certainty factor derived from the discrepancy of the loss function. The certainty factor is calculated and only used for display, similarly to the in-vivo component volume. The converting, restoring, and training steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Examiner’s Note
No prior art rejections are applicable based on prior art found in the search for amended claims 1, 7, 9, and 10. Any amendments to the claims to overcome the rejections under 35 U.S.C. 112(b) and 101 may require further search and consideration due to changes in the scope of the claim resulting from amendments, and could result in the application of prior art rejections in future actions.
The following is a statement of reasons for the lack of prior art rejections of claims 1, 4-7, and 9-16:
Regarding claim 1, the closest identified prior art is considered to be a combination of US Patent Publication 2020/0193597 by Fan et al. – previously cited (“Fan”), in view of US Patent Publication 2021/0193298 by Sun et al. – previously cited (“Sun”), as evidenced by US Patent Publication 2009/0317856 by Mycek et al. – previously cited (“Mycek”), in view of US Patent Publication 2022/0011224 by Bertsimas et al. – previously cited (“Bertsimas”).
Fan teaches an information processing device comprising: a camera configured to image a spectral reflectance of a skin of a patient ([0160-0161]; healing predictions may be accomplished based on one or more images of the wound captured using a multispectral image sensor (i.e., camera)); and processing circuitry configured to ([0234]; All methods and tasks may be performed by a computer system and may be implemented in application-specific circuitry.): acquire observation data, which is acquired when an edema is observed and includes an image having a plurality of pixels corresponding to the spectral reflectance of the skin imaged by the camera (Fig. 16, multispectral image sensor as described in par. [0161] is used to acquire the ulcer image. Figs. 24 and 28; Input of ulcer image to the autoencoder, the target event is considered when the capturing of the observation data for training. [0188-0190]; The image data contains 3 channels per pixel, the 3 channels represented the diffuse reflectance of light from the tissue at the wavelengths from the skin, where an ulcer would occur as shown in Fig. 16. Ulcers can be caused by edemas, and therefore the image can be acquired when an edema is observed); convert the observation data to a feature quantity of the edema using a machine learning model (Fig. 24, [0161]; The encoder of the autoencoder can “generate a reduced feature representation of the input, here a reduced number of values (e.g., numerical values) representing the pixel values in the input image(s)”); restore expected observation data from the feature quantity ([0169]; the decoder of the autoencoder: “then decompress this encoded data with the decoder such that the output is a good/perfect reconstruction of the original input data”); and train the machine learning model ([0167]; Neural networks are trained through backpropagation in which the network parameters are tuned to produce expected outputs given corresponding inputs in labeled training data.) based on a loss function based on a discrepancy between first observation data, which is the observation data that has not been converted to the feature quantity, and second observation data, which is the expected observation data restored from the feature quantity ([0190]; “For each predicted pixel value (in R3) of the output of the decoder layer, the loss was computed with mean square error (MSE) wherein the target values were the pixel values of the original image”).
Fan does not teach restoring the observation data from the feature quantity by using a numerical simulation model.
Fig. 3 of Sun teaches a method including a step (Step S206) of generating (i.e., restoring) a sample training image using feature data generated from the original training image from a neural network model (i.e., machine learning model) ([0039, 0051]). The training image is generated using a generative model, which may be a numerical simulation using a Monte-Carlo sampling method ([0020]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the device of Fan such that the processing circuitry is configured to use a numerical simulation model to restore the observation data from the feature quantity, as taught by Sun ([0020, 0039, 0051]). Mycek teaches that a Monte Carlo simulation is a computational method that is accurate throughout the optical parameter space for modeling photon transport in biological tissue ([0080]), and therefore the combination of Fan and Sun would use an accurate computational method.
Fan in view of Sun does not teach the loss function being based on the assumption that a frequency distribution of the discrepancy conforms to a predetermined probability density distribution, or wherein the loss function is a function to calculate the discrepancy for each of the plurality of pixels included in the image, the function being based on the assumption that the frequency distribution of the discrepancy for each of the plurality of pixels conforms to the predetermined probability density distribution.
Sun teaches a method of training a machine learning model. Sun teaches determining one or more training distribution divergences based at least in part on comparing the one or more training distributions to a reference distribution (i.e., predetermined probability density function), such as a normal distribution having a mean of zero and a variance of unity. A loss corresponding to the one or more training distribution divergences is determined ([0027]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the processing circuitry of Fan in view of Sun such that the machine learning model is trained based on a loss being based on the assumption that a frequency distribution of the discrepancy conforms to a predetermined probability density distribution, as taught by Sun ([0027]). This modification merely comprises a simple substitution of one known element (loss based on a frequency distribution conforming to a predetermined probability density distribution) for another (loss based on mean square error of target values vs original values) to obtain predictable results. See MPEP 2143.I.B. It is noted that Fan uses the loss function to train the machine learning model such that the loss is minimized, therefore, in the combination of Fan and Sun, the distribution divergences of would be minimized. It is noted that the method of Fan is directed towards the analysis of each pixel value in the image, and therefore the loss function would be calculated for each of the pixels. Therefore, the combination of Fan and Sun would result in wherein the loss function is a function to calculate the discrepancy for each of the plurality of pixels included in the image, the function being based on the assumption that the frequency distribution of the discrepancy for each of the plurality of pixels conforms to the predetermined probability density distribution.
Fan in view of Sun does not teach wherein the feature quantity is an in-vivo component volume of the patient.
Fig. 1B of Bertsimas teaches a method wherein the encoder of an autoencoder generates feature values (i.e., latent variables) in step 106B ([0069]). Bertsimas teaches that conventional techniques of dimension reduction provide a set of latent variables that may not provide a human interpretable indication of characteristics of a biological sample (i.e., in-vivo component volume). The feature generation step of Bertsimas identifies a subset of wavelengths of the spectral data that indicate characteristics of a biological sample, allowing a clinician to interpret a diagnosis result ([0051]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the processing circuitry of Fan in view of Sun such that the feature quantity is an in-vivo component volume of the patient, to provide a human interpretable indication of characteristics of a biological sample that allows a clinician to interpret a diagnosis result, as taught by Bertsimas ([0051]).
The combination of Fan, Sun, and Bertsimas does not teach wherein the processing circuitry is further configured to generate an in-vivo component volume map in which each of the plurality of pixels included in the image are replaced with the in-vivo component volume, and display the in-vivo component volume map on a display. The feature quantity taught by the combination of Fan, Sun, and Bertsimas is a singular quantity and is not generated for each pixel. Therefore, one of ordinary skill in the art at the time of the effective filing date to have modified the combination of Fan, Sun, and Bertsimas to replace each pixel in the image to generate an in-vivo component volume map.
Similar limitations are present in independent claims 7, 9, and 10. The limitations of these claims are patentably distinct over the prior art cited in this Office action and any other prior art.
Response to Arguments
Applicant’s arguments, filed 08/19/2026 have been fully considered.
The amendments to the claims overcome the rejections of record under 35 U.S.C. 112(b) of claim 4.
Applicant’s arguments regarding the rejections of the claims under 35 U.S.C. 101 are acknowledged. These arguments are not found persuasive. Applicant’s arguments regarding the training step not reciting a judicial exception is not found persuasive. Claims 1, 7, 9, and 10 recite the training step being based on a loss function, wherein the loss function is a function to calculate the discrepancy for each of the plurality of pixels in the image. Applicant argues that the characterization of the limitations identified in the Office action as judicial exceptions are analogous to the applying transformations limitation in USPTO Example 39 cited in the Memorandum to Patent Examiners dated August 4, 2025. Examiner notes that while both the independent claims of the instant application and USPTO Example 39 both recite training steps, the limitations of the instant application directly recite the training being based on the loss function, which comprises a calculation. The recitation of calculating the discrepancy is a clear recitation of a Mathematical Concept (i.e., mathematical calculation) during the training step, and is therefore drawn to a judicial exception. Further, the converting and restoring steps are also drawn to Mathematical Concepts. The converting step comprises generating feature quantities (i.e., converting observation data to another quantity) and the restoring step comprises the use of a numerical simulation model (applying a numerical model to a quantity).
Applicant’s arguments that the recitation of a camera to generate an image having a plurality of pixels corresponding to the spectral reflectance of skin a patient integrates the Abstract idea into practical application is not found persuasive. The camera is a generic camera configured to obtain the image recited in the acquiring step, and is therefore drawn to a generic device/sensor configured to perform the pre-solutional activity of data gathering for the Abstract Idea. The characterization of the camera as a “generic camera” is provided to provide context for the device not comprising a particular machine. Any camera is capable of imaging a spectral reflectance of the skin of the patient. Further, the generic camera is only relied upon to acquire an image to be operated on by the judicial exception.
Applicant argues that claim 1 is directed towards a specific technical solution to a specific technical problem. However, claim 1 does not recite a limitation that ties a specific technical solution to a specific technical problem. Examiner invites applicant to identify the specific technical problem and identify the specific technical solution present in the claims.
Conclusion
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
