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 .
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 12-16 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 claim 12, the phrase “the values of the user-selected parameter” (plural) in the second to last line lacks proper antecedent basis as the claim previously recites “a value of a user-selected parameter” (singular). For this examination, the phrase is being interpreted as “the value of the user-selected parameter”.
Claims not explicitly rejected above are rejected due to their dependence 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.
Claim 9 is 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 9, the claim recites a series of steps or acts, including receiving an image of a body part while the body part is illuminated by a light pattern containing at least one pattern feature, determining skin pattern features from the image, determining a value of a user-selected parameter by using a data-driven model, and outputting the value of the user-selected parameter. 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 step of determining a value of a user-selected parameter by using a data-driven model sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
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 9 recites outputting the value of the user-selected parameter, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The output 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 output, nor does the method use a particular machine to perform the Abstract Idea.
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 Idea, the claim recites additional steps of receiving an image of a body part while the body part is illuminated by a light pattern containing at least one pattern feature, determining skin pattern features from the image, and outputting a determined value of the user-selected parameter. The receiving and determining steps are each recited at a high level of generality such that they to insignificant presolution activity, e.g., mere data gathering steps 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 activity engaged in by medical professionals prior to Applicant's invention. As noted above, outputting the value of the user-selected parameter is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). Furthermore, it is well established that the mere physical or tangible nature of an additional element such as the receiving step does 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)).
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.
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, 9-14, and 16 rejected under 35 U.S.C. 103 as being unpatentable over George et al.’085 (US Pub No. 2021/0275085 – previously cited) in view of Frank et al.’873 (US Pub No. 2020/0245873).
Regarding claim 1, George et al.’085 discloses a method for training a data-driven model for determining a value of a user-selected parameter related to a condition of a human, the method comprising: (a) receiving an image of a body part of the human while the body part is illuminated by a light pattern containing at least one pattern feature (sections [0071], [0081-0083]); (b) determining skin pattern features of the human from the image, wherein a skin pattern feature is a pattern feature which has been reflected by skin (sections [0050-0053], [0072-0074], [0084]); (c) receiving, from the human via a user interface a value of the user-selected parameter related to the condition of the human (section [0039] of the published specification of the current application states that the user interface can be “a data transfer interface which can receive a file provided by a user, such as an interface to a data storage medium or a communication interface such as a network connection surface”; the processor that uses the trained physical skin properties deep learning model 252, the trained visual skin properties deep learning model 254, and the trained skin disorder severity assessment deep learning model of sections [0050-0053] is a user interface that receives from the human a value of the user-selected parameter related to the condition of the human); and (d) training a data-driven model with a training dataset comprising a set of skin pattern features and the value of the user-selected parameter (sections [0050-0052], [0058]).
George et al.’085 fails to explicitly disclose that the training dataset is specific to the human and comprises a set of the skin pattern features of the human. Frank et al.’873 teaches that training a data-driven model with a training dataset specific to the user upon which the model is to be used provides more accurate model outputs for the specific user (section [0500]). Frank et al.’873 also teaches retraining a general data-driven model with a training dataset specific to a human in order to generate a data-driven model personalized to the human (section [0190]) and to make the data-driven model perform better (section [0388]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of George et al.’085 such that its training dataset is specific to the human and comprises a set of skin pattern features of the human, as this would provide a more accurate output of the data-driven model when used on the particular user. Furthermore, Official notice is being taken that it is well known in the art to personalize a data-driven model to be used on a specific human by training the data-driven model with data from the specific human (see references cited in paragraph 14 below). Section [0058] of George et al.’085 teaches that the training of the data-driven model is performed iteratively (i.e., over a period of time).
Regarding claim 2, the body part is illuminated by a light pattern in the near infrared region (section [0083] of George et al.’085).
Regarding claim 5, a convolutional neural network is used for determining the skin pattern features (sections [0052], [0056] of George et al.’085).
Regarding claim 6, the method comprises receiving from, from the human via the user interface, the user-selected parameter related to the condition of the human ([0050-0053] of George et al.’085).
Regarding claim 9, the sections of George et al.’085 cited above, as modified by Frank et al.’873, disclose a method for determining a value of a user-selected parameter related to the condition of a human, the method comprising steps (a)-(d) recited in the claim.
Regarding claim 10, the condition of the human is a condition of the human’s skin (sections [0032], [0050-0053] of George et al.’085).
Regarding claim 11, George et al.’085, as modified by Frank et al.’873, discloses a non-transitory computer-readable medium storing a computer program including instructions for executing steps of the method according to claim 1 (sections [0033-0034], [0036] of George et al.’085).
Regarding claim 12, Figure 12 of George et al.’085 discloses a device for determining a value of a user-selected parameter related to the condition of a human, the device comprising: (a) a projector for projecting patterned light containing at least one pattern feature onto a body part of the human (projectors 1210, sections [0081-0083]); (b) a camera for recording an image of the body part while it is illuminated by patterned light (camera 1212, sections [0081-0083]); (c) a user interface for receiving, from the human, a value of a user-selected parameter related to the condition of the human (section [0039] of the published specification of the current application states that the user interface can be “a data transfer interface which can receive a file provided by a user, such as an interface to a data storage medium or a communication interface such as a network connection surface”; the processor that uses the trained physical skin properties deep learning model 252, the trained visual skin properties deep learning model 254, and the trained skin disorder severity assessment deep learning model of sections [0050-0053] is a user interface that receives, from the human, a value of a user-selected parameter related to the condition of the human); and (d) a processor for – determining skin pattern features of the human from the image, wherein a skin pattern feature is a pattern feature which has been reflected by skin (sections [0050-0053], [0072-0074]), and training a data-driven model with a training dataset comprising skin pattern features and the values of the user-selected parameter (sections [0050-0052], [0058]).
George et al.’085 fails to explicitly disclose that the training dataset is specific to the human and comprises a set of the skin pattern features of the human. Frank et al.’873 teaches that training a data-driven model with a training dataset specific to the user upon which the model is to be used provides more accurate model outputs for the specific user (section [0500]). Frank et al.’873 also teaches retraining a general data-driven model with a training dataset specific to a human in order to generate a data-driven model personalized to the human (section [0190]) and to make the data-driven model perform better (section [0388]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of George et al.’085 such that its training dataset is specific to the human and comprises a set of skin pattern features of the human, as this would provide a more accurate output of the data-driven model when used on the particular user. Furthermore, Official notice is being taken that it is well known in the art to personalize a data-driven model to be used on a specific human by training the data-driven model with data from the specific human (see references cited in paragraph 14 below). Section [0058] of George et al.’085 teaches that the training of the data-driven model is performed iteratively (i.e., over a period of time).
Regarding claim 13, the device is a portable device (section [0080] of George et al.’085).
Regarding claim 14, the processor is further configured for determining a current value of the user-selected parameter related to the condition from the skin pattern features by using the trained data-driven model (sections [0050-0053], [0072-0074] of George et al.’085).
Regarding claim 16, the device is a smartphone (section [0080] of George et al.’085).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over George et al.’085 in view of Frank et al.’873, as applied to claim 1, further in view of Petit’444 (US Pub No. 2017/0076444 – previously cited).
George et al.’085 in view of Frank et al.’873 discloses all of the elements of the current invention, as discussed in paragraph 7 above, except for the body part being a face. Official notice is being taken that psoriasis is know to affect the face of a human. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have performed the method of George et al.’085 in view of Frank et al.’873 by receiving an image of a face of the human, as this would allow psoriasis on the human’s face to be detected.
Furthermore, Petit’444 discloses analyzing images of a face to determine skin disorders (section [0044]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have performed the method of George et al.’085 in view of Frank et al.’873 by receiving an image of a face of the human, as this would help determine if the human’s face is showing signs of a skin disorder.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over George et al.’085 in view of Frank et al.’873, as applied to claim 1, further in view of Chen et al.’512 (US Pub No. 2021/0174512 – previously cited).
George et al.’085 in view of Frank et al.’873 discloses all of the elements of the current invention, as discussed in paragraph 7 above, except for cropping the image into several partial images, wherein each partial image contains a pattern feature. Chen et al.’512 teaches segmenting an image into several partial images in order to calculate a Body Surface Area score indicative of the severity of psoriasis (see ABSTRACT, and section [0002]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of George et al.’085 in view of Frank et al.’873 to include cropping the image into several partial images (each partial image would contain a pattern feature), as this would allow a Body Surface Area score to be calculated, thus providing an additional metric by which to determine a psoriasis severity.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over George et al.’085 in view of Frank et al.’873, as applied to claim 1, further in view of Ivanov et al.’876 (US Pub No. 2022/0088876).
George et al.’085 in view of Frank et al.’873 discloses all of the elements of the current invention, as discussed in paragraph 7 above, except for the training comprising training a plurality of data-driven models, wherein the method further comprises selecting, from the plurality of data-driven models, a model with a lowest deviation from a validation data set. Ivanov et al.’876 teaches a method wherein a plurality of data-driven models are trained, and from the plurality of trained data-driven models, a model with a lowest deviation from a validation data set is selected (sections [0107-0111]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of George et al.’085 in view of Frank et al.’873 to include training a plurality of data-driven models and selecting from the plurality of data-driven models a model with a lowest deviation from a validation set as it would merely be combining prior art elements according to known methods to yield predictable results. The modification to George et al.’085 in view of Frank et al.’873 would result in the most accurately trained data-driven model being used on the human.
Furthermore, Official notice is being taken that it is well known in the art to train multiple data-driven models and to select a final data-driven model based on the accuracy results of the trained data-driven models; the data-driven model providing the highest accuracy results is selected for subsequent data analysis.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over George et al.’085 in view of Frank et al.’873, as applied to claim 1, further in view of Abid’567 (USPN 10,878,567 – previously cited).
George et al.’085 in view of Frank et al.’873 discloses all of the elements of the current invention, as discussed in paragraph 7 above, except for retraining the trained data-driven model with new data sets. Abid’567 teaches retraining a data-driven model with new data sets in order to improve performance of the data-driven model (col. 10, lines 36-44). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of George et al.’085 in view of Frank et al.’873 to include retraining the trained data-driven model with new data sets, as this would improve performance of the data-driven model.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over George et al.’085 in view of Frank et al.’873, as applied to claim 11, further in view of Larimer et al.’283 (US Pub No. 2020/0250283 – previously cited).
George et al.’085 in view of Frank et al.’873 discloses all of the elements of the current invention, as discussed in paragraph 7 above, except for the processor being or comprising a secure enclave processor. It is noted that George et al.’085 discloses that its device is capable of being implemented as a smartphone. Official notice is being taken that the use of a secure enclave processor in smartphones is known.
Furthermore, Larimer et al.’283 teaches a smartphone including a secure enclave processor that is configured to run a secure operating system (section [0064]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have incorporated a secure enclave processor in the smartphone of George et al.’085 in view of Frank et al.’873, as the secure enclave processor would allow the smartphone to run a secure operating system.
Response to Arguments
Applicant's arguments filed 06 July 2026 have been fully considered.
Regarding the rejections of the claims under 35 U.S.C. 112(b), while the amendments have overcome the previous rejections, as noted in paragraph 3 above, they have warranted a new rejection.
Regarding the rejection of claim 9 under 35 U.S.C. 101, Applicant’s arguments are not persuasive. The inclusion of the subject matter of claim 1 into claim 9 does not overcome the Abstract Idea rejection. The Abstract Idea is present in claim 9, which is why claim 9 was rejected under 35 U.S.C. 101. It is noted that claim 10 was previously inadvertently rejected under 35 U.S.C. 101, and that the rejection of claim 10 has been withdrawn.
Regarding the rejections of the claims in view of the previously cited prior art, Applicant’s arguments are moot as the amendments to the claims have necessitated new prior art rejections.
It is noted that the Applicant has not traversed the Examiner’s previous assertions of Official Notice. As such, the Official Notice statements are taken to be admitted prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Baumann et al.’765 (US Pub No. 2017/0265765) teaches that data-driven models can be trained using user-specific datasets (section [0036]).
Tanriover et al.’201 (US Pub No. 2019/0038201) teaches personalizing a data-driven model by using a training dataset specific to a user, and also teaches updating an already generated data-driven model with more data from the specific user (section [0034]).
Amitava et al.’604 (US Pub No. 2019/0188604) teaches that training a data-driven model with a training dataset specific to a user provides a more accurate model (section [0030]).
Jarosiewicz et al.’007 (US Pub No. 2020/0298007) teaches training a plurality of data-driven models and selecting the best model of the plurality of data-driven models (section [0121]).
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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/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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