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 .
Notice to Applicants
This action is in response to the Restriction Election filed on 12/13/2025.
Claims 1-20 are pending.
Priority
The Application claims priority to PCT/EP2020/077761 with filing date 10/02/2020, and also claims priority to Provisional Application 62/910,158 with filing date 10/03/2019, both of which are acknowledged.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 04/04/2022 has been fully considered by the examiner.
Restriction/Election
The examiner thanks Applicant for their careful consideration of the Restriction Requirement mailed on 08/27/2025.
Applicant’s election without traverse of Group I (claims 18-20) in the reply filed on 12/13/2025 is acknowledged.
Claims 1-17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/13/2025.
Claim Objections
Claims 18-20 are objected to because of the following informalities:
Regarding claim 18, in line 13, “obtaining, from the trained ML,” should read “obtaining, from the trained ML model,” (emphasis added).
Regarding claims 19-20, these dependent claims are objected to based on their dependency on claim 18 above.
Appropriate correction is required.
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 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Analysis for claim 18 is provided in the following. Claim 18 is reproduced in the following (annotation added):
A computer-implemented method comprising:
receiving, by an image sensor of a computing device, a plurality of images of a subject;
receiving, by the computing device, at least one current dermatological treatment plan used by the subject during a current time period;
generating, by a neural network disposed within the computing device, a dermatological condition metric related to a severity of at least one dermatological condition exhibited by the subject in the plurality of images;
providing, to a trained machine learning (ML) model, the at least one current dermatological treatment plan and the dermatological condition metric,
wherein the trained ML model is trained to receive current dermatological treatment plans and dermatological condition metrics for subjects and predict dermatological treatment plans for the subjects during future time periods;
obtaining, from the trained ML, a dermatological treatment plan for the subject during a future time period;
and providing the dermatological treatment plan.
Step 1: Does the claim belong to one of the statutory categories? Claim 18 is directed to a method, which is a statutory category of invention (YES).
Step 2A Prong One: Does the claim recite a judicial exception? Parts d and f can be regarded as reciting mental processes that can be practically performed in the human mind. Part d recites generating a dermatological condition metric related to a severity of at least one dermatological condition exhibited by the subject in the plurality of images, and part f recites predicting dermatological treatment plans for the subjects during future time periods.
Other than “by a neural network disposed within the computing device” and “wherein the trained ML is trained to”, nothing in the claims preclude a mental determination of any kind of severity of a dermatological condition exhibited by images of a subject and mentally predicting any kind of dermatological treatment plan for the subject, respectively. Note that the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer (see MPEP 2106.04(a)(2).III, fourth paragraph).
Furthermore, predicting and providing the dermatological treatment plans is not regarded as applying or using the recited judicial exceptions to effect a particular treatment/prophylaxis. For instance, the broad claim language of “dermatological conditions” encompasses any medical condition related to the field of dermatology. Furthermore, the claim only recites the prediction and output of the treatment plan, not positively applying the plan to treat the condition. See MPEP 2106.04(d)(2) (YES).
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? Parts a-c recite data gathering performed by a computerized system at a high level of generality. Part e merely recites providing the gathered data and the generated condition metric to the trained machine learning model. Finally, parts g-h recite mere data output (NO).
Step 2B: Does the claim as a whole amount to significantly more than the recited exception? The claim as a whole recites a computerized system at a high level of generality that performs data gathering (parts a-c and e), performs mental processes using said data (parts d and f), and outputs the results of performing a mental process (parts g-h) (NO). Claim 18 is not eligible.
Claim 19 recites receiving patient information related to the subject and providing it to the trained ML model, which is regarded as mere data gathering. Claim 19 further recites that the trained ML model is trained to predict dermatological treatment plans for the subjects during future time periods, which is a mental process that can be practically performed in the human mind. Claim 19 is not eligible.
Claim 20 recites receiving at least one previous dermatological treatment plan and providing it to the trained ML model, which is regarded as mere data gathering. Claim 20 further recites that the trained ML model is trained to predict dermatological treatment plans for the subjects during future time periods, which is a mental process that can be practically performed in the human mind. Claim 20 is not eligible.
Claim Rejections – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 18-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Amir et al. (U.S. Publ. US-2018/0014777-A1).
Regarding claim 18, Amir discloses a computer-implemented method (see figures 1-2) comprising:
receiving, by an image sensor of a computing device, a plurality of images of a subject (see figure 1, step 102, figure 2, and paragraphs 0086-0087, where images of a patient's face can be acquired by an image sensor 214 of a computing device, such as a skin treatment applicator 202);
receiving, by the computing device, at least one current dermatological treatment plan used by the subject during a current time period (see figure 1, steps 102, 112 and paragraphs 0089 and 0155, where the previous treatment plan is obtained or already present from the previous iteration of step 112 of figure 1);
generating, by a neural network disposed within the computing device, a dermatological condition metric related to a severity of at least one dermatological condition exhibited by the subject in the plurality of images (see figure 2, step 104, figure 3, and paragraphs 0090-0092, 0098-0100, and 0110-0144, where the facial images can be analyzed to determine values of variable skin characteristics and/or worsening of said characteristics relative to earlier skin states);
providing, to a trained machine learning (ML) model, the at least one current dermatological treatment plan and the dermatological condition metric, wherein the trained ML model is trained to receive current dermatological treatment plans and dermatological condition metrics for subjects and predict dermatological treatment plans for the subjects during future time periods (see figure 1, step 106 and paragraphs 0125 and 0133-0139, where the current facial skin profile and current or past treatment plans are input to a machine learning classifier that predicts new treatment plans);
obtaining, from the trained ML, a dermatological treatment plan for the subject during a future time period; and providing the dermatological treatment plan (see figure 1, steps 108-110, figure 2, and paragraphs 0140, 0142, 0151, and 0154, where instructions for how to operate a skin treatment applicator 202 are obtained from the model and presented to the user, such as through a GUI 236).
Regarding claim 19, Amir discloses receiving, by the computing device, patient information related to the subject (see figure 1, step 102 and paragraphs 0086, 0088, and 0109, where additional patient data can be input, such as age, gender, location, demographics, and historical or scheduling records),
and providing, to the trained ML model, the patient information (see paragraph 0125, where the additional patient data can also be input with the facial data and treatment plans),
wherein the trained ML model is trained to receive current dermatological treatment plans, dermatological condition metrics, and patient information for subjects and predict dermatological treatment plans for the subjects during future time periods (see figure 1, step 106 and paragraphs 0125 and 0133-0139, where the current facial skin profile, current or past treatment plans, and additional patient data are input to a machine learning classifier that predicts new treatment plans).
Regarding claim 20, Amir discloses receiving, by the computing device, at least one previous dermatological treatment plan used by the subject during a previous time period (see figure 1, steps 102, 112 and paragraphs 0089, 0092, and 0155, where the previous treatment plan is obtained or already present from the previous iteration of step 112 of figure 1),
and providing, to the trained ML model, the at least one previous dermatological treatment plan (see paragraph 0136, where the current or previous treatment plans can also be input to the model),
wherein the trained ML model is trained to receive current dermatological treatment plans, dermatological condition metrics, and previous dermatological treatment plans for subjects and predict dermatological treatment plans for the subjects during future time periods (see figure 1, step 106 and paragraphs 0125 and 0133-0139, where the current facial skin profile and current or past treatment plans are input to a machine learning classifier that predicts new treatment plans).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS JOHN HELCO whose telephone number is (703)756-5539. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached at telephone number 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS JOHN HELCO/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667