DETAILED ACTION
This action is in reference to the communication filed on 18 JUN 2026.
Amendments to claims 1, 6, 11, 16, 21 are entered.
Claims 1-21 are present and have been examined.
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 1-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-21, the independent claim(s) 1, 11, 21 recite(s) a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-21 the independent claim(s) (claims 1, 11, 21) is/are directed, in part, to:
Claim 1: A method, comprising:
receiving,
generating an indication by applying a machine-learned model to the input image, the indication representing a likelihood whether the input image shows two or more skin abnormalities or a single skin abnormality, at last a training image and one or more labels indicating whether the training image presents two or more skin abnormalities or a single skin abnormality;
responsive to the indication indicating a single skin abnormality in the input image:
generating
responsive to the indication indicating two or more skin abnormalities in the input image, outputting a message for display without outputting a .
These claim elements are considered to be abstract ideas because they are directed to a method of mental processes, which includes concepts performed in the mind such as observation, evaluation, judgment and opinion. Generating an indication of a likelihood, generating a diagnosis from a picture, and outputting a message are all examples of concepts which can be performed in the human mind.
The claims further recite the concept of certain methods of organizing human activity, including managing personal behavior or relationships/interactions between people, including following rules and/or instructions. In the instant case the claims recite a method of receiving a request and subsequently providing a diagnosis and communication thereof to a user in response to the request, i.e. following a set of instructions to do so.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, mathematical concepts, and/or certain methods of organizing human activity, the claims is/are found to recite an abstract idea. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, each of the claim(s) recite(s) additional elements such as a “client device” with a ”display,” and wherein the diagnosis is “computerized.” Claim 11 further recites a “non-transitory computer program product comprising a computer readable storage medium comprising a computer program code;” and claim 21 recites “a computer system comprising one or more processors; a non-transitory computer program product comprising a computer readable storage medium comprising computer program code for execution by one or more processors.” Claims 1, 11, 21 have been amended to recite that the machine learning model is “trained.” The client device, display, and “computerized” diagnosis common to all claims, as well as the computer program products/CRM/processors in claims 11, 21 are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examienr finds similarly with regard to the machine learning model being “trained.” Examiner finds no improvement to the functioning of the computer, the machine learning model/training thereof, or any other technology or technical field in the elements as identified above and as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). Further, Examiner notes that storage of data (i.e. via the CRM) and the display of a message on a screen, as well as the general sending/receiving of data from the client device, are generally considered to be examples of adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements common to claims 1, 11, 21, including “client device” with a ”display,” and wherein the diagnosis is “computerized.” Claim 11 further recites a “non-transitory computer program product comprising a computer readable storage medium comprising a computer program code;” and claim 21 recites “a computer system comprising one or more processors; a non-transitory computer program product comprising a computer readable storage medium comprising computer program code for execution by one or more processors.” Claims 1, 11, 21 have been further amended to recite that the model is “trained” using a given set of data.
When considered individually, the above identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0060] The client device 110 may be a computing device such as a smartphone with an operating system such as ANDROID® or APPLE® IOS®, a tablet computer, a laptop computer, a desktop computer, or any other type of network-enabled device that includes or can be configured to connect with a camera. In another embodiment, the client device 110 is a headset including a computing device or a smartphone camera for generating an augmented reality (AR) environment to the user, or a headset including a computing device for generating a virtual reality (VR) environment to the user. A typical client device 110 includes the hardware and software needed to connect to the network 122 (e.g., via WiFi and/or 4G or 5G or other wireless telecommunication standards).
[0070] The training module 920 trains the set of weights for the feature extractor model and the feature classifier model described in conjunction with FIG. 4. In one embodiment, the feature extractor model and the feature classifier model are trained by training a neural network architecture including a first portion and a second portion. The set of weights for the first portion of the neural network architecture may be stored as the set of weights for the feature extractor model and the set of weights for the second portion may be stored as the set of weights for the feature classifier model. The set of weights for the feature classifier model are subsequently retrained using a plurality of components extracted from the training images.
[080] While the diagnosis model can generally be trained using diagnostic training data (e.g., training images and corresponding health condition labels), the training module 925 further trains the diagnosis model to incorporate the indicator functionality by including indicational training data for the diagnosis model in addition to the diagnostic training data.
[0093] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
[0094] Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. Examiner further notes that the model’s training is only vaguely disclosed in the specification in terms of output/results/description of the training set at most. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-10, 12-20:
Dependent claims 2, 12, do not recite any additional abstract ides, but do recite the non-abstract element of a database. Examiner finds that the storage or retrieval of information from a database is insignificant extra solution activity, and as such does not support a finding of a practical application or significantly more.
Dependent claims 3-10, 13-20 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as additional clarification of the mathematical relationships such as thresholds or modeling, as well as the further steps of providing messages based on those relationships, and the mental determinations of threshold considerations. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Non-Obvious Subject Matter
Claims 1-20 as amended are believed to be free of the prior art.
The closest prior art of record is believed to be:
Flank (US 20190220738 A1)
Reicher (us20160364539)
Stoval (US202000250814)
Flank as cited teaches identifying one or more abnormalities of skin conditions in an image, and generating a likelihood of each potential diagnosis of disease in the picture – i.e. two corresponding probabilities of two separate diagnoses being output to the requesting user. Flank does this using two neural networks trained on a plurality of images for a given skin condition or abnormality.
Reicher teaches a machine learning model specifically trained using a training image for the abnormalities, and using that image to determine one or more potential diagnoses, including a probability of a severity of a given abnormality. Reicher also teaches the concept of a differential diagnoses causing the system to reference the model for several diagnoses for a given image, and if the number of possibilities is too great the model/system will prompt the user for additional information to refine the diagnosis (i.e. in the event of two or more possible diagnoses the system will “output a message for display without outputting a computerized diagnosis for the two or more abnormalities.”)
Stoval teaches using a neural network to monitor changes in conditions over time in an image, and teaches wherein possible outcomes are malignant/benign disease, normal, or inconclusive. Based on one or more of these outcomes, the system will also ask for additional information if needed prior to outputting a diagnosis.
However, the references cited when taken separately and/or in combination, do not fairly teach the newly amended limitation clarifying that the machine learned model first generates a likelihood of two or more abnormalities or a single abnormality prior to an actual diagnosis for any abnormality being output. Examiner finds this a distinction over the prior art, as the cited references either teach a probability of each possible diagnosis being likely, or a probability of a plurality of diagnoses being appropriate. This is not analogous to determining a possible quantity of conditions prior to outputting a diagnosis. Examiner also finds Applicant’s remarks clarifying the amendments on page 10 to be persuasive, in that there is a difference between outputting a condition and a probability of the specific condition, vs. whether or not two ore more abnormalities are present in the target image.
Response to Arguments
Applicant’s remarks as filed on 18 JUN 2026 have been fully considered.
Examiner notes Applicant’s remarks regarding the interview held on 18 JUN 2026.
Applicant’s amendments to the claims has negated the objection previously presented.
Applicant’s remarks/amendments are 112b are found persuasive and as such the rejection is withdrawn.
Applicant’s remarks regarding the prior art and the amendments are found persuasive as discussed above.
Applicant’s remarks regarding the rejection under 35 USC 101 begin on page 12 of the remarks, with a partial restatement of USPTO policy regarding subject matter eligibility. Examiner respectfully disagrees with Applicant’s remarks regarding a purported improvement – Examiner respectfully submits that while the claims perhaps recite an improvement in diagnosis of a skin abnormality, this is not itself a technical problem nor improvement per the analysis referenced by Applicant. The references to Applicant’s specification on page 12 support Examiner’s position – [025] highlights the improvements to a diagnosis process, rather than any sort of “computerized” diagnosis or other technical improvement.
Applicant’s remarks regarding Desjardins, on page 13, are also found unpersuasive. Examiner does not find that the model itself is improved in any way, in that changing an output of the learned model is not analogous to improving or overcoming a known problem in machine learning nor is any such improvement recited in the claims. That the model itself is trained on a specific set of data to perform at task is not sufficient to support a finding of eligibility per current USPTO guidance. As per the remaining remarks on page 13, Examiner again respectfully disagrees that any improvement to the functioning of the model itself is found, nor any resources conserved.
Conclusion
THIS ACTION IS MADE FINAL. 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 KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
