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 .
Election/Restrictions
Applicant’s election without traverse of the invention of Group I (claims 1-11) in the reply filed on May 20, 2026 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) received on June 20, 2024 has been considered by examiner.
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 1-11 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1-11 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a process (i.e., a method).
Although claims 1-11 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-11 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 recite (emphasized in bold and italics):
a) retrieving, by a server, a scratch dataset, the scratch dataset comprising data records of corresponding scratch events of a patient population with a skin disease;
b) retrieving, by the server, a contextual dataset comprising data records of additional information associated with the corresponding scratch events;
c) training, by the server, a prediction model using a supervised training approach based on the scratch dataset and the contextual dataset;
d) receiving, by the server, periodic data indicating occurrences of scratch events for a time period of a particular patient with the skin disease;
e) feeding, by the server, the received periodic data into the trained prediction model; and
f) in response to the prediction model outputting a likelihood of a flare, transmitting, by the server, an alert notification to a user device of the particular patient
The identified limitations of a),b), d), e), and f), using their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting servers, nothing in the claim elements precludes the steps from being performed practically in the mind. For example, the steps encompass a user collecting and evaluating scratch datasets comprising scratch events related to skin disease for the purpose of predicting and transmitting the likelihood of a flare for a patient. The limitations cover observation, evaluation, judgement and opinion and fall within the Mental Processes groupings of abstract ideas. Thus, the claimed invention recites a judicial exception.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
As a whole, the additional elements recite training, by the server, a prediction model using a supervised training approach based on the scratch dataset and the contextual dataset. The servers and training step are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does 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.
Dependent claims 2-11, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. For instance, claims 2-11 recite collecting and evaluating scratch datasets comprising scratch events related to skin disease for the purpose of predicting and transmitting the likelihood of a flare for a patient, which is a mental process.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". For example, the steps require no more than a conventional computer to perform generic computer functions. As stated above, the servers and training step are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-11, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2-11 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Ikoma (US 2018/0228427 A1) in view of Neumann (US 2021/0166802 A1).
Regarding claim 1, Ikoma discloses a computer-implemented method comprising:
retrieving, by a server, a scratch dataset, the scratch dataset comprising data records of corresponding scratch events of a patient population with a skin disease (Paragraph [0037]: Data indicative of the movement detected by the actigraph sensor 155 may be stored in the memory 170 and sent via the device interface 180 to the controller 250);
retrieving, by the server, a contextual dataset comprising data records of additional information associated with the corresponding scratch events ((Paragraph [0038]: the controller 250, via the device interface 280, receives the data indicative of the measured movement and determines, via the processor 260, whether the movement constitutes a scratching movement based on the received data));
receiving, by the server, periodic data indicating occurrences of scratch events for a time period of a particular patient with the skin disease (Paragraph [0037]: After a predetermined amount of time (for example, a period corresponding to a user or patient's entire sleep session), the wearable device 150 is configured to send the data indicative of the movement detected by the actigraph sensor 155 to the controller).
in response to the prediction model outputting a likelihood of a flare, transmitting, by the server, an alert notification to a user device of the particular patient (Paragraph [0038]: the system 100 alerts the user or patient to the scratching behavior ).
Ikoma discloses the limitations above. Ikoma does not explicitly disclose:
training, by the server, a prediction model using a supervised training approach based on the scratch dataset and the contextual dataset;
feeding, by the server, the received periodic data into the trained prediction model.
Neumann teaches:
training, by the server, a prediction model using a supervised training approach based on the scratch dataset and the contextual dataset (Paragraph [0093]: model 152 may include supervised machine-learning module 304. Supervised machine-learning algorithms, may include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs);
feeding, by the server, the received periodic data into the trained prediction model (Paragraph [0096]: receiving symptomatic training data 124 by computing device 104. Symptomatic training data 124 includes a plurality of symptoms… , user symptom 132 may include a description of intense itching, and a tickling feeling that a user experiences from movement on user's head and scalp….) Machine-learning algorithm may include any of the machine-learning algorithms as described above, including for example a supervised machine-learning algorithm).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Ikoma to disclose training, by the server, a prediction model using a supervised training approach based on the scratch dataset and the contextual dataset; and feeding, by the server, the received periodic data into the trained prediction model as taught by Neumann. Using the supervised training approach of Neumann would provide improved accuracy in predicting scratch events in order to better manage flares.
Regarding claim 2, Ikoma discloses the computer-implemented method of claim 1, further comprising:
in response to the prediction model outputting a likelihood of a flare, transmitting, by the server, a second alert notification to a clinician dashboard (Paragraph [0045]).
Regarding claim 3, Ikoma discloses the computer-implemented method of claim 2, further comprising:
receiving, by the server, a patient communication message provided at the clinician dashboard in response to the second alert notification (Paragraph [0045]); and
transmitting, by the server, the patient communication message to the user device of the particular patient (Paragraph [0044]).
Regarding claim 4, Ikoma discloses the computer-implemented method of claim 3, wherein the patient communication message comprises an indication for the particular patient to communicate with a clinician (Paragraph [0046]).
Regarding claim 5, Ikoma discloses the computer-implemented method of claim 3, wherein the patient communication message is for a prescription medication to control the flare (Paragraph [0045]).
Regarding claim 6, Ikoma discloses the computer-implemented method of claim 3, wherein the periodic data is received from a healthcare application running on the user device (Paragraph [0035]).
Regarding claim 7, Ikoma discloses the computer-implemented method of claim 6, wherein the patient communication message is transmitted by the server to the healthcare application running on the user device (Paragraph [0044]).
Regarding claim 8, Ikoma discloses the method of claim 1, wherein the data records of the corresponding scratch events comprise the frequency of the scratch events (Paragraph [0038]).
Regarding claim 9, Ikoma discloses the method of claim 1, wherein the data records of the corresponding scratch events comprise the severity of the scratch events (Paragraph [0041]).
Regarding claim 10, Ikoma discloses the method of claim 1, wherein the data records of the additional information comprise whether a corresponding scratch event was associated with a flare (Paragraph [0041]).
Regarding claim 11, Ikoma discloses the method of claim 1, wherein the data records of the additional information comprise an association of a corresponding scratch event with at least one of weather, food intake, other infections, allergens, fabrics worn, presence of saliva at the skin disease site, dry skin, sweat level, stress level, exercise level, or hormonal level (Paragraph [0046]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINYERE MPAMUGO whose telephone number is (571)272-8853. The examiner can normally be reached Monday-Friday, 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685
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