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 Objections
Claim 22 is objected to because of the following informalities: Correction of “The method as set forth in claim 1” to read as “The method as set forth in claim 10” is suggested, as it appears there is a typographical error with regard to claim dependency. Appropriate correction is required.
Response to Arguments
Applicant’s arguments, see pages 9-10 of the Remarks, filed 01/29/2026, with respect to the rejection of claims 1, 10 and 17 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US 2018/0137250 A1 (Ding et al.), which teaches the limitation of “select a healthcare provider device among a plurality of healthcare provider devices based on the one or more predictions of the ailment”, as set forth below.
Regarding the previous rejection of claims 4 and 19 under 35 USC 112(d), the rejection is maintained below because Applicant’s amendment does not sufficiently overcome the rejection. Claims 4 and 19 contain redundant subject matter (“the inputs from the healthcare provider device include a confirmation or a rejection of the image”) already claimed in each of their base claims 1 and 17 (“the inputs from the healthcare provider device including at least a confirmation or a rejection of the image”), respectively.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims each recite “wherein the image prediction machine learning model is a separate large language model from the ailment prediction machine learning model.” Neither Applicant’s specification nor the drawings appear to describe either the image prediction machine learning model or the ailment prediction machine learning model as “large language models”, nor does the specification state that they are each “separate” models, as claimed. The drawings also do not appear to indicate that the models are “separate”, and only refers to all illustrated models as “machine learning models”, from which it cannot be appropriately inferred that the “machine learning models” are separate and distinct. Therefore, the claims fail to meet the written description requirement. For purposes of examination, the limitations will be given their broadest reasonable interpretation in view of available prior art.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 4 and 19 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The additional limitations of each of claims 4 and 19 do not further limit those of claims 1 and 17, respectively, because both claim 1 and claim 17 already recite the limitation "the inputs from the healthcare provider device including at least a confirmation or a rejection of the image”. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-5, 7-14 and 16-23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 8,548,828 B1 (“Longmire”; applicant-submitted prior art) in view of US PG PUB. 2018/0137250 A1 (hereinafter “Ding”), and further in view of “MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery” (hereinafter “Bihorac”; published 2019).
Regarding claim 1, Longmire teaches a system for providing telehealth services to a patient, the system comprising (Longmire, Abstract, Fig. 1, Fig. 6, col.5, l.59 – col. 6, l.6):
a computing device that includes at least one processor and at least one memory including instructions that, when executed by the at least one processor, cause the at least one processor to (Longmire, col.6, l.1-6; “A processor 110 comprising of several modules houses user registration module 102, user communication module 104, healthcare provider registration module 106 and health care provider communication module 108. Cloud based server or storage device 112 enables the disease management system 100 to be accessible and unified for ease of management and accessibility from anywhere in the world.”):
receive inputs from a patient device, the inputs including at least health data of the patient and an image of an ailment (Longmire, col.10, l.32-50; “Once the user or patient has logged in they may upload a user medical history 614 to enable complete the registration by at least one of a self-filling form and prompt by offering predefined question and answer. The user may also attach their data 616 for further processing. The image processing module 418 may accept or reject the image taken by a camera after quality checks and permit the user to upload an image of a particular region of a user body.”);
with an image prediction machine learning model, determine if the image is of sufficient quality to generate one or more predictions of the ailment (Longmire, col.10, l.32-50; “The image processing module 418 may accept or reject the image taken by a camera after quality checks ”);
with an ailment prediction machine learning model, generate one or more predictions of the ailment based on the health data of the patient and the image of the ailment; transmit the one or more predictions of the ailment and the image to a healthcare provider device; establish communication between the patient device and the healthcare provider device; receive inputs from the healthcare provider device, the inputs from the healthcare provider device including at least a confirmation or a rejection of the image and including at least one or more predictions of the ailment (Longmire, col.5, l.23-30, col.10, l.30 – col.11, l.45, col.12, l.55-65; “An HCP can review a case without any private health information simply to evaluate adequacy of image quality. The HCP can deny or accept a case based on image quality or appropriateness of the possible diagnosis for telemedicine. If the image is not of adequate image quality to render a diagnosis, the HCP can notify the patient that the images need to be revised.” and “As stated, machine learning involving algorithms for image processing provide a machine-suggested diagnosis and treatment for the HCP. The HCP can decline or accept the machine-created diagnosis and treatment suggestion.”).
Although Longmire teaches selecting a healthcare provider device from among a plurality of healthcare provider devices and transmitting the one or more predictions of the ailment and the image to the selected healthcare provider device (Longmire, col.5, l.15-30, col.10, l.60- col.11, l.10; “The user may tap on the application, fill out their symptoms, upload image…let the Disease management consortium Suggest the best match” wherein “The disease management system may also select and assign providers automatically 620. Once a provider has been elected or selected by the system the health data is sent to the provider 622.”), Longmire does not expressly teach the limitations as further claimed, but, in an analogous field of endeavor, Ding does as follows.
Ding teaches select a healthcare provider device among a plurality of healthcare provider devices based on the one or more predictions of the ailment (Ding, ¶0061-0063; “The data management module matches a corresponding treatment doctor from the medical knowledge base according to the disease diagnosis result so as to generate a medical guide result and feeds back the medical guide result to the mobile medical terminal through the mobile medical cloud server connection interface.”).
Ding is considered analogous art because it pertains to machine learning based diagnostic support. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by Longmire to select the provider to which the patient health data is sent for further analysis based on the knowledge-based disease diagnosis result, as taught by Ding, in order to guide the patient in obtaining a more accurate diagnosis through review by an appropriate doctor (Ding, ¶0051, 0061-0063).
The combination of Longmire in view of Ding does not expressly teach the limitations as further claimed, but, in an analogous field of endeavor, Bihorac does as follows.
Bihorac teaches: train, based on the inputs from the healthcare provider, the image prediction machine learning model and the ailment prediction machine learning model (Bihorac, p.653, Predictive Analytics Workflow, Fig. 1B; “This platform resides in a secure environment, and in real-time integrates and transforms EHR data, runs predictive algorithms, produces outputs for physicians, inputs their feedback, and prospectively collects data for the future retraining of the prediction models”).
Bihorac is considered analogous art because it pertains to machine-learning based diagnostics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system taught by the combination of Longmire and Ding to include retraining the diagnostic machine learning model based on physician feedback, as taught by Bihorac, in order to continuously optimize the diagnostic prediction model.
Regarding claim 2, claim 1 is incorporated, and Longmire in the combination further teaches wherein the at least one memory further includes instructions that, when executed by the at least one processor, cause the processor to: with the ailment prediction machine learning model, generate one or more predictions of a treatment plan for the patient; transmit the one or more predictions of the treatment plan for the patient to the healthcare provider device; and wherein the inputs from the healthcare provider device include a confirmation or rejection of the one or more predictions of the treatment plan for the patient (Longmire, col.12, l.55-65; “As stated, machine learning involving algorithms for image processing provide a machine-suggested diagnosis and treatment for the HCP. The HCP can decline or accept the machine-created diagnosis and treatment suggestion.”)
Regarding claim 3, claim 1 is incorporated, and Longmire in the combination further teaches further including a database that retains images for comparison by the image prediction machine learning model and the ailment prediction machine learning model (Longmire, col.11, l.5-11; “Storing the data generated by the user, medical history, forms, the image of the particular part of the body and the pattern for differential diagnosis in at least one of a local database and cloud server is essential and is performed on a routine basis.”).
Regarding claim 4, claim 1 is incorporated, and Longmire in the combination further teaches wherein the inputs from the healthcare provider device include a confirmation or a rejection of the image (Longmire, col.5, l.23-30; “An HCP can review a case without any private health information simply to evaluate adequacy of image quality. The HCP can deny or accept a case based on image quality or appropriateness of the possible diagnosis for telemedicine. If the image is not of adequate image quality to render a diagnosis, the HCP can notify the patient that the images need to be revised.”).
Regarding claim 5, claim 1 is incorporated, and Longmire in the combination further teaches wherein the at least one memory further includes instructions that, when executed by the at least one processor, cause the processor to: with the image prediction machine learning model, determine if the image of the ailment is a false image that was not taken by the patient; and in response to a determination that the image is a false image, request an additional image from the patient device (Longmire, col.10, l.42-60; “The uploaded image is authenticated by verifying from the database if it is a stock image or real image of the body of the person. The verification if it is the same as the body of the person may be performed by asking the user/patient to take images from various regions and compare the skin architecture with stored and other images.”)
Regarding claim 7, claim 1 is incorporated, and Longmire in the combination further teaches wherein the at least one memory further includes instructions that, when executed by the at least one processor, cause the processor to: enhance the image prior to transmitting the image to the healthcare provider device (col.9, l.35-45; “Data visualization and HCP image interaction module 408 allows the provider and the user to preview the image, expand on the image and rotate the image for clarity and observation for diagnosing. Many platforms may be used to display the image for any given operating system. The disease management system may recognize the optimal program suitable for a device of use. Such as android application may be compatible to certain display Software and the disease management system may suggest the user and the provider to use the optimal display program or mode.”).
Regarding claim 8, claim 1 is incorporated, and Longmire in the combination further teaches wherein the computing device is a server that is remote from the patient device and from the healthcare provider device (Longmire, col.6, l.1-8; “Cloud based server or storage device 112 enables the disease management system 100 to be accessible and unified for ease of management and accessibility from anywhere in the world.”).
Regarding claim 9, claim 1 is incorporated, and Longmire in the combination further teaches wherein the computing device is associated with the healthcare provider device (Longmire, col.6, l.1-8; “A processor 110 comprising of several modules houses user registration module 102, user communication module 104, healthcare provider registration module 106 and health care provider communication module 108. Cloud based server or storage device 112 enables the disease management system 100 to be accessible and unified for ease of management and accessibility from anywhere in the world.”).
Claim 10 recites a method having features which correspond to the elements of the system recited in claim 1, the rejection of which is applicable here.
Claim 11 recites a method having features which correspond to the elements of the system recited in claim 2, the rejection of which is applicable here.
Claim 12 recites a method having features which correspond to the elements of the system recited in claim 3, the rejection of which is applicable here.
Claim 13 recites a method having features which correspond to the elements of the system recited in claim 4, the rejection of which is applicable here.
Claim 14 recites a method having features which correspond to the elements of the system recited in claim 5, the rejection of which is applicable here.
Claim 16 recites a method having features which correspond to the elements of the system recited in claim 7, the rejection of which is applicable here.
Claim 17 recites a system having features which correspond to the elements of the system recited in claim 2, the rejection of which is applicable here, and Longmire in the combination further teaches a cloud computing device (Longmire, Fig. 1, col.6, l.1-10; “cloud based server or storage device 112”) and the inputs from the healthcare provider device including a confirmation or rejection of the one or more predictions of the ailment, and a confirmation or rejection of the one or more predictions of the treatment plan (Longmire, col.5, l.23-30, col.10, l.30 – col.11, l.45, col.12, l.55-65; “As stated, machine learning involving algorithms for image processing provide a machine-suggested diagnosis and treatment for the HCP. The HCP can decline or accept the machine-created diagnosis and treatment suggestion.”).
Regarding claim 18, claim 17 is incorporated, and Longmire in the combination further teaches wherein the at least one memory further includes instructions that, when executed by the at least one processor, cause the processor to: in response to a determination that the image of the ailment is not of sufficient quality to generate the one or more predictions of the ailment, request an additional image from the patient device (Longmire, col.5, l.21-30, col.7, l.15-20; “The user may be prompted to take proper images, if the image quality is not good requested to retake, fill all the sections in the medical history form, symptoms, and other pertinent information regarding their ailment.”).
Claim 19 recites a system having features which correspond to the elements of the system recited in claim 4, the rejection of which is applicable here.
Claim 20 recites a system having features which correspond to the elements of the system recited in claim 5, the rejection of which is applicable here.
Regarding claim 21, claim 1 is incorporated, and Longmire in the combination further teaches wherein the image prediction machine learning model is a separate large language model from the ailment prediction machine learning model (Longmire, Fig. 4, Fig. 5, col.10, l.14- col. 11, l.41; “The image processing module 418 may accept or reject the image taken by a camera after quality checks”, and differential diagnosis is provided by differential diagnosis module 504).
Claim 22 recites a method having features which correspond to the elements of the system recited in claim 21, the rejection of which is applicable here.
Claim 23 recites a system having features which correspond to the elements of the system recited in claim 21, the rejection of which is applicable here.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The additionally cited references pertain generally to machine learning-based image quality assessment and diagnosis.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMAH A BEG whose telephone number is (571)270-7912. The examiner can normally be reached M-F 9 AM - 5 PM.
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/SAMAH A BEG/Primary Examiner, Art Unit 2676
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