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 .
Claims 1-20 are pending. Claims 1-20 are rejected herein.
Priority
This Application claims priority to provisional application # 63/633,201. The effective filing date of this application is 12 April 2024.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Messaging system – claim 20
Movie script integration module – claim 14
Sufficient structure is contained in the Figures, specifically Figure 2 which indicates the modules are operating on a medical information sharing server. Algorithms are disclosed on Figure 22-24.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: The Statutory Categories
Claims 1,8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a CRM, device, and method for information retrieval.
Step 2A Prong One: The Abstract Idea
The limitations of (claim 1 being representative) receiving […] a first name of a potential user, a last name of the potential user, an image of a government issued identification of the potential user, and a medical license identification for the potential user; performing a government issued identification confirmation, by determining that the first name and the last name match a first name and a last name on the government issued identification; performing a medical license identification confirmation, by determining that the first name and the last name match a first name and a last name associated with the medical license identification; receiving a live image of a potential user […] associated with the user [...]; performing a facial confirmation, by: identifying a face in the live image, extracting first facial features from the live image, encoding the first facial features into a first vector of numerical values representing unique characteristics of the face, identifying the face from a facial image on the government issued identification, extracting second facial features from the facial image, encoding the second facial features into a second vector of numerical values representing unique characteristics of the face, and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, a face depicted in the government issued identification; performing a medical license confirmation, by determining that the medical license identification is valued based on a response from a medical license database; in response to the government issued identification confirmation, the medical license identification confirmation, the facial confirmation, and the medical license confirmation, granting the potential user an account with a medical information sharing service comprising only authorized medical professionals; generating a facial template from the facial features from the live image, and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service; and allowing the potential user access to medical information posted within the medical information sharing service, as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting one or more computer storage media and a device nothing in the claim precludes the step from practically being performed in the mind.
For example, but for the one or more non-transitory computer storage media and a device implemented by a computer or the processor, this claim encompasses receiving a first name of a potential user, a last name of the potential user and an image of a government issued identification of the potential user, and a medical license identification for the potential user; performing a government issued identification confirmation; receiving a live image of the potential user associated with the user; performing a facial confirmation, by: identifying a face in the live image, extracting first facial features from the live image, encoding the first facial features into a first vector of numerical values representing unique characteristics of the face, identifying the face from a facial image on the government issued identification, extracting second facial features from the facial image, encoding the second facial features into a second vector of numerical values representing unique characteristics of the face, and determining, using a biometric facial matching algorithm that compares the first vector with the second vector image analysis, that [[a]] the face depicted in the live image matches, within a threshold, [[a]] the face depicted in the government issued identification; performing a medical license confirmation; in response to the government issued identification confirmation, the medical license identification confirmation, the facial confirmation, and the medical license confirmation, granting the potential user an account with a medical information sharing service; generating a facial template from the facial features from the live image, and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service; and allowing the potential user access to medical information posted within the medical information sharing service in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Practical Application
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of one or more non-transitory computer storage media and a device that implements the identified abstract idea. The one or more computer storage media and a device is not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing a generic computer functions of computing, determining, and selecting) such that it amounts 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.
Step 2B: Significantly More
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using one or more computer storage media and a device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Dependent Claims and Dependent Additional Elements
Claims 2-7,9-14,16-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 merely describe(s) wherein the medical license database is a specific type of database. Claim 3 merely describes using facial recognition technology to authenticate the potential user by comparing a live image received from the user with an image associated with the medical license. Claim 4 merely describes wherein the medical information is selected from the group consisting of videos, images, textual descriptions, and analytical data. Claim 5 merely describes obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient. Claim 6 merely describes copying videos from the medical information sharing service is prevented using certain rights management. Claim 7 merely describes using native voice triggers to initiate specific events during a surgical procedure to generate the medical information. Claim 9 merely describes wherein the facial recognition uses a live image of the user. Claim 10 merely describes the medical information sharing service comprises a messaging system that allows secure communication between authorized medical professionals. Claim 11 merely describes wherein the medical information sharing service involves video capture. Claim 12 merely describes a knowledge based verification process to confirm an identity of a medical professional and a patient before allowing video capture of a medical procedure. Claim 13 merely describes the video capture system. Claim 14 merely describes wherein the video capture system comprises a movie script integration module that synchronizes camera actions with a doctor’s narration during a medical procedure. Claim 16 merely describes the video is a live medical procedure being broadcast in substantially real-time. Claim 17 merely describes obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient. Claim 18 merely describes comprising associating the patient consent with the medical video. Claim 19 merely describes using native voice triggers to initiate specific events during a surgical procedure depicted in the video. Claim 20 merely describes outputting messaging content that allows secure communication between authorized medical professional viewing a video.
The dependent claims contain a variety of additional elements, including a messaging system, a video storage system, a video capture system and a camera tracking system and a camera. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claims are not patent eligible.
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.
Claim(s) 1,3,4,7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 9280684 B1 (hereafter Kragh) in view of US 2023/0196830 A1 (hereafter Weksler) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view of WO 2019/236284 A1 (hereafter Mostafa) in view of US 2018/0373859 A1 (hereafter Ganong).
Regarding Claim 1
Kragh teaches:
One or more non-transitory computer storage media comprising computer-executable instructions that when executed by a computing device performs a method of sharing medical information, the method comprising: receiving, from a user device, a first name of a potential user, a last name of the potential user, and a medical license identification for the potential user; [Kragh teaches at Figure 2B Item 125 verifying license information. Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification. Kragh teaches at col. 6 line 53-57 the legal name of the user and his/her standing is also verified with a valid debit or credit card via a third party, such as a national financial institution (block 112). Kragh teaches at col. 7 line 31-35 teaches another aspect of the process will include the verification of licensing information (block 125), such as those that the user will have acquired through training, schooling, and/or certification credits that will be issued nationally or in some other verifiable manner.]
performing a government issued identification confirmation, by determining that the first name and the last name match a first name and a last name on the government issued identification; [Kragh teaches at Figure 2B Item 125 verifying license information. Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification.]
performing a medical license identification confirmation, by determining that the first name and the last name match a first name and a last name associated with the medical license identification; [Kragh teaches at Figure 2B Item 125 verifying license information. Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification. Kragh teaches at col. 6 line 53-57 the legal name of the user and his/her standing is also verified with a valid debit or credit card via a third party, such as a national financial institution (block 112). Kragh teaches at col. 7 line 31-35 teaches another aspect of the process will include the verification of licensing information (block 125), such as those that the user will have acquired through training, schooling, and/or certification credits that will be issued nationally or in some other verifiable manner. Kragh teaches at col. 7 line 35-38 teaches such licenses will include, but are not intended to be limited to, licenses to practice medicine, carry a fire arm, perform law enforcement duties, perform financial auditing services, carry out a professional trade, etc. Kragh teaches at col. 4 line 5-11 another registry is established for license healthcare providers and institutions, so that qualifications, and access privileges can be verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events.]
[…]
performing a medical license confirmation, by determining that the medical license identification is valid based on a response from a medical license database; [Kragh teaches at Figure 2B Item 125 verifying license information. Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification. Kragh teaches at col. 6 line 53-57 the legal name of the user and his/her standing is also verified with a valid debit or credit card via a third party, such as a national financial institution (block 112). Kragh teaches at col. 7 line 31-35 teaches another aspect of the process will include the verification of licensing information (block 125), such as those that the user will have acquired through training, schooling, and/or certification credits that will be issued nationally or in some other verifiable manner. Kragh teaches at para. [0067] the logic will then move to block 904 were the device will use at least one of the one or more first images to perform facial recognition to compare facial features from the one or more first images to a certified or other reference image as may be accessed from a third party remote storage location or provided by the user themselves (e.g., via an image of the user’s current physical/tangible photo ID itself). Collectively, this teaches performing a medical license confirmation, by determining that the medical license identification is valid based on a response from a medical license database;…]
in response to the government issued identification confirmation, [Taught above by Kragh.]
the medical license identification confirmation, [Taught above by Kragh.]
granting the potential user an account with a medical information sharing service comprising only authorized medical professionals; [Kragh teaches at the Abstract a system and method verify and validate a user identity for enrollment in a secure personal dataset accessing system, wherein a personal dataset includes identifiable attributes of the user. Kragh teaches at the Abstract a generated digital security element results in the user electronically receiving a password and unique electronic address assigned to the user. Kragh teaches at the Abstract the digital security element is then transmitted to the user and enables electronic access to the personal dataset, the personal dataset having been authenticated through the verification and validation. Kragh teaches at col. 4 line 5-11 another registry is established for licensed emergency healthcare providers and institutions, so that their credentials, qualifications, and access privileges can be independently verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events. Collectively, Kragh teaches granting the potential user an account with a medical information sharing service comprising only authorized medical professionals.]
[…]
and allowing the potential user access to medical information posted within the medical information sharing service. [Kragh teaches at the Abstract a system and method verify and validate a user identity for enrollment in a secure personal dataset accessing system, wherein a personal dataset includes identifiable attributes of the user. Kragh teaches at the Abstract a generated digital security element results in the user electronically receiving a password and unique electronic address assigned to the user. Kragh teaches at the Abstract the digital security element is then transmitted to the user and enables electronic access to the personal dataset, the personal dataset having been authenticated through the verification and validation. Kragh teaches at col. 4 line 5-11 another registry is established for licensed emergency healthcare providers and institutions, so that their credentials, qualifications, and access privileges can be independently verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events. This is allowing the potential user access to medical information posted within the medical information sharing service.]
Kragh may not explicitly teach:
[…]
receiving a live image of the potential user captured by a camera associated with the user device;
performing a facial confirmation, by; identifying a face in the live image,
extracting first facial features from the live image,
encoding the first facial features into a first vector of numerical values representing unique characteristics of the face,
identifying the face from a facial image on the government issued identification,
extracting second facial features from the facial image,
encoding the second facial features into a second vector of numerical values representing unique characteristics of the face,
and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, the face depicted in the government issued identification;
generating a facial template from the facial features from the live image,
and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service;
[…].
Weksler teaches:
[…]
receiving a live image of the potential user captured by a camera associated with the user device; [Weksler teaches at para. [0067] beginning at block 900, the device will receive one or more first images from a camera to, at block 902, perform liveness detection as described above to verify that a person is presenting themselves live in front of the camera (e.g. rather than fraudulently attempting to register a person shown in a still picture/photograph).]
[…]
extracting first facial features from the live image, [Weksler teaches at para. [0057] the overall facial recognition match itself will be required to be within a threshold level of confidence such as ninety percent, for example or may be more particularlized in that the app may be configured to compare specific facial features between the live image(s) and the reference image that are less likely to change. Weksler teaches at para. [0057] this will include, for example, matching at least ten feature points of the user’s eyes/iris/eye area between the live and reference images, where fifteen feature points are available. The matching at least ten feature points of the user’s eyes/iris/eye area between the live and reference images, where fifteen feature points are available is the extracting the first facial feature from the live image.]
[…]
identifying the face from a facial image on the government issued identification, [Weksler teaches at para. [0056] this GUI shows that the next step in the example process is to perform facial recognition using live images from the camera to verify that the person shown in the live images is the same person shown in an older certified image and/or for which a biometric face ID has already been registered. Weksler teaches at para. [0068] for example, at block 906 the device will identify the real name or username indicated on the digital certificate as matching the real name or username of the user as identifier through facial recognition. Collectively, Weksler teaches identifying the face from a facial image on the government issue identification.]
[…]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the device and method for populating a legal filing with an individual’s name from a government issued identification document of Vanko to the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler with the motivation of addressing when this in turn can lead to other who are looking at the photograph being unable to accurately verify that the person shown in the photograph is the same person presenting themselves in person (Weksler at para. [0002]).
Kragh/Weksler may not explicitly teach:
[…]
performing a facial confirmation, by; identifying a face in the live image,
[…]
encoding the first facial features into a first vector of numerical values representing unique characteristics of the face,
[…]
extracting second facial features from the facial image,
encoding the second facial features into a second vector of numerical values representing unique characteristics of the face,
and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, the face depicted in the government issued identification;
[…]
generating a facial template from the facial features from the live image,
and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service;
[…].
Xi teaches:
[…]
encoding the first facial features into a first vector of numerical values representing unique characteristics of the face, [Xi teaches at pg. 1 basically, a set of selected features (like eyes, mouth, nose, etc.) is extracted first, then by concatenating features a face-vector is built which is eventually recognized. Xi teaches at pg. 1 in our system all of the features are extracted using SVMs specially trained for each task.]
[…]
extracting second facial features from the facial image, [Xi teaches at pg. 1 basically, a set of selected features (like eyes, mouth, nose, etc.) is extracted first, then by concatenating features a face-vector is built which is eventually recognized. Xi teaches at Fig. 1 main and componential coordinates used to define facial geometry shape with Xm and Ym dimensions defining different facial features.]
encoding the second facial features into a second vector of numerical values representing unique characteristics of the face, [Xi teaches at pg. 1 basically, a set of selected features (like eyes, mouth, nose, etc.) is extracted first, then by concatenating features a face-vector is built which is eventually recognized. Xi teaches at pg. 5 the features are extracted one by one, first both of the eyes and the mouth, starting at locations predicted by the wavelet statistics system. The mouth features extracted into the face vector that is built is encoding the second facial features into a second vector of numerical values representing unique characteristics of the face.]
[…].
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi with the motivation of performing more accurate feature extraction from images.
Kragh/Weksler/Xi may not explicitly teach:
[…]
generating a facial template from the facial features from the live image,
performing a facial confirmation, by; identifying a face in the live image,
[…]
and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, the face depicted in the government issued identification;
[…]
and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service;
[…].
Mostafa teaches:
[…]
generating a facial template from the facial features from the live image, [Mostafa teaches at para. [0061] encoding of the selected images will include encoding features (e.g. facial features) of the user to define the features in the images as one or more feature vectors in a feature space. Mostafa teaches at para. [0123] in 258A, unlock feature vector(s) 256 are compared to feature vectors in the templates of the first template space (the template space for the first enrollment profile) to get first matching score 260A for the unlock attempt image. Mostafa teaches at para. [0041] digital images captured by camera will include, for example, still images, video images, and/or frame-by-frame images. The video images that are frame-by-frame images are interpreted to be live images.]
performing a facial confirmation, by; identifying a face in the live image, [Mostafa teaches at the Abstract a facial recognition authentication on a device having a camera may operate with multiple enrollment profiles on the device. Mostafa teaches at para. [0041] digital images captured by camera will include, for example, still images, video images, and/or frame-by-frame images. The video images that are frame-by-frame images are interpreted to be live images. Mostafa teaches at the Abstract multiple enrollment profiles may include separate profiles for different appearances of a user and/or separate profiles for different users authorized to use the device.]
[…]
and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service; [Mostafa teaches at para. [0061] after images are selected in 206, features of the user in the selected (template) images will be encoded in 208. Mostafa teaches at the Abstract a facial recognition authentication on a device having a camera may operate with multiple enrollment profiles on the device. Mostafa teaches at the Abstract multiple enrollment profiles may include separate profiles for different appearances of a user and/or separate profiles for different users authorized to use the device. Mostafa teaches at the Abstract the enrollment profiles will be generated using an enrollment process where the enrollment process is operated separately to generate each of the different enrollment profiles.]
[…].
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi to the multiple enrollments in facial recognition of Mostafa with the motivation of performing more secure facial recognition during profile enrollment on a smart device.
Kragh/Weksler/Xi/Mostafa may not explicitly teach:
and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, the face depicted in the government issued identification;
Ganong teaches:
and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, the face depicted in the government issued identification; [Ganong teaches at para. [0017] there is provided an encryption/decryption system for user authentication and document authentication using face biometrics. Ganong teaches at para. [0072] in some embodiment, external systems will provide connection to external data storages of images and information associated with one or more persons (e.g., external data storage will be a Department of Motor Vehicles database containing driver’s license images and identity information). Ganong teaches at para. [0072] external data storage will allow the system 110 to better authenticate the identity of user(s) by performing the systems and methods described herein upon images in the external data storage as well as captured or received images of the user to be authenticated. Ganong teaches at para. [0085] in some embodiments, authentication system will use face signatures as feature for comparing one digital image of a face to another digital image of a face. Ganong teaches at para. [0085] face signatures will also be used when attempting to compare a digital image of a face to a set of digital images of known faces. Ganong teaches at para. [0089] a similarity threshold is linked directly to the method for determining the likeness between two face signatures. Ganong teaches at para. [0089] in mathematical terms, the two faces being compared are represented by a vector of real numbers (e.g. face signatures) and a proxy for likeness is the distance between those two vector (e.g. similarity metric). Collectively, Ganong teaches and determining, using a biometric facial matching algorithm that compares the first vector with the second vector, that the face depicted in the live image matches, within a threshold, the face depicted in the government issued identification.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi to the multiple enrollments in facial recognition of Mostafa to the systems and methods for authentication using digital signature with biometrics of Ganong with the motivation of improving encryption, which is only as good as the password that protects access to a user’ private key (Ganong at para. [0004]).
Regarding Claim 3
Kragh/Weksler/Xi/Mostafa/Ganong teach the media of claim 1. Kragh/Weksler/Xi/Mostafa/Ganong further teach:
further comprising using facial recognition technology to authenticate the potential user by comparing a live image received from the user device with an image associated with the medical license. [Weksler teaches at para. [0056] this GUI shows that the next step in the example process is to perform facial recognition using live images from the camera to verify that the person shown in the live images is the same person shown in an older certified image and/or for which a biometric face ID has already been registered. Weksler teaches at para. [0056] the reference image will be accessed from a remote storage location, such as one hosted by a third part like a DMV or other government agency or whatever third party initially issued the photo ID that is being updated. Weksler teaches at para. [0056] the reference image will also be accessed based on receipt of a digital photograph provided by the person that shows the person’s existing photo ID, which itself would show the reference image (E.g. the user uses the same camera to take a picture of their photo ID while participating in the current process. Weksler teaches at para. [0056] or the user will hold up their existing photo ID to the camera for the camera to extract the reference image therefrom in real time. Weksler teaches at para. [0057] the overall facial recognition match itself will be required to be within a threshold level of confidence such as ninety percent, for example, or will be more particularized in that the app will be configured to compare specific facial features between the live image(s) and the reference image that are less likely to change. The refence photo ID is interpreted as the image associated with the medical license taught by Kragh.]
Regarding Claim 4
Kragh/Weksler/Xi/Mostafa/Ganong teach the media of claim 1. Kragh/Weksler/Xi/Mostafa/Ganong further teach:
wherein the medical information is selected from a group consisting of videos, images, textual descriptions, and analytical data. [Weksler teaches at para. [0047] the camera will be a thermal imaging camera, an infrared (IR) camera, a digital camera such as a webcam, a three-dimensional (3D) camera, and/or a camera otherwise integrated into the system and controllable by the processor to gather still images and/or vide consistent with present principles (e.g. to update a photo ID). This teaches wherein the medical information is selected from a group consisting of videos.]
Regarding Claim 7
Kragh/Weksler/Xi/Mostafa/Ganong teach the media of claim 1. Kragh/Weksler/Xi/Mostafa/Ganong may not explicitly teach:
further comprising using native voice triggers to initiate specific camera movements during a surgical procedure to generate the medical information.
Paul teaches:
further comprising using native voice triggers to initiate specific camera movements during a surgical procedure to generate the medical information. [The specification, which aligns with broadest reasonable interpretation of the claim limitation, indicates at para. [0081] native voice triggers enable the doctor to user pre-defined voice commands to initiate specific camera movements, such as panning, tilting, or zooming. Paul teaches at pg. 11 the incorporation of a keyword-triggered language interface into the da Vinci surgical robot holds potential in enhancing surgical outcomes and expanding the accessibility of this technology. Paul teaches at Table 1 the list of commands for voice recognition includes start/stop and tracking commands to track midpoint of the tools and track the left/right of the tool. Paul teaches at pg. 4 based on feedback from a robotic surgeon, two additions were made to the existing list of functions –“record video” and “take picture.” The generated medical information is the recorded video and/or picture. Paul teaches at pg. 4 this allows the user to record a video or take a picture of a procedure (or sub procedure) from the endoscopic camera, which can be used for documenting or teaching purposes. This is using native voice triggers to initiate specific camera movements during a surgical procedure to generate the medical information. The spoken words are the native voice triggers.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi to the multiple enrollments in facial recognition of Mostafa to the systems and methods for authentication using digital signature with biometrics of Ganong to the evaluation of a voice-enabled autonomous camera control system for the da Vinci surgical robot of Paul with the motivation of addressing that robotic surgery involves significant task switching between tool control and camera control, which can be a source of distraction and error (Paul at the Abstract).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0025790 A1 (hereafter Shelton) in view of US 2023/0196830 A1 (hereafter Weksler) in view US 2020/0285836 A1 (hereafter Davis) in view of US 9280684 B1 (hereafter Kragh).
Regarding Claim 15
Shelton teaches:
A method for sharing medical information, comprising: receiving, at a medical information sharing service, a request from an authorized user to view a video depicting a medical operation; [Shelton teaches at para. [0148] the location and surgical procedure specific data will include the surgeon’s preferences, surgical instrument settings used for a surgical procedure, procedure simulations and plans viewed by the surgeon, videos, annotations, and/or transcriptions associated with the surgeon, and/or the like. Shelton teaches at para. [0149] the surgical hub will request the location and surgical procedure specific data from one or more data storages, such as a local storage, an edge network storage, a cloud storage unit, and/or the like, for example. Shelton teaches at para. [0197] for example, or video surveillance will be used as a verification, for example, based on facial recognition. Shelton teaches at para. [0197] the facial recognition will confirm and/or allow access and/or the exchange of data between the surgical hub and other systems and/or visibility and use of the data. This is receiving, at a medical information sharing service, a request from an authorized user to view a video depicting a medical operation.]
determining that a permission setting associated with the video requires facial recognition validation during viewing of the video; [Shelton teaches at para. [0168] the surgical hub will generate surgical procedure information and/or control programs based on the location and surgical procedure specific data, for example, for use in a surgical procedure. Shelton teaches at para. [0168] the surgical hub will send the generated surgical procedure information and/or control programs and one or more modules and/or systems associated with a surgical procedure. Shelton teaches at para. [0197] for example, OR video surveillance will be used as a verification, for example, based on facial recognition. Shelton teaches at para. [0197] the facial recognition will confirm and/or allow access and/or the exchange of data between the surgical hub and other systems and/or visibility and use of the data.]
[…]
and outputting the video to the device. [Shelton teaches at para. [0194] in an example, a video will be recorded during a surgical procedure and stored in the data storage. Shelton teaches at para. [0194] certain credentials and/or access permissions will allow access to the recorded video. Shelton teaches at para. [0194] for example, the surgeon will have access permissions to review the video and/or edit the video prior or after the video is archived. Shelton teaches at para. [0194] other entities will have access permission to review the video but not edit the video. Shelton teaches at para. [0069] thus, for example, a USB port will be used to provide input to the computer system and to output information from the computer system to an output device. Collectively, this teaches and outputting the video to the device.]
Shelton may not explicitly teach:
receiving a live image of the authorized user captured from a camera on a user device from which the authorized user is accessing the medical information sharing service;
performing, at the medical information sharing service, facial recognition using the live image to authenticate the authorized user by extracting facial features from the live image
and generating a first vector of numerical values representing unique characteristics of the face in the live image,
comparing the first vector with the second vector representing unique characteristics of the facial feature generated from an image of the authorized user capture during account set up of a user account associated with the authorized user and determining that the first vector matches the second vector the within a threshold,
wherein account set up includes confirming a medical license identification associated with the user is valid based on a response from a medical license database;
Weksler teaches:
receiving a live image of the authorized user captured from a camera on a user device from which the authorized user is accessing the medical information sharing service; [Weksler teaches at para. [0056] this GUI shows that the next step in the example process is to perform facial recognition using live images from the camera to verify that the person shown in the live images is the same person shown in an older certified image and/or for which a biometric face ID has already been registered. Weksler teaches at para. [0056] the reference image will be accessed from a remote storage location, such as one hosted by a third part like a DMV or other government agency or whatever third party initially issued the photo ID that is being updated. Weksler teaches at para. [0056] the reference image will also be accessed based on receipt of a digital photograph provided by the person that shows the person’s existing photo ID, which itself would show the reference image (E.g. the user uses the same camera to take a picture of their photo ID while participating in the current process). Weksler teaches at para. [0056] or the user will hold up their existing photo ID to the camera for the camera to extract the reference image therefrom in real time. Weksler teaches at para. [0057] the overall facial recognition match itself will be required to be within a threshold level of confidence such as ninety percent, for example, or will be more particularized in that the app will be configured to compare specific facial features between the live image(s) and the reference image that are less likely to change.]
performing, at the medical information sharing service, facial recognition using the live image to authenticate the authorized user by extracting facial features from the live image [Weksler teaches at para. [0067] the logic will then move to block 904 where the device will use at least one of the one or more first images to perform facial recognition to compare facial features from the one or more first images to a certified or other reference image as may be accessed from a third party remote storage location or provided by the user themselves (e.g., via an image of the user’s current physical/tangible photo ID itself). Weksler teaches at Claim 1 based on the verifications and validation, store a second image from the camera showing the person, the second image stored at a storage location accessible to other devices besides the first device. This teaches the medical information sharing service.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler with the motivation of addressing when this in turn can lead to other who are looking at the photograph being unable to accurately verify that the person shown in the photograph is the same person presenting themselves in person (Weksler at para. [0002]).
Shelton/Weksler may not explicitly teach:
and generating a first vector of numerical values representing unique characteristics of the face in the live image,
comparing the first vector with the second vector representing unique characteristics of the facial feature generated from an image of the authorized user capture during account set up of a user account associated with the authorized user and determining that the first vector matches the second vector the within a threshold,
wherein account set up includes confirming a medical license identification associated with the user is valid based on a response from a medical license database;
Davis teaches:
and generating a first vector of numerical values representing unique characteristics of the face in the live image, [Davis teaches at para. [0023] shortly after a connected camera 10 (as well as corresponding image processing software) will be activated by the computing device in order to capture a live image of the user as depicted in phase 1. Davis teaches at para. 21 the pre-processing will include (i) pose estimation of the facial images, (ii) affine transformation of the facial images, (iii) the extraction of 128-dimensional embedding vectors of the transformed facial images using a deep neural network, e.g., the feature set, (iv) training the classifier on the feature set, and (v) inferring on new data using the trained model. Davis teaches at para. [0021] as such, the deep neural network will represent (or embed the face on the 128-dimensional unit hyper-sphere). Davis teaches at para. [0021] according to an embodiment, a large distance between two face embedding vectors indicates that the faces are likely not the same person.]
comparing the first vector with the second vector representing unique characteristics of the facial feature generated from an image of the authorized user capture during account set up of a user account associated with the authorized user and determining that the first vector matches the second vector the within a threshold, [Davis teaches at para. 21 the pre-processing will include (i) pose estimation of the facial images, (ii) affine transformation of the facial images, (iii) the extraction of 128-dimensionsal embedding vectors of the transformed facial images using a deep neural network, e.g., the feature set, (iv) training the classifier on the feature set, and (v) inferring on new data using the trained model. Davis teaches at para. [0023] according to an embodiment, after the plurality of images and corresponding machine learning classifier model are successfully registered with a username in a blockchain, the user will be authenticated and authorized to access a proprietary company system based on facial recognition. Davis teaches at para. [0021] according to an embodiment, a large distance between two face embedding vectors indicates that the faces are likely not the same person. Davis teaches at para. [0021] in other words, with the embedding vectors, techniques such as clustering, similarity decisions, and classification will be made easier as compared to other facial recognition techniques, in which the Euclidean distance between features is not meaningful. Davis teaches at para. [0026] according to an embodiment, as depicted in Fig. 2F, the classifier will be a distance metric, which will determine the Euclidian distances between corresponding embedding vectors. Davis teaches at para. [0026] in this regard, a small distance between two face embedding vectors indicates that the corresponding faces are likely the same person, while a large distance indicates that the faces are likely not of the same person. The difference between “small” distance and “large” distance is describing the threshold. Collectively, this teaches comparing the first vector with the second vector representing unique characteristics of the facial feature generated from an image of the authorized user capture during account set up of a user account associated with the authorized user and determining that the first vector matches the second vector the within a threshold.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler to the systems and methods for secure user logins with facial recognition and blockchain of Davis with the motivation of reducing the occurrence of, in many cases, information technology (IT) support staff, who are required to assist those who have forgotten or have simply run into problems with their passwords, resulting in additional lost time and decreased efficiency (Davis at para. [0003]).
Shelton/Weksler/Davis may not explicitly teach:
wherein account set up includes confirming a medical license identification associated with the user is valid based on a response from a medical license database;
Kragh teaches:
wherein account set up includes confirming a medical license identification associated with the user is valid based on a response from a medical license database; [Kragh teaches at the abstract a system and method verify and validate a user identity for enrollment in a secure personal dataset accessing system, wherein a personal dataset includes identifiable attributes of the user. This teaches the account set up context. Kragh teaches at Figure 2B Item 125 verifying license information. Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification. Kragh teaches at col. 6 line 53-57 the legal name of the user and his/her standing is also verified with a valid debit or credit card via a third party, such as a national financial institution (block 112). Kragh teaches at col. 7 line 31-35 teaches another aspect of the process will include the verification of licensing information (block 125), such as those that the user will have acquired through training, schooling, and/or certification credits that will be issued nationally or in some other verifiable manner. Kragh teaches at col. 7 line 35-38 teaches such licenses will include, but are not intended to be limited to, licenses to practice medicine, carry a fire arm, perform law enforcement duties, perform financial auditing services, carry out a professional trade, etc. Kragh teaches at col. 4 line 5-11 another registry is established for license healthcare providers and institutions, so that qualifications, and access privileges can be verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the method for providing an authenticated digital identity of Kragh with the motivation of providing a way to provide consent for an unconscious patient.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 9280684 B1 (hereafter Kragh) in view of US 2023/0196830 A1 (hereafter Weksler) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view of WO 2019/236284 A1 (hereafter Mostafa) in view of US 2018/0373859 A1 (hereafter Ganong) in view of US 20150073951 A1 (Ladd).
Regarding Claim 2
Kragh/Weksler/Xi/Mostafa/Ganong teach the media of claim 1. Kragh/Weksler/Xi/Mostafa/Ganong may not explicitly teach:
wherein the medical license database is a National Provider Identifier (NPI) database.
Ladd teaches:
wherein the medical license database is a National Provider Identifier (NPI) database. [Ladd teaches at para. [0059] during operation, the HCP repository store data about physicians and other healthcare professional. Ladd teaches at para. [0059] for each healthcare professional, the HCP repository includes the name, previous name (if any), nickname (if any), salutation, gender, birth date, social security number (SSN), driver’s license number, driver’s license state, type of healthcare professional, National Provider Identifier (NPI), home address, home phone number, work address, work phone number, cell phone number, email address, hospital position, hospital department, hospital supervisor, hours worked per week, board specialty, board organization, medical license number, medical license state, fellowship, internship, medical school, and medical degree.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi to the multiple enrollments in facial recognition of Mostafa to the systems and methods for authentication using digital signature with biometrics of Ganong to professionals of Ladd with the motivation of addressing that many professionals, including those in medicine, law, accounting, and securities, are subject to extensive statutory, regulatory, and ethical rules that proscribe their behavior in order to protect best interests of society and their clients (Ladd at para. [0003]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 9280684 B1 (hereafter Kragh) in view of US 2023/0196830 A1 (hereafter Weksler) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view of WO 2019/236284 A1 (hereafter Mostafa) in view of US 2018/0373859 A1 (hereafter Ganong) in view of US 2004/0193445 A1 (hereafter Lauryssen).
Regarding Claim 5
Kragh/Weksler/Xi/Mostafa/Ganong teach the media of claim 1. Kragh/Weksler/Xi/Mostafa/Ganong may not explicitly teach:
further comprising obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient.
Lauryssen teaches:
further comprising obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient. [Lauryssen teaches at para [0032] the informed consent system includes a visual or graphic component, an auditory component, and a textual component integral with the graphic component and the auditory component. Lauryssen teaches at para. [0032] the visual component consists of a detailed 3-dimensinal (3d) animation of the surgery. Lauryssen teaches at para. [0032] such animation permits all angles of the surgical anatomy and nearby important structures that could be damaged during surgery to be easily conveyed to the patient. Lauryssen teaches at para. [0032] the patient initiates the appropriate informed consent program for his or her surgery. Lauryssen teaches at para. [0073] a copy of the entire informed consent process is stored by the informed consent system, preferably in a centralized and secure database such as a database of Fig. 4 Lauryssen teaches at para. [003] the informed consent system allows the patient to replay the entire procedure or segments thereof at any time. Collectively, this teaches further comprising obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi to the multiple enrollments in facial recognition of Mostafa to the systems and methods for authentication using digital signature with biometrics of Ganong to the automated informed consent and surgical outcome tracking system and method therefor of Lauryssen with the motivation of addressing that the quality and consistency of verbal informed consents vary significantly between surgeons becomes of variations in communication skills, time available, and a surgeon’s ability and willingness to outline all of the possible complications (Lauryssen at para. [0004]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 9280684 B1 (hereafter Kragh) in view of US 2023/0196830 A1 (hereafter Weksler) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view of WO 2019/236284 A1 (hereafter Mostafa) in view of US 2018/0373859 A1 (hereafter Ganong) in view Lin (Advances in Digital Video Content Protection).
Regarding Claim 6
Kragh/Weksler/Xi/Mostafa/Ganong teach the media of claim 1. Kragh/Weksler/Xi/Mostafa/Ganong may not explicitly teach:
wherein copying of videos from the medical information sharing service is prevented using digital rights management technology.
Lin teaches:
wherein copying of videos from the medical information sharing service is prevented using digital rights management technology. [Lin teaches at the Abstract that popular internet software based on a peer-to-peer architecture has been used to share copyrighted movies, music, software, and other materials. Lin teaches at Table 2 pg. 175 a summary of DRM systems for video presents multiple digital rights management technology to protect video. The peer to peer architecture is interpreted as the medical information sharing service taught by Smith.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the identity validation and verification system and associated methods of Kragh to the verification of liveness and person ID to certify digital image of Weksler to the facial component extraction and facial recognition with support vector machines of Xi to the multiple enrollments in facial recognition of Mostafa to the systems and methods for authentication using digital signature with biometrics of Ganong to the advances in digital video content protection of Lin with the motivation of addressing the technical challenging for securing digital content, which are formidable, and which previously have not succeeded (Lin at the Abstract).
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0196830 A1 (hereafter Weksler) in view of US 9280684 B1 (hereafter Kragh) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view US 2020/0285836 A1 (hereafter Davis).
Regarding Claim 8
Weksler teaches:
[…]
receiving live image of the user captured from a camera on a user device from which the user is attempting to access the medical information sharing service; [Weksler teaches at para. [0056] this GUI shows that the next step in the example process is to perform facial recognition using live images from the camera to verify that the person shown in the live images is the same person shown in an older certified image and/or for which a biometric face ID has already been registered. Weksler teaches at para. [0068] for example, at block 906 the device will identify the real name or username indicated on the digital certificate as matching the real name or username of the user as identifier through facial recognition.]
performing, at the medical information sharing service, facial recognition using the live image to authenticate the user by extracting facial features from the live image [Weksler teaches at para. [0056] this GUI shows that the next step in the example process is to perform facial recognition using live images from the camera to verify that the person shown in the live images is the same person shown in an older certified image and/or for which a biometric face ID has already been registered. Weksler teaches at para. [0056] Weksler teaches at para. [0067] the logic will then move to block 904 where the device will use at least one of the one or more first images to perform facial recognition to compare facial features from the one or more first images to a certified or other reference image as may be accessed from a third party remote storage location or provided by the user themselves (e.g., via an image of the user’s current physical/tangible photo ID itself).]
Weksler may not explicitly teach:
A method for verifying an identity of medical professionals, comprising: receiving, at a medical information sharing service a username for a user;
and generating a first vector of numerical values representing unique characteristics of the face in the live image,
comparing the first vector with a second vector representing unique characteristics of facial features in an image of the user captured during account set up of a user account associated with the username
and determining that first vector matches the second vector within a threshold,
wherein account set up includes confirming a medical license identification associated with the user is valid based on a response from a medical license database;
and granting access to the medical information sharing service comprising only authorized medical professionals.
Kragh teaches:
A method for verifying an identity of medical professionals, comprising: receiving, at a medical information sharing service a username for a user; [Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification. Kragh teaches at col. 6 line 53-57 the legal name of the user and his/her standing is also verified with a valid debit or credit card via a third party, such as a national financial institution (block 112). Kragh teaches at col. 4 line 5-11 another registry is established for license healthcare providers and institutions, so that qualifications, and access privileges can be verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events. Collectively, this teaches receiving, at a medical information sharing service a username for a user.]
[…]
wherein account set up includes confirming a medical license identification associated with the user is valid based on a response from a medical license database; [Kragh teaches at Figure 2B Item 125 verifying license information. Kragh teaches at col. 6 line 49-52 teaches next the user’s name is verified against the date of birth and gender along with the last four digits of the social security number (SSN; block 111), which will typically comprise a third-party verification. Kragh teaches at col. 6 line 53-57 the legal name of the user and his/her standing is also verified with a valid debit or credit card via a third party, such as a national financial institution (block 112). Kragh teaches at col. 7 line 31-35 teaches another aspect of the process will include the verification of licensing information (block 125), such as those that the user will have acquired through training, schooling, and/or certification credits that will be issued nationally or in some other verifiable manner. Kragh teaches at col. 7 line 35-38 teaches such licenses will include, but are not intended to be limited to, licenses to practice medicine, carry a fire arm, perform law enforcement duties, perform financial auditing services, carry out a professional trade, etc. Kragh teaches at col. 4 line 5-11 another registry is established for license healthcare providers and institutions, so that qualifications, and access privileges can be verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events.]
and granting access to the medical information sharing service comprising only authorized medical professionals. [Kragh teaches at col. 4 line 5-11 another registry is established for licensed emergency healthcare providers and institutions, so that their credentials, qualifications, and access privileges can be independently verified real time via a third-party source (policy and procedures) and that such validation will enable them to access the emergency medical data registry at local, regional, or national emergency events.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh with the motivation of providing a way to provide consent for an unconscious patient.
Weksler/Kragh may not explicitly teach:
[…]
and generating a first vector of numerical values representing unique characteristics of the face in the live image,
comparing the first vector with a second vector representing unique characteristics of facial features in an image of the user captured during account set up of a user account associated with the username
and determining that first vector matches the second vector within a threshold,
[…]
Xi teaches:
[…]
and determining that first vector matches the second vector within a threshold, [Xi teaches at Figure 3 some faces and face vectors extracted. Xi teaches at Figure 3 in some, subtle differences among face vectors extracted from the same images can be seen. The subtle differences among face vectors extracted are the threshold. Xi teaches at pg. 5 other feature positions are predicted, basing of features already found, with a use of a face geometry defined in fuzzy terms: we defined fuzzy functions for the expected distance between eyes, eyes and the nose, nose and the mouth, inclusion of the nose in the triangle formed by the eyes and the mouth. The expected distance between the eyes of the subtle difference among the face vectors teaches determining that the first vector matches the second vector within a threshold.]
[…].
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh to the facial component extraction and facial recognition with support vector machines of Xi the motivation of performing more accurate feature extraction from images.
Weksler/Kragh/Xi may not explicitly teach:
and generating a first vector of numerical values representing unique characteristics of the face in the live image,
comparing the first vector with a second vector representing unique characteristics of facial features in an image of the user captured during account set up of a user account associated with the username
Davis teaches:
and generating a first vector of numerical values representing unique characteristics of the face in the live image, [Davis teaches at para. [0023] shortly after a connected camera 10 (as well as corresponding image processing software) will be activated by the computing device in order to capture a live image of the user as depicted in phase 1. Davis teaches at para. 21 the pre-processing will include (i) pose estimation of the facial images, (ii) affine transformation of the facial images, (iii) the extraction of 128-dimensional embedding vectors of the transformed facial images using a deep neural network, e.g., the feature set, (iv) training the classifier on the feature set, and (v) inferring on new data using the trained model. Davis teaches at para. [0021] as such, the deep neural network will represent (or embed the face on the 128-dimensional unit hyper-sphere). Davis teaches at para. [0021] according to an embodiment, a large distance between two face embedding vectors indicates that the faces are likely not the same person.]
comparing the first vector with a second vector representing unique characteristics of facial features in an image of the user captured during account set up of a user account associated with the username [Davis teaches at para. [0023] shortly after a connected camera 10 (as well as corresponding image processing software) will be activated by the computing device in order to capture a live image of the user as depicted in phase 1. Davis teaches at para. 21 the pre-processing will include (i) pose estimation of the facial images, (ii) affine transformation of the facial images, (iii) the extraction of 128-dimensionsal embedding vectors of the transformed facial images using a deep neural network, e.g., the feature set, (iv) training the classifier on the feature set, and (v) inferring on new data using the trained model. Davis teaches at para. [0023] according to an embodiment, after the plurality of images and corresponding machine learning classifier model are successfully registered with a username in a blockchain, the user will be authenticated and authorized to access a proprietary company system based on facial recognition. Davis teaches at para. [0021] according to an embodiment, a large distance between two face embedding vectors indicates that the faces are likely not the same person. Davis teaches at para. [0021] in other words, with the embedding vectors, techniques such as clustering, similarity decisions, and classification will be made easier as compared to other facial recognition techniques, in which the Euclidean distance between features is not meaningful. Collectively, this teaches comparing the first vector with a second vector representing unique characteristics of facial features in an image of the user captured during account set up of a user account associated with the username.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh to the facial component extraction and facial recognition with support vector machines of Xi to the systems and methods for secure user logins with facial recognition and blockchain of Davis with the motivation of reducing the occurrence of, in many cases, information technology (IT) support staff, who are required to assist those who have forgotten or have simply run into problems with their passwords, resulting in additional lost time and decreased efficiency (Davis at para. [0003]).
Regarding Claim 9
Weksler/Kragh/Xi/Davis teach the method of claim 8. Weksler/Kragh/Xi/Davis further teach:
wherein the facial recognition uses a live image of the user. [Weksler teaches at para. [0056] this GUI shows that the next step in the example process is to perform facial recognition using live images from the camera to verify that the person shown in the live images is the same person shown in an older certified image and/or for which a biometric face ID has already been registered. Weksler teaches at para. [0056] the reference image will be accessed from a remote storage location, such as one hosted by a third part like a DMV or other government agency or whatever third party initially issued the photo ID that is being updated. Weksler teaches at para. [0056] the reference image will also be accessed based on receipt of a digital photograph provided by the person that shows the person’s existing photo ID, which itself would show the reference image (E.g. the user uses the same camera to take a picture of their photo ID while participating in the current process. Weksler teaches at para. [0056] or the user will hold up their existing photo ID to the camera for the camera to extract the reference image therefrom in real time. Weksler teaches at para. [0057] the overall facial recognition match itself will be required to be within a threshold level of confidence such as ninety percent, for example, or will be more particularized in that the app will be configured to compare specific facial features between the live image(s) and the reference image that are less likely to change. The refence photo ID is interpreted as the image associated with the medical license taught by Kragh.]
Claim(s) 10,11,13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0196830 A1 (hereafter Weksler) in view of US 9280684 B1 (hereafter Kragh) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view US 2020/0285836 A1 (hereafter Davis) in view of US 2015/0261917 A1 (hereafter Smith).
Regarding Claim 10
Weksler/Kragh/Xi/Davis teach the method of claim 8. Weksler/Kragh/Xi/Davis may not explicitly teach:
wherein the medical information sharing service comprises a messaging system that allows secure communication between authorized medical professionals.
Smith teaches:
wherein the medical information sharing service comprises a messaging system that allows secure communication between authorized medical professionals. [Smith teaches at para. [0065] this medical condition page provides a treatment group for the group of medical practitioners that collaborate to treat Mary Martin’s breast cancer. Smith teaches at para. [0065] they contributed in a threaded chat where the practitioners will share pertinent information, post office notes or external medical records or call for a collaborative medical consultation session online related to the medical condition which the object of the medical condition subcategory page.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh to the facial component extraction and facial recognition with support vector machines of Xi to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the federated collaborative medical records system utilizing cloud computing network and methods of Smith with the motivation of health privacy and security experts remaining quite reluctant to allow unrestricted access or data sharing with other medical entities and third parties due to security concerns and proprietary intranet work investment interests (Smith at para. [0005]).
Regarding Claim 11
Weksler/Kragh/Xi/Davis teach the method of claim 8. Weksler/Kragh/Xi/Davis may not explicitly teach:
wherein the medical information sharing service comprises a video capture system.
Smith teaches:
wherein the medical information sharing service comprises a video capture system. [Smith teaches at claim 32 whereby the information pertinent to said user’s activities on a federated collaborative medical record system comprises audio and video information collected from device connected to user equipment. Smith teaches at claim 39 inviting a user that is logged in to collaborate via a federated collaborative medical record system.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh to the facial component extraction and facial recognition with support vector machines of Xi to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the federated collaborative medical records system utilizing cloud computing network and methods of Smith with the motivation of health privacy and security experts remaining quite reluctant to allow unrestricted access or data sharing with other medical entities and third parties due to security concerns and proprietary intranet work investment interests (Smith at para. [0005]).
Regarding Claim 13
Weksler/Kragh/Xi/Davis/Smith teach the method of claim 11. Weksler/Kragh/Xi/Davis/Smith further teach:
wherein the video capture system comprises includes an automated camera tracking system. [Smith teaches at para. [0011] the federated cloud based medical records system and associated methods disclosed herein automatically track the activity of medical providers and patients when accessing health information, thus enabling compliance with federal regulations. Smith teaches at claim 32 whereby the information pertinent to said user’s activities on a federated collaborative medical record system comprises audio and video information collected from devices connected to user equipment.]
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0196830 A1 (hereafter Weksler) in view of US 9280684 B1 (hereafter Kragh) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view US 2020/0285836 A1 (hereafter Davis) in view of US 2015/0261917 A1 (hereafter Smith) in view of US 2004/0193445 A1 (hereafter Lauryssen).
Regarding Claim 12
Weksler/Kragh/Xi/Davis/Smith teach the method of claim 11. Weksler/Kragh/Xi/Davis/Smith may not explicitly teach:
wherein the video capture system comprises a knowledge-based verification process to confirm an identity of a medical professional and a patient before allowing video capture of a medical procedure.
Lauryssen may not explicitly teach:
wherein the video capture system comprises a knowledge-based verification process to confirm an identity of a medical professional and a patient before allowing video capture of a medical procedure. [Lauryssen teaches at para [0032] the informed consent system includes a visual or graphic component, an auditory component, and a textual component integral with the graphic component and the auditory component. Lauryssen teaches at para. [0032] the visual component consists of a detailed 3-dimensinal (3d) animation of the surgery. Lauryssen teaches at para. [0032] such animation permits all angles of the surgical anatomy and nearby important structures that could be damage during surgery to be easily conveyed to the patient. Lauryssen teaches at para. [0032] the patient initiates the appropriate informed consent program for his or her surgery. Lauryssen teaches at para. [0073] a copy of the entire informed consent process is stored by the informed consent system, preferably in a centralized and secure database such as a database of Fig. 4 Lauryssen teaches at para. [003] the informed consent system allows the patient to replay the entire procedure or segments thereof at any time. Collectively, Lauryssen teaches the video capture system comprises a knowledge-based verification process to confirm an identity of a patient before allowing video capture of a medical procedure. Lauryssen teaches at para. [0072] the informed consent system will now be described in operation with the reference to Fig. 3. Lauryssen teaches at para. [0072] a medical staff person (i.e., secretary, nurse, doctor) enters a password to log on to the informed consent system. Collectively, Lauryssen teaches the video capture system comprises a knowledge-based verification process to confirm an identity of a patient and a medical professional before allowing video capture of a medical procedure.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh to the facial component extraction and facial recognition with support vector machines of Xi to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the federated collaborative medical records system utilizing cloud computing network and methods of Smith to the automated informed consent and surgical outcome tracking system and method therefor of Lauryssen with the motivation of addressing the quality and consistency of verbal informed consents, which vary significantly between surgeons because of variations in communication skills, time available, and a surgeon’s ability and willingness to outline all of the possible complications (Lauryssen at para. [0004]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0196830 A1 (hereafter Weksler) in view of US 9280684 B1 (hereafter Kragh) in view of Xi (Facial Component Extraction and Face Recognition with Support Vector Machines) in view US 2020/0285836 A1 (hereafter Davis) in view of US 2015/0261917 A1 (hereafter Smith) in view of Rafiq (Digital Video Capture and Synchronous Consultation in Open Surgery).
Regarding Claim 14
Weksler/Kragh/Xi/Davis/Smith teach the method of claim 11. Weksler/Kragh/Xi/Davis/Smith may not explicitly teach:
wherein the video capture system comprises a movie script integration module that synchronizes camera actions with a doctor’s narration during a medical procedure.
Rafiq teaches:
wherein the video capture system comprises a movie script integration module that synchronizes camera actions with a doctor’s narration during a medical procedure. [Rafiq teaches at pg. 569 the robotic arm provided a stable platform for capturing the laparoscopic optics in close proximity to the open surgical filed and was driven by the surgeon’s voice commands spoken into a microphone, which were converted by the Hermes unit into digital signals for AESOP to recognize. This teaches synchronization of camera actions with a doctors narration during a medical procedure. Rafiq teaches at the Abstract Methods that twenty-five thyroidectomy explorations in 15 patients were monitored and transmitted bidirectionally with audio and video data in real-time. Rafiq teaches at the Abstract-Methods remotely located surgical trainees (n = 4) and medical students (n = 3) confirmed 7 different anatomic landmarks during each surgical procedure. Rafiq teaches at the Abstract Methods the study used the Socrates System (Computer Motion, Inc. [CMI], Goleta, CA), an interactive telementoring system inclusive of a telestration whiteboard, in conjunction with the AESOP robotic arm and Hermes voice command system (CMI). Rafiq teaches at the Abstract-Methods a 10-mm flat laparoscopic telescope was used to capture the optical surgical field. Rafiq teaches at the Abstract-Methods voice, telestrator, or marker confirmed each anatomic landmark the image parameters of resolution, chroma (light position and intensity), and luminance were assessed with survey responses. The confirmation of each anatomic landmark by voice was also interpreted as the doctor’s narration. Collectively, Rafiq teaches wherein the video capture system comprises a movie script integration module that synchronizes camera actions with a doctor’s narration during a medical procedure.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, the verification of liveness and person ID to certify digital image of Weksler to the identity validation and verification system and associated methods of Kragh to the facial component extraction and facial recognition with support vector machines of Xi to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the federated collaborative medical records system utilizing cloud computing network and methods of Smith to the digital video capture and synchronous consultation in open surgery of Rafiq with the motivation of advancing real-time or simultaneous surgical consultation and education to students in distant locations by achieving successful integration of robotics, video-teleconferencing, and intranet transmission using currently available hardware and Internet capabilities (Rafiq at the Objective).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0025790 A1 (hereafter Shelton) in view of US 2023/0196830 A1 (hereafter Weksler) in view US 2020/0285836 A1 (hereafter Davis) in view of US 9280684 B1 (hereafter Kragh) in view of Rafiq (Digital Video Capture and Synchronous Consultation in Open Surgery).
Regarding Claim 16
Shelton/Weksler/Davis/Kragh teach the method of claim 15. Shelton/Weksler/Davis/Kragh may not explicitly teach:
wherein the video is a live medical procedure being broadcast in substantially real-time.
Rafiq teaches:
wherein the video is a live medical procedure being broadcast in substantially real-time. [Rafiq teaches twenty-five thyroidectomy explorations in 15 patients were monitored and transmitted bidirectionally with audio and video data in real time. This teaches wherein the video is a live medical procedure being broadcast in substantially real-time.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the method for providing an authenticated digital identity of Kragh with the motivation of providing a way to provide consent for an unconscious patient to the digital video capture and synchronous consultation in open surgery of Rafiq with the motivation of advancing real-time or simultaneous surgical consultation and education to students in distant locations by achieving successful integration of robotics, video-teleconferencing, and intranet transmission using currently available hardware and Internet capabilities (Rafiq at the Objective).
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0025790 A1 (hereafter Shelton) in view of US 2023/0196830 A1 (hereafter Weksler) in view US 2020/0285836 A1 (hereafter Davis) in view of US 9280684 B1 (hereafter Kragh) in view of US 2004/0193445 A1 (hereafter Lauryssen).
Regarding Claim 17
Shelton/Weksler/Davis/Kragh teach the method of claim 15. Shelton/Weksler/Davis/Kragh may not explicitly teach:
further comprising obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient.
Lauryssen teaches:
further comprising obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient. [Lauryssen teaches at para [0032] the informed consent system includes a visual or graphic component, an auditory component, and a textual component integral with the graphic component and the auditory component. Lauryssen teaches at para. [0032] the visual component consists of a detailed 3-dimensinal (3d) animation of the surgery. Lauryssen teaches at para. [0032] such animation permits all angles of the surgical anatomy and nearby important structures that could be damage during surgery to be easily conveyed to the patient. Lauryssen teaches at para. [0032] the patient initiates the appropriate informed consent program for his or her surgery. Lauryssen teaches at para. [0073] a copy of the entire informed consent process is stored by the informed consent system, preferably in a centralized and secure database such as a database of Fig. 4 Lauryssen teaches at para. [003] the informed consent system allows the patient to replay the entire procedure or segments thereof at any time. Collectively, this teaches further comprising obtaining patient consent prior to generating, broadcasting, or publishing any medical video content that depicts the patient.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the method for providing an authenticated digital identity of Kragh to the automated informed consent and surgical outcome tracking system and method therefor of Lauryssen with the motivation of addressing that the quality and consistency of verbal informed consents vary significantly between surgeons becomes of variations in communication skills, time available, and a surgeon’s ability and willingness to outline all of the possible complications (Lauryssen at para. [0004]).
Regarding Claim 18
Shelton/Weksler/Davis/Kragh/Lauryssen teach the method of claim 17. Shelton/Weksler/Davis/Kragh/Lauryssen further teach:
further comprising associated the patient consent with the medical video within a video storage system. [Lauryssen teaches at para [0032] the informed consent system includes a visual or graphic component, an auditory component, and a textual component integral with the graphic component and the auditory component. Lauryssen teaches at para. [0032] the visual component consists of a detailed 3-dimensinal (3d) animation of the surgery. This teaches comprising the patient consent with the medical video within a video storage system. Lauryssen teaches at para. [0032] the patient initiates the appropriate informed consent program for his or her surgery. Lauryssen teaches at para. [0073] a copy of the entire informed consent process is stored by the informed consent system, preferably in a centralized and secure database such as a database of Fig. 4 The informed consent system is the video storage system. Collectively, this teaches comprising associated the patient consent with the medical video within a video storage system.]
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0025790 A1 (hereafter Shelton) in view of US 2023/0196830 A1 (hereafter Weksler) in view US 2020/0285836 A1 (hereafter Davis) in view of US 9280684 B1 (hereafter Kragh) in view of Paul (Evaluation of a Voice Enabled Autonomous Camera Control System for the da Vinci Surgical Robot).
Regarding Claim 19
Shelton/Weksler/Davis/Kragh teach the method of claim 15. Shelton/Weksler/Davis/Kragh may not explicitly teach:
further comprising using native voice triggers to initiate specific camera movement during a surgical procedure depicted in the video.
Paul teaches the following noted feature:
further comprising using native voice triggers to initiate specific camera movement during a surgical procedure depicted in the video. [The specification, which aligns with broadest reasonable interpretation of the claim limitation, indicates at para. [0081] native voice triggers enable the doctor to user pre-defined voice commands to initiate specific camera movements, such as panning, tilting, or zooming. Paul teaches at pg. 11 the incorporation of a keyword-triggered language interface into the da Vinci surgical robot holds potential in enhancing surgical outcomes and expanding the accessibility of this technology. Paul teaches at Table 1 the list of commands for voice recognition includes start/stop and tracking commands to track midpoint of the tools and track the left/right of the tool. Paul teaches at pg. 4 based on feedback from a robotic surgeon, two additions were made to the existing list of functions –“record video” and “take picture.” Paul teaches at pg. 4 this allows the user to record a video or take a picture of a procedure (or sub procedure) from the endoscopic camera, which can be used for documenting or teaching purposes. This is using native voice triggers to initiate specific camera movement during a surgical procedure depicted in the video. The spoken words are the native voice triggers.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the method for providing an authenticated digital identity of Kragh to the evaluation of a voice enabled autonomous camera control system for the da Vinci Surgical Robot of Paul with the motivation of reducing a significant source of distraction and error in robotic surgery, which involve significant task switching between tool control and camera control (Paul at the Abstract).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0025790 A1 (hereafter Shelton) in view of US 2023/0196830 A1 (hereafter Weksler) in view US 2020/0285836 A1 (hereafter Davis) in view of US 9280684 B1 (hereafter Kragh) in view of US 2015/0261917 A1 (hereafter Smith).
Regarding Claim 20
Shelton/Weksler/Davis/Kragh teach the method of claim 15. Shelton/Weksler/Davis/Kragh may not explicitly teach:
further comprising a outputting messaging content from a messaging system that allows secure communication between authorized medical profession viewing the video.
Smith teaches:
further comprising a outputting messaging content from a messaging system that allows secure communication between authorized medical profession viewing the video. [Smith teaches at para. [0065] this medical condition page provides a treatment group for the group of medical practitioners that collaborate to treat Mary Martin’s breast cancer. Smith teaches at para. [0065] they contributed in a threaded chat where the practitioners will share pertinent information, post office notes or external medical records or call for a collaborative medical consultation session online related to the medical condition which the object of the medical condition subcategory page. Smith teaches at the Communications section online collaboration participants David Jones (radiologist), Dr. Mary Maguire (Pulomonologist), Dr James Thompson (Thoracic surgeon) and an imaging study reviewed: chest CT dated 12/31/12 at 11125pm. Smith teaches at para. [0067] there is a posting by Dr. Badbone including a link to his imported office notes and a video if the patient’s foot at the time of the initial visit and a single frame capture still photo showing a swollen, red toe. Collectively, Smith teaches outputting messaging content from a messaging system that allows secure communication between authorized medical profession viewing the video.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the location and surgical procedure specific data storage and retrieval of Shelton to the verification of liveness and person ID to certify digital image of Weksler to the systems and methods for secure user logins with facial recognition and blockchain of Davis to the method for providing an authenticated digital identity of Kragh to the federated collaborative medical records system utilizing cloud computing network and methods of Smith with the motivation of health privacy and security experts remaining quite reluctant to allow unrestricted access or data sharing with other medical entities and third parties due to security concerns and proprietary intranet work investment interests (Smith at para. [0005]).
Response to Arguments
Applicant argues at step 2A prong one that the claims do not recite a mental process. Applicant argues that the following steps cannot be practically performed in the human mind (note that steps are coming directly from the claims, not the paraphrased steps from Applicant arguments). Next to each limitation is a breakdown of how the limitation qualifies as a mental process and how the additional elements (if any) are analyzed.
receiving, from a user device, a first name of a potential user, a last name of the potential user, an image of a government issued identification of the potential user, and a medical license identification for the potential user;
The user device is an additional element that implements the abstract idea one a generic device. The abstract idea: receiving […] a first name of a potential user, a last name of the potential user, an image of a government issued identification of the potential user, and a medical license identification for the potential user… Given broadest reasonable interpretation, this could be practically executed in the human mind by merely looking at a picture of an individual and their government issued identification and reading off the image.
performing a government issued identification confirmation, by determining that the first name and the last name match a first name and a last name on the government issued identification;
Given broadest reasonable interpretation, this could be practically executed in the human mind by merely looking at a picture of an individual and their government issued identification and reading off the image and comparing it to a reference list. The entire recited limitation is part of the abstract idea.
performing a medical license identification confirmation, by determining that the first name and the last name match a first name and a last name associated with the medical license identification;
Same as above. The entire recited limitation is part of the abstract idea, which the Examiner asserts could be completed practically in the human mind.
receiving a live image of the potential user captured by a camera associated with the user device from the user device;
The human mind could practically receive a live image by watching the image play on the device, watching a live image and deriving information being a close derivative related to watching a video and deriving information. A limitation can require the use of a computer or generic device and still recite a mental process. The additional elements are the camera and the associated user device. The camera generally links and the user device is analyzed per the generic computing devices in the independent claims.
performing a facial confirmation, by: identifying a face in the live image,
By broadest reasonable interpretation, this reads on looking at an image and identifying with words or written indications who is in the image, activities that are clearly within the practical mental processes of the human mind. As such it is firmly part of the abstract idea, certainly represented at least as a mental process.
extracting first facial features from the live image, encoding the first facial features into a first vector of numerical values representing unique characteristics of the face, identifying the face from a facial image on the government issued identification,
This could be achieved by looking at the live image, identifying a facial feature that can be ascertained visually (asymmetry of the nose) and encoding the results into a first vector,
a
→
, (hypothetical vector encoding for nose asymmetry) with a pen and paper and then identifying the face from a facial image on the government issued identification (by for example reading the government ID).
extracting second facial features from the facial image, encoding the second facial features into a second vector of numerical values representing unique characteristics of the face,
As per above, this could be achieved by looking at the live image, identifying a facial feature that can be ascertained visually (asymmetry of the nose) and encoding the results into a first vector,
b
→
, (hypothetical vector encoding for eye asymmetry) with a pen and paper and then identifying the face from a facial image on the government issued identification (by for example reading the government ID).
and determining, using a biometric facial matching algorithm that compares the first vector with the second vector image analysis, that [[a]] the face depicted in the live image matches, within a threshold, [[a]] the face depicted in the government issued identification;
This is practically achieved in the human mind. It could be achieved for example by using a pre-existing algorithm to determine if the features represented mathematically are present in the face depicted in the live images. The algorithm could be instructions to subtract the first vector and the second vector, which could be practically performed in the human mind. The result of the algorithmic process on the two vectors could be read (nose asymmetry and facial asymmetry) could be mentally compared to the face depicted in the government issued identification.
performing a medical license confirmation, by determining that the medical license identification is valid based on a response from a medical license database;
This could be practically performed in the human mind. Performing a medical license confirmation by checking an expiration date on the license or in the license information received from the database. The use of the term database for example, also extends beyond electronic databases.
in response to the government issued identification confirmation, the medical license identification confirmation, the facial confirmation, and the medical license confirmation, granting the potential user an account with a medical information sharing service comprising only authorized medical professionals;
This could all be practically performed in the human mind. It amounts to making some potentially simple determinations (subtracting two vectors for example), which each have been described in detail herein to be practically performable in the human mind and then granting the potential user an account with medical information sharing service comprising only authorized medical professionals. The granting of the account could be by giving the potential user a password and username, thereby fulfilling the claim and being practically performable in the human mind. Note that the medical sharing service comprising only authorized medical professionals is not limited to the realm of the computer by broadest reasonable interpretation.
generating a facial template from the facial features from the live image, and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information sharing service;
Also practically achievable in the human mind. Generating a facial template from the facial features from the live image and storing the facial template in association with the account for subsequent authentication of the potential user when accessing the medical information could involve drawing a picture of the facial features depicting in the live image with a pen and paper (thereby also storing the facial template in association with the account in the mind and on the paper, which could be depicted by a written or remembered number) when accessing the medical information sharing service (which by broadest reasonable interpretation could a group of medical professionals that share medical information). Also “…for subsequent authentication of the potential user when accessing the medical information sharing service;…” is a limitation that describes intended use.
and allowing the potential user access to medical information posted within the medical information sharing service.
This could be practically achieved by reading out information in response to orally receiving a password given broadest reasonable interpretation. Also practically executable in the human mind.
Applicant argues that none of the steps paraphrased can be practically performed in the human mind. A human being cannot extract facial features from a digital image and encode them in a vector of numerical values.
The Examiner disagrees and has shown line by line how this could be achieved for the recited abstract idea.
A human being cannot generate a second vector of numerical values from a second digital image and then apply a biometric facial matching algorithm to compare the two vectors against a numerical threshold. The are inherently computational operations that require machine processing of digital image data. The encoding of facial features into numerical vectors and the algorithmic comparison of those vectors are mathematical operations performed on digital representations. They are not observation or evaluations that a human could undertake mentally or with a pen or paper.
The features, limitations or steps that are actually recited in the claim could, by broadest reasonable interpretation, be completed in the human mind in a practical fashion.
The claims recite specific steps, such as face identification, feature extraction, vector encoding, and vector comparison using a biometric matching algorithm, that achieve the technological result of biometric identify verification.
An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Applicant has recited limitations that can be separated into verifiable abstract ideas, which the Examiner has outlined specifically and provided examples for, and recited portions that extend beyond the abstract idea, which in this case can be analyzed as additional elements. Here, the additional elements alone are generic computer parts or devices, and they serve to implement the abstract idea on the computer in a fact pattern that communicates implementation of the abstract idea on a computer. At step 2A prong two, for example, the relationship of the additional elements and/or the elements themselves, either alone or in combination, must establish a relationship to one of the established ways of demonstrating an improvement in MPEP 2106.04(d)(I). Herein, Applicant has recited an abstract idea and laid out the additional elements in a way in which the implementation of the abstract idea on a computer is the communicated distinguishing inventive concept, and this is explicitly not valid reasoning for establishing eligibility at Step 2A prong two or Step 2B. The additional elements do not establish an technical solution to the technical problem defining the improvement.
Applicant argues that Durbin does not teach any of the specific step recited in the amended facial confirmation process. For example, Durbin does not teach identifying a face in a live image. Durbin does not teach extracting facial features from a live image. Durbin does not teach encoding facial features into a vector of numerical values. Durbin does not teach generating a second vector from a facial image on a government issued identification. Durbing does not teach comparing two vectors using a biometric facial matching algorithm.
The arguments are moot; the independent claims have been revised in response to amendment.
Applicant argues Finkelstein is deficient for two reasons.
The arguments are moot; the independent claims have been revised in response to amendment.
Applicant argues the application of Vanko, Kragh, and Smith are deficient.
The arguments directed toward Vanko are entirely moot as Vanko does not appear in the revised rejection. The arguments towards Kragh are that Kragh teaches a multi-step identity proofing process that includes capturing a photograph but does not teach extracting facial features, encoding them into numerical vectors, or comparing vectors using a biometric matching algorithm. Kragh has been rewritten and reapplied to the limitations in response to amendment. The argument that Smith is deficient is generally that Smith does not teach any of the specific computational steps in the amended facial confirmation process. These limitations (as specifically recited) have been addressed line by line in the revised rejection above.
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.
US 10452897 B1 (hereafter Benkreira). Benkreira teaches a system for verifying the identity of a user,
CN 101401104 B (hereafter Koster) teaches retrieving medical data from a server.
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/T.I.E./Examiner, Art Unit 3683 /CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
