DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-4, 6-9, 11-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-4, 6-9, 11-17, and 19-20 are within the four statutory categories. However, as will be shown below, Claims 1-4, 6-9, 11-17, and 19-20 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 11, and 16 are representative of the inventive concept and recite:
Claim 1
A computer-implemented system for automatically assisting physicians during patient encounters, the system comprising:
at least one input device, wherein the input device capture at least one of audio data or image data; and a computerized device comprising a processor, wherein the processor processes, interprets and analyzes data to provide accurate and timely assistance to physicians, by:
receiving audio data from a patient-physician interaction captured by the input device, and transcribing the audio data into transcribed text in real time;
diarizing the transcribed text to attribute speech to a correct speaker;
identifying and extracting clinical concepts from the transcribed text via natural language processing by:
formatting characters and punctuation;
categorizing one or more specific entities in the transcribed text, wherein the one or more specific entities comprise one or more of medical terms, symptoms, diseases, treatments, and medication names from the transcribed text ;
identifying relationships between the one or more entities to produce identified transcribed text;
and analyzing the identified transcribed text to infer at least one of a symptom and a diagnosis;
and integrating real-time data with historical patient records into a combined data set, and via a machine learning algorithm, analyzing the combined data set to generate evidence-based suggestions to the physician comprising one or more of a diagnosis, treatment, or follow-up care based on one or more of patient medical records, patient diagnostic tests, or patient treatment histories, wherein the evidence-based suggestions are presented to the physician through a display device;
training and validating a machine learning model using historical data of the physician, by:
creating a physician-specific model trained on a dataset of anonymized patient-doctor interactions and associated outcomes, the physician-specific model creating a unique profile for each physician;
tracking and storing interactions between physician and the computerized device in a data repository, wherein an interaction from the computerized device to the physician comprise at least one of visual, audio, or tactile input;
and receiving feedback from the physician, wherein the feedback comprises one or more of annotations, corrections, or explanations;
analyzing the feedback from the physician to understand decision-making patterns;
updating the unique profile of the physician based on the analyzed feedback and one or more of the interactions between the physician and the computerized device;
and learning continuously from one or more of the interactions between the physician and the computerized device, and refining the physician model over time;
and generating a comprehensive visit summary of the patient-physician interaction based on the audio data and the image data of the physician, wherein the comprehensive visit summary comprises: predefined templates comprising a format based on a refinement of the physician-specific model, wherein the refinement of the physician-specific model comprises a learned documentation style of the physician.
Claim 11
A computer-implemented method for automatically assisting physicians during patient encounters, the method comprising:
using at least one input device of a computerized device to capture at least one of audio data or image data;
capturing audio data from a patient-physician interaction and transcribing the audio data into transcribed text in real-time;
diarizing the transcribed text to attribute speech to a correct speaker using a-natural language processing;
identifying and extracting clinical concepts from the transcribed text via the natural language processing by:
formatting characters and punctuation; categorizing one or more specific entities in the transcribed text, wherein the one or more specific entities comprise one or more of medical terms, symptoms, diseases, treatments, and medication names from the transcribed text;
identifying relationships between the one or more entities to produce identified transcribed text;
and analyzing the identified transcribed text to infer at least one of a symptom and a diagnosis;
integrating real-time data with historical patient records to form a combined data set, and, via a machine learning algorithm, analyzing the combined data set to generate evidence-based suggestions to the physician comprising one or more of a diagnosis, treatment, or follow-up care based on one or more of patient medical records, patient diagnostic tests, or patient treatment histories, whereby the evidence-based suggestions are presented provided to the physician through a display device;
training and validating a machine learning model using historical data of the physician by:
creating a physician-specific model trained on a dataset of anonymized patient-doctor interactions and associated outcomes, the physician-specific model creating a unique profile for each physician;
tracking and storing interactions between the physician and the computerized device in a data repository, wherein an interaction from the computerized device to the physician comprises at least one of visual, audio, or tactile feedback, wherein an interaction from the physician to the computerized device comprises at least one of acoustic, speech, or tactile input;
receiving feedback from the physician, wherein the feedback comprises one or more of annotations, corrections, or explanations;
analyzing the feedback from the physician to understand decision-making patterns of the physician to at least one of: reject, accept, or accept the evidence- based suggestions with physician inputs;
updating the unique profile of the physician based on the analyzed feedback and one or more of the interactions between the physician and the computerized device;
and learning continuously from one or more of the interactions between the physician and the computerized device, and refining the physician model over time;
and generating a comprehensive visit summary of the patient-physician interaction based on the audio data and the image data of the physician, wherein the comprehensive visit summary comprises: predefined templates comprising a format based on a refinement of the physician-specific model, wherein the refinement of the physician-specific model comprises a learned documentation style of the physician.
Claim 16
A computer-implemented system for interpreting physicians during patient encounters, the system comprising;
at least one input device, wherein the input device captures at least one of audio data or image data;
and a computerized device comprising a processor, wherein the processor processes, interprets, and analyzes data to provide accurate and timely assistance to physicians, by:
diarizing the transcribed text to attribute speech to a correct speaker;
identifying and extracting clinical concepts from the transcribed text via natural language processing, by:
formatting characters and punctuation;
categorizing one or more specific entities in the transcribed text, wherein the one or more specific entities comprise one or more of medical terms, symptoms, diseases, treatments, and medication names from the transcribed text;
identifying relationships between the one or more entities to produce identified transcribed text;
and analyzing the identified transcribed text to infer at least one of a symptom and a diagnosis;
integrating real-time data with historical patient records into a combined data set, and, via a machine learning algorithm, analyzing the combined data set to generate evidence-based suggestions to the physician comprising one or more of a diagnosis, treatment, or follow-up care based on one or more of patient medical records, patient diagnostic tests, or patient treatment histories, wherein the evidence-based suggestions are presented to the physician through a display device;
training and validating a machine learning model using historical data of the physician, by:
creating a physician-specific model trained on a dataset of anonymized patient-doctor interactions and associated outcomes, the physician-specific model creating a unique profile for each physician;
tracking and storing interactions between the physician and the computerized device in a data repository;
using one or more cameras and one or more microphones to:
receive feedback from the physician, wherein the feedback comprises one or more hand gestures corresponding to an acceptance or rejection of the evidence-based suggestion;
analyze the feedback from the physician to understand decision- making patterns;
recognize the one or more hand gestures from the physician;
convert the one or more hand gestures to a command for accepting and rejecting the evidence-based suggestions;
and update the unique profile for the physician based on the analyzed feedback and one or more of the interactions between the physician and the computerized device;
and generating a comprehensive visit summary of a patient-physician interaction based on the audio data and the image data of the physician, wherein the comprehensive visit summary comprises: predefined templates comprising a format based on a refinement of the physician-specific model, wherein the refinement of the physician-specific model comprises a learned documentation style of the physician.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
processing, interpreting, analyzing, transcribing, diarizing, extracting, identifying, integrating, and capturing, tracking, recognizing, converting, formatting, categorizing, creating, generating, refining, learning, and updating) or using pen and paper. Other than reciting generic computer components/functions such as “computer-implemented system”, “input device”, “computerized device comprising a processor”, nothing in the claims preclude the highlighted portions from practically being performed in the mind. For example, in claim 2, but for the computer language, the claim encompasses further defining audio data. 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/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping and thus, the claim recites a mental process. The recitation of generic computer components/functions such as receiving, creating, and generating also covers behavioral or interactions between people, and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2-4, 6-9, 12-15, 17, and 19-20 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 2, reciting how to further categorize audio data, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional limitations:
Claim 1 recites: “computer-implemented system”, “input device”, “computerized device comprising a processor”, “natural language processing”, “machine learning algorithm”, “display”, “storing”, “input”, “physician model”, “physician-specific model”.
Claim 11 recites: “computer-implemented system”, “input device of a computerized device”, “natural language processing”, “display”, “machine learning algorithm”, “training and validating a machine learning model using historical data”, “physician-specific model”, “storing”, “input”.
Claim 16 recites: “A computer-implemented system”, “a computerized device comprising a processor”, “input device in communication with the memory”, “natural language processing”, “machine learning algorithm”, “display”, “device”, “training and validating a machine learning model using historical data of the physician”, “physician-specific model”, “storing”, “computerized device”, “camera”, and “microphone”.
In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations
are recited as being performed by “computer-implemented system”, “input device”, “computerized device comprising a processor”, “natural language processing”, “machine learning algorithm”, “physician model”, “physician-specific model”, “creating a physician-specific model trained on a dataset of anonymized patient-doctor interactions and associated outcomes”, and “input device of a computerized device”, “training and validating a machine learning model using historical data”. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The model is used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “display”, “input”, and “storing”, “camera”, and “microphone”.
Dependent claims 2, 4, 7-9, 17, and 19-20 recite processor
Dependent claim 15 recites storing
In particular, the additional elements do not integrate the abstract idea into a practical
application, other than the abstract idea per se, because the additional elements amount to no more
limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations
are recited as being performed by a processor. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “storing”.
Dependent claims 3, 6, 10, and 12-14 do not include any additional elements beyond those already recited in independent claims 1, 11, and 16 and dependent claims 2, 4, 7-9, 15, 17, and 19-20, hence do not integrate the aforementioned abstract idea into a particular application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 11, and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A system in claim 1; amount to no more than mere instructions to apply and exception and add insignificant extra-solution activity to the
abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no
more than limitations which amount to elements that have been recognized as well- understood,
routine, and conventional activity in particular fields as demonstrated by the recitation of :
Display, which is expressly used to visualize data (Para 0077, Yamada(US 20250172443 A1) discloses: “The main display 600 may include a conventional display device such as a liquid crystal display (LCD), an OLED display, a QLED display or the like. “) in a manner that would be well-understood, routine, and conventional.
Storing data which is the process of retaining information and making it accessible using technology (Para 0069, Nadathur(US 20220206864 A1) discloses: “Storage subsystem 480 includes storage device(s) 484, which can be or include any conventional medium for storing large amounts of data in a nonvolatile manner, such as one or more magnetic, solid state, or optical based disks, or a combination.”) in a manner that would be well-understood, routine, and conventional.
Inputting data refers to information that is put into a computer (Para 0128, Guo(US 20210037397 A1) discloses: “The user input component 2434 can include such conventional input device technologies such as a keypad, keyboard, mouse, stylus pen, and/or touch screen, for example.”) in a manner that would be well-understood, routine, and conventional.
Image, which is expressly used to describe a visual representation of an object in digital form ( Para 0185, Patel(US 20220151541 A1) discloses: “For example, the camera 107 may take routine images of the person's body on different intervals after allergens have been injected into the person's body. “) in a manner that would be well-understood, routine, and conventional.
Audio, which is expressly used to describe sound when it is recorded, transmitted, or reproduced (Para 0069, Chang(US 20120057716 A1) discloses: “FIG. 8a illustrates conventional audio systems with microphones, audio amplifiers, and speakers.”) in a manner that would be well-understood, routine, and conventional.
Camera, which refers to an optical instrument that captures and records images or videos (Para 0282, Raichelgauz(US20230059047A1) discloses: “System 4900 may obtain sensed information from any type of sensors—a camera, one or more sensors implemented using any suitable imaging technology instead of, or in addition to, a conventional camera, an infrared sensor, a radar, an ultrasound sensor, any electro-optic sensor, a radiography sensor, a LIDAR (light detection and ranging), telemetry ECU sensor, shock sensor, etc”) in a manner that would be well-understood, routine, and conventional.
Microphone, which refers to an audio transducer that converts sound waves into electrical signals (Para 0481, Fleury(US 20230031613 A1) discloses: “In another example, a microphone on a local computing device such as a user's mobile phone, maybe used to capture ambient sound and time-stamped. In some embodiments, conventional microphones can be used to capture ambient sound.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 3, 6, 10, and 12-14 do not include any additional elements beyond those already recited in independent claims 1, 11, and 16 and dependent claims 2, 4, 7-9, 15, 17, and 19-20. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 11, and 16 hence do not amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
Subject Matter Free of Prior Art
The following is a statement of reasons for the subject matter free of prior art:
Claims 1, 11, and 16 distinguish over the prior art for the following reasons.
Claim 1:
“… validating a machine learning model using historical data of the physician, by: creating a physician-specific model trained on a dataset of anonymized patient-doctor interactions and associated outcomes, the physician-specific model creating a unique profile for each physician; tracking and storing interactions between the physician and the computerized device in a data repository…”
*Claims 11 and 16 recite similar limitations
The underlined/italicized limitations indicate the reason for subject matter free of prior art.
The closest available prior art of record as follows:
DeVries(US20230170065A1) discloses a treatment recommendation system but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Riskin(WO2012094422A2) discloses a voice based system and method for data input but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Sacaleanu(US20190006027A1) discloses automatic identification and extraction of medical conditions and evidences from electronic health records but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Based on the evidence presented above, none of the closest available prior art of record fairly
discloses or suggests the claimed invention. For this reason, claims 1, 11, and 16 would be found to be subject matter free of prior art as would claims 2-4, 6-9, 12-15, 17, and 19-20 via dependency.
Response to Arguments
112 Rejections
Applicant’s amendments have been fully considered and are persuasive. The 112(f), 112(b), and 112(a) rejections have been withdrawn.
35 U.S.C. 101
(Page 17) Regarding the use of Example 39 to show that the limitations in claim 1 do not recite an abstract idea.
Applicant's arguments filed have been fully considered but they are not persuasive. Training and validating a machine learning model, as applied in the claim amount to mere instructions to apply an exception. Reciting the utilization of a generic machine learning model to analyze data renders it abstract. When removing the machine learning model, from the claim, the data analysis can be performed mentally or using pen and paper, hence the analysis is considered abstract. In example 39, the image processing cannot be done in the human mind or using pen and paper, when the machine learning elements are removed from the claim.
(Page 18) Regarding the assertion that the additional elements recite a specific improvement over prior art systems (referring to Example 42).
Applicant's arguments filed have been fully considered but they are not persuasive. The additional elements identified above do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea.
(Pages 21-22) Regarding the assertion that the claim is a technical improvement by way of Ex Parte Desjardins.
Applicant's arguments filed have been fully considered but they are not persuasive. There is no indication of any improvement to a machine learning model itself. The claim only defines the inputs and outputs of the machine learning model and only improves the output of the model by means of inputs without any alteration of the model itself, which is a requirement to apply Ex Parte Desjardins.
35 U.S.C. 103
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The claims are found to be subject matter free of prior art and the 103 rejection has been withdrawn.
Conclusion
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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