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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/10/2025 has been entered.
Response to Amendments
This action is in response to the amendments filed on 02/10/2026. Claims 1 and 9 have been amended. Claims 1-9 are examined below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the limitation “generate a report comprising one or more medical procedures, based on the identified one or more medical conditions, using a trained neural network model, wherein the one or more medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs, cancer screening or a combination thereof” lacks a proper written description. The Examiner points to paragraph [0046] of the specification which states, “The method 700 also includes recommending one or more additional medical procedures to the one or more patients using a plurality of neural network techniques, wherein the one or more additional medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs or a combination thereof in step 790. In one embodiment, recommending one or more additional medical procedures to the one or more patients using a plurality of neural network techniques, wherein the one or more additional medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs or a combination thereof includes recommending one or more additional medical procedures to the one or more patients using a plurality of neural network techniques, wherein the one or more additional medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs, cancer screening or a combination thereof by the diagnostic module.” The specification fails to provide any mention whatsoever of a trained neural network model, nor is there any mention of a generated report. As such, there is no indication that the inventor had possession of a system capable of generating a report using a trained neural network.
Regarding claim 1, the limitation “provide the second healthcare professional with the report, via the consultation module, for communication to the one or more patients” is not supported by the specification. As indicated above, the specification makes no mention of a generated report. Further there is no mention of providing said report to a healthcare professional via the consultation module.
Regarding claim 1, the limitation “compare the first pattern with a prestored data in the integrated database (60) to identify on or more medical conditions of the one or more patients, using a trained neural network model” is not supported by the specification. As indicated above, there is no mention of a trained neural network model, let alone two trained neural network models.
Regarding claim 1, the limitation “analyze the transcript using one or more natural language processing (NLP) techniques comprising at least one of: named entity recognition, semantic analysis, symptom extraction, medical term identification, topic modeling, and clinical summarization to extract audio-based clinical attributes including at least symptoms, conditions, observations, or treatment-related information” is not supported by the specification. The Examiner points to at least paragraph [0023] which states, “The processing subsystem 20 also includes a diagnostic module 110 operatively coupled to the integrated database 60. The diagnostic module 110 is configured to prepare a transcript of the consultation by analyzing one or more audio signals produced during the consultation of the first health care professional using at least one natural language processing technique, wherein the one or more audio signals are corresponding to speech of conversation of the first health care professional with the second health care professional. In one embodiment, the natural language processing technique may include, named entity recognition, sentiment analysis, text summarization, aspect mining, topic modeling and the like. Initially, the one or more audio signals produced during the consultation may be converted into text. A semantic analysis may be carried out to understand a meaning of the text. After the semantic analysis, a sentiment analysis may be performed to detect a positive or negative sentiment in the text. Output of the semantic analysis and the sentiment analysis may be collectively used by the diagnostic module 110 to prepare the transcript of the consultation.” (emphasis added) Based on this, it is apparent that natural language processing technique is used to generate the transcript instead of analyzing the already created transcript. Further, the specification makes no mention of using NLP to extract audio-based clinical attributes. Moreover, the NLP techniques are not indicated as including “symptom extraction”, “medical term identification”, or “clinical summarization”.
Regarding claim 1, the limitation “generate a first pattern based on the audio-based clinical attributes and the image-based clinical attributes” does not appear to be supported by the specification. The Examiner points to at least paragraph [0024] which states, “The diagnostic module 110 is also configured to analyze the one or more medical records of the one or more patients and the transcript using a plurality of image processing techniques to generate a first pattern. In one embodiment, the image processing techniques may be used to generate bounding boxes and segmentation masks for one or more regions detected on the one or more medical records and the transcript using a feature pyramid network technique. In one embodiment, the bounding boxes may include a surrounding sphere (SS), an axis-aligned bounding box (AA.BB), an oriented bounding box (OBB), a fixed-direction hull (FDH), and a convex hull (CH) and the like. In some embodiments, the segmentation masks may be based on semantic segmentation, instance segmentation and the like. As used herein, the one or more regions may be defined as a group of connected pixels with similar properties. As used herein, the feature pyramid network technique may be defined as a feature extractor , which takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. In one embodiment, the first pattern corresponding to the the one or more medical records of the one or more patients and the transcript may be generated based on the bounding boxes and segmentation masks. In one embodiment, the feature pyramid network technique may be replaced by a mask region based convolutional neural network or a graph region based convolutional neural network technique.” Based on this, the first pattern is at most generated based on information ascertained using image processing techniques, which does not include audio-based clinical attributes.
Regarding claim 1, the limitation “wherein the one or more regions correspond to groups of connected pixels having similar medical properties” is not supported by the specification. The Examiner points to at least paragraph [00044] of the specification which states, in part, “As used herein, the one or more regions may be defined as a group of connected pixels with similar properties.” (emphasis added) See MPEP 2163.05 – “New or amended claims which introduce elements or limitations that are not supported by the as-filed disclosure violate the written description requirement. See, e.g., In re Lukach, 442 F.2d 967, 169 USPQ 795 (CCPA 1971) (subgenus range was not supported by generic disclosure and specific example within the subgenus range); In re Smith, 458 F.2d 1389, 1395, 173 USPQ 679, 683 (CCPA 1972) (an adequate description of a genus may not support claims to a subgenus or species within the genus).”
Claim 9 features limitations similar to those of claim 1, and is therefore rejected using the same rationale.
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the limitation “generate a report comprising one or more medical procedures, based on the identified one or more medical conditions, using a trained neural network model, wherein the one or more medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs, cancer screening or a combination thereof” is indefinite. The limitation is preceded by “compare the first pattern with a prestored data in the integrated database (60) to identify on or more medical conditions of the one or more patients, using a trained neural network model”. It is unclear if the “trained neural network model” of the “generate” step is the same as that of the “compare” step, or a separate model as the specification makes no mention of neural network models whatsoever.
Claims 2-8 are rejected as well since they inherit the limitations of claim 1.
Claim 9 features limitations similar to those of claim 1, and is found to omit essential method steps.
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 1-9 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more. Representative claim 1 recites (additional limitations crossed out):
A system for
establish a two-way communication channel between the first health care professional and the second healthcare professional at a time specified during a reservation of the appointment by the appointment management module to avail a consultation of first health care professional by the second health care professional; and
provide one or more medical records of one or more patients relevant to the consultation from the integrated database to the first healthcare professional for consultation.
generate a transcript of the consultation by converting one or more audio signals produced during the consultation into text;
analyze the transcript using o
analyze the one or more medical records comprising medical images of the one or more patients and the transcript
generate a first pattern based on the audio-based clinical attributes and the image based clinical attributes;
compare the first pattern with a prestored data in the integrated database to identify one or more medical conditions of the one or more patients,
generate a report comprising one or more medical procedures based on the identified one or more medical conditions,
provide the second healthcare professional with the report,
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people. That is, other than reciting the steps as being performed by a “processor”, a “memory coupled to the processor”, a “processing subsystem hosted on a server”, “modules”, “at least one natural language processing technique”, “image processing techniques” and “trained neural network models” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people. For example, but for the “processor”, “memory”, “processing subsystem hosted on a server” and “modules” language, the limitations describe a system for health care providers registering to create a profile, a second health care provider providing parameters to conduct a search for a health care professional, reserving an appointment for a consultation, facilitating communication between the health care professionals, providing patient medical records to the first health care professional, transcribing the audio produced during the consultation, analyzing the medical records and the transcript to identify a pattern, comparing the pattern with prestored data to identify a medical condition, generate a report comprising a medical procedure, and providing said report to the second health care provider. The limitations describe the management of personal behavior. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a “processing subsystem hosted on a server”, “modules”, “at least one natural language processing technique”, “trained neural network models”, and “image processing techniques” to perform the claimed steps. The “processing subsystem hosted on a server” and “modules” are recited at a high level of generality (see at least Paras. [00019]-[00025]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. In regards to the “at least one natural language processing technique”, “image processing techniques”, and “trained neural network models”, they are considered to be generic computer function and/or field-of-use/”general link” implementations and does not meaningfully limit the claim (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”). Moreover, the functionality intended to be performed by the “at least one natural language processing technique”, “image processing techniques” and “trained neural network models” appear to be based on very rudimentary constraints (e.g., audio signals, medical records, medical procedures). Without some prohibition in the claims regarding scalability, computation load, etc., these natural language techniques, image processing techniques, and neural network models could reasonably be considered an additional abstract idea in the “mental process” category, but for which is simply automated (i.e., “apply it”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore still directed to an abstract idea.
The claims do 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 elements of a “processing subsystem hosted on a server”, “modules”, “at least one natural language processing technique”, “image processing techniques”, and “trained neural network models” to perform the claimed steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claims are not found to be patent eligible.
Claim 9 features limitations similar to those of claim 1, and is also directed to the same abstract idea without significantly more.
Claims 2-9 are dependent on claim 1, and include all the limitations of claim 1. Therefore, they are also found to be directed to an abstract idea. Claim 4 features the additional limitations of a “payment link” and “payment gateways”, however these additional elements merely serve to place a judicial exception (i.e., payment transaction) into a computer environment. Claim 7 features “display[ing] one or more advertisements on the one or more devices”, however this is merely insignificant extra-solution activity as it is merely the output of data. The remaining dependent claims have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 5, 6, 8, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US 2019/0147141) in view of Joshi (US 2021/0327582) and Mask R-CNN by Kaiming He, available January 24, 2018, hereinafter referred to as He.1
Regarding claim 1, Kahn discloses A system for enabling medical consultation online comprising:
A processor; and
A memory coupled to the processor,
Wherein the memory comprises a set of program instructions in the form of a processing subsystem, configured to be executed by the processor,
Wherein the processing subsystem hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules comprising:
a registration module configured to register one or more health care professionals by creating one or more health care professional profiles in an integrated database upon receiving one or more health care professional details via one or more devices; (See at least Para. [0125] – “In some embodiments, experts such as doctors, psychologists, lawyers, counselors, realtors, professionals, service providers and others 204 may also register to provide services to clients 202 through the counseling services platform 203. In some embodiments, the expert 204 may access the same initial web page that a user may use to register or a separate web page interface may be provided for experts 204. In any case, an expert may access an expert registration page that facilitates exchange of information between the expert 204 and the counseling services platform 203. An expert may input personal and business information, credit card data, banking data and other information to facilitate business transactions. An expert may also input 208 service descriptions, payment terms and a calendar or schedule. This information may be stored by the counseling services platform 203 as an expert or professional profile and schedule 210.” See at least Para. [0117] for disclosure of a server, which would include a processor and memory.)
a specialist search module operatively coupled to the registration module, wherein the specialist search module is configured to conduct a search for a first health care professional in the one or more health care professional profiles created by the registration module upon receiving a search query raised by a second health care professional, wherein the search query comprises one or more parameters associated with the first healthcare professional; (See at least Para. [0007] – “In such configurations, upon accessing and logging into the website, the client is permitted to search a database for online experts and select an appropriate expert for a counseling session.”, and Para. [0076] – “In some embodiments, clients can search for experts based on their ranking, location, areas of practice, credentials, years of experience, education, and/or other variables.” he Examiner asserts that the that claim language of a “second health care professional” is merely a label for the “user” of the system and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the title of the user) which does not explicitly alter or impact the steps of the method (i.e., the actions performed by the claimed system) does not patentably distinguish the claimed invention from the prior art in terms of patentability. Therefore, it would have been obvious to a person of ordinary skill in the art at the time of invention to have the “client” of Kahn be a “second health care professional” because the particularity of the user of the claimed system does not functionally alter or relate to the steps of the method and merely labeling the user differently from that of the prior art does not patentably distinguish the claimed invention.)
an appointment management module operatively coupled to the specialist search module, wherein the appointment management module is configured to reserve an appointment of the first health care professional appeared in the search conducted by the specialist search module upon receiving a reservation request from the second health care professional; (See at least Para. [0135] – “Some embodiments of the present invention may comprise a platform 203 with a network of connected virtual waiting rooms associated with experts 204 who provide services through the platform 203. These waiting rooms provide a launch point for providing services to users 202 who have scheduled appointments for services and who have indicated their readiness to receive services by entering the virtual waiting room.”
a consultation module operatively coupled to the appointment management module wherein the consultation module is configured to:
establish a two-way communication channel between the first health care profession and the second healthcare professional at a time specified during a reservation of the appointment by the appointment management module to avail a consultation of first health care professional by the second health care professional; (See at least Para. [0131] – “When both the expert 204 and the user 202 have indicated readiness, an online session 238 between the user 202 and the expert 204 may be facilitated by the platform 203. This online session 238 may comprise a videoconference, an audio conference, a chat forum, virtual messaging, document exchange and other media functions.” The Examiner notes that the language “to avail a consultation of first health professional by the second health care professional” is a statement of intended use and fails to result in a manipulative difference between the claimed invention and the prior art.) and
provide one or more medical records of one or more patients relevant to the consultation from the integrated database to the first healthcare professional for consultation. (See at least Para. [0175] – “In some embodiments, an expert 204 can collect psychosocial history data including presenting problems, current symptom checklists, emotional/psychiatric history, family history, medical history, substance use history, developmental history, socio-economic history and other data. In some embodiments, this data can be collected by making electronic forms available in the expert's virtual waiting room. Data can be collected pre, during or post session.”
Kahn does not explicitly disclose a diagnostic module operatively coupled to the integrated database, wherein the diagnostic module is configured to:
prepare a transcript of the consultation by analyzing one or more audio signals produced during the consultation of the first health care professional using at least one natural language processing technique;
analyze the one or more medical records comprising medical images of the one or more patients using a plurality of image processing techniques to extract image-based clinical attributes;
generate a first pattern based on the audio-based clinical attributes and the image-based attributes
compare the first pattern with a prestored data in the integrated database to identify one or more medical conditions of the one or more patients, using a trained neural network model;
generate a report comprising one or more additional medical procedures based on the identified one or more medical conditions, using a trained neural network model, wherein the one or more medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs, cancer screening or a combination thereof; and
provide the second healthcare professional with the report, via the consultation module, for communication to the one or more patients.
(See at least Joshi, Para. [0056] – “In accessing/receiving the medical record data (e.g., electronic health records), the data acquisition/link module 103 can use a medical record API ( e.g., the Allscripts API or the Veterans Affairs Health API). As further examples, the data acquisition/link module can use one or more of optical character recognition (OCR), NLP, and fuzzy logic in accessing/receiving the medical record data. For example, the OCR, NLP, and/or fuzzy logic can be applied to imaged faxes, pill bottle prescription labels, and/or reimbursement checks/deposits. The imaging of these inputs can be performed via scanner or smartphone camera, as just some examples. In this way, benefits such as being able to utilize various types of medical forms, semi-unstructured medical data, and unstructured medical data can accrue. The OCR, NLP, and fuzzy logic capabilities can be provided by the machine learning module 129, as just an example. Further still, in various embodiments the system can integrate with electronic health records to share patient health data with the care team ( e.g., clinicians thereof). Also, the system can import patient health data through this integration using the system's API framework. The system can, in various embodiments, utilize Fast Healthcare Interoperability Resources (FHIR) for achieving interoperability in terms of health data. It is noted that the term "electronic health record" as used herein throughout can refer, for example, to an external electronic health record which is accessed by the system.”, Para. [0061] – “The analysis of the received linguistic data can include extracting keywords from the linguistic data. The keywords can, as just some examples, regard sentiment of the monitored user, and/or be medically related keywords (e.g., keywords indicative of symptoms and conditions). As an example, the keyword extraction can be performed using one or more MLMs of the machine learning module 129.”, Para. [0073] – “While extraction of keywords from a text representation of verbal input is discussed, other possibilities exist. As examples, keyword extractions can alternately or additionally be performed with respect to data generated by IoT/health-monitoring devices and/or data drawn from electronic health records.”, Para. [0076] - “Discussed has been an example where an MLP based classifier receives as input one or more of: a) data regarding verbal inputs to the system; b) data generated by IoT/health-monitoring devices; and c) data regarding electronic health records. Further discussed has been this classifier generating as output a predicted condition/health status, and/or a care recommendation.”, Para. [0081] – “Turning to FIG. 6, an MLP-based classifier of the sort noted, which can output care recommendations, is discussed. As depicted by FIG. 6, the classifier can receive as input data regarding electronic health records 601 (labeled "Preexisting Symptom Conditions" in FIG. 6). This input can be derived from historical medical records, and can regard, as just an example, medications prescribed. In some embodiments, such data can be obtained from static storage. The classifier can also receive as input data generated by IoT/health-monitoring devices 603 (labeled "IoT Based Current Data" in FIG. 6). This input can include timestamped data and location data ( e.g., GPS and Bluetooth beacon data). The data can be obtained regularly from a smartphone of the monitored user and/or sensors worn by the monitored user. In some embodiments, the data can be obtained from dynamic storage. Further, the classifier can receive as input data regarding verbal inputs to the system ( or inputs provided via the mobile app) 605 (labeled "Current Symptoms" in FIG. 6). This input can include manually recorded current symptoms, and/or qualitative observations (e.g., fever and cough). In some embodiments, the data can be obtained from dynamic storage.”, Para. [0085] – “For example, a recording of the conversation can be received from the device at the human interface module 131. The human interface module 131 (or another module) can then obtain a transcription of the recording (e.g., via AWS Transcribe Medical). Subsequently, the transcribed text can be analyzed for indicators of health such as keywords relating to health symptoms, conditions, sentiment, nutrition, fitness, health data, social indicators, and indicators of cognitive ability. Such keyword extraction can be performed as discussed above ( e.g., using the noted RNN-based MLM, or using Amazon Comprehend Medical). Then, the generated keywords can be provided to the MLP-based classifier. The classifier can subsequently use the keywords in generating care recommendation and predicted condition/health status outputs.”, Para. [0086] – “In this way, the system can analyze conversations with monitored users, using natural language processing to extract keywords that are indicators of health….Alternately or additionally, in some embodiments the reply of the monitored user can be used in connection with a third-party symptom checker web service or database, in order to receive care recommendations and predicted condition/health status outputs therefrom.”, and claim 1 – “ communicating, by the computing system, using one or more of a mobile app or a virtual assistant capability, one or more of the condition/health status or the care recommendation.” Also see at least Para. [0067]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kahn to utilize the teachings of Joshi since it would allow the medical experts of Kahn to automate the diagnosis of patients. The Examiner asserts that the that claim language of “medical records comprising medical images”, and “wherein the one or more additional medical procedures comprises at least one of an acute disease screening, chronic diseases screening, immunization programs, cancer screening or a combination thereof” are merely labels for the “medical records” and “medical procedures” and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of medical record/procedure) which does not explicitly alter or impact the steps of the method (i.e., analyzing a medical record, recommending a medical procedure) does not patentably distinguish the claimed invention from the prior art in terms of patentability. Therefore, it would have been obvious to a person of ordinary skill in the art at the time of invention to have the “medical records” comprise “medical images”, and “care recommendation” of Kahn and Joshi be “at least one of an acute disease screening, chronic diseases screening, immunization programs, cancer screening or a combination thereof” because the particular type of medical record/procedure does not functionally alter or relate to the steps of the method and merely labeling the medical record/procedure differently from that of the prior art does not patentably distinguish the claimed invention.)
Kahn and Joshi do not disclose:
wherein the plurality of image processing techniques are configured to generate bounding boxes and segmentation masks for one or more regions detected on the one or more medical records and the transcript using a feature pyramid network technique,
wherein each bounding box comprises at least one of a surrounding sphere (SS), an axis-aligned bounding box (AABB), an oriented bounding box (OBB), a fixed-direction hull (FDH), and a convex hull (CH),
wherein each segmentation mask is based on at least one of semantic segmentation or instance segmentation,
wherein the one or more regions correspond to groups of connected pixels having similar medical properties, and
(See He, Page 1 – “We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.”, Page 3 – “Mask R-CNN is conceptually simple: Faster R-CNN has two outputs for each candidate object, a class label and a bounding-box offset; to this we add a third branch that outputs the object mask. Mask R-CNN is thus a natural and intuitive idea. But the additional mask output is distinct from the class and box outputs, requiring extraction of much finer spatial layout of an object. Next, we introduce the key elements of Mask R-CNN, including pixel-to-pixel alignment, which is the main missing piece of Fast/Faster R-CNN.”, Page 4 – “We also explore another more effective backbone recently proposed by Lin et al. [27], called a Feature Pyramid Network (FPN). FPN uses a top-down architecture with lateral connections to build an in-network feature pyramid from a single-scale input. Faster R-CNN with an FPN backbone extracts RoI features2 from different levels of the feature pyramid according to their scale, but otherwise the rest of the approach is similar to vanilla ResNet. Using a ResNet-FPN backbone for feature extraction with Mask RCNN gives excellent gains in both accuracy and speed. For further details on FPN, we refer readers to [27].” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Kahn and Joshi to analyze medical records and transcripts utilizing the teachings of He since at least Joshi and Kahn are in the same field of endeavor (i.e., feature extraction), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. However, in regards to the bounding boxes, He does not explicitly disclose that they are a surrounding sphere (SS), an axis-aligned bounding box (AABB), an oriented bounding box (OBB), a fixed-direction hull (FDH), or a convex hull (CH). NPL Li (See Conclusion section below) indicates that the listed types are the five types of bounding boxes. Therefore, the Examiner concludes that the bounding boxes of He would include at least one of the listed types disclosed in the claim. The Examiner asserts that the that claim language of “medical properties” is merely labels for the “properties” and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of property) which does not explicitly alter or impact the steps of the method (i.e., identifying similar properties) does not patentably distinguish the claimed invention from the prior art in terms of patentability. Therefore, it would have been obvious to a person of ordinary skill in the art at the time of invention to have the properties of Kahn, Joshi, and He be “medical properties” because the particular type of property does not functionally alter or relate to the steps of the method and merely labeling the property differently from that of the prior art does not patentably distinguish the claimed invention. )
Regarding claim 2, Kahn discloses The system as claimed in claim 1, wherein the one or more health care professional details comprises at least one of an enrollment ID, personal whereabouts, experience, specialization and available timings for consultation. (See at least Para. [0075] – “In some embodiments, experts may choose, or be required, to enter their qualifications, credentials, etc., in order to register for and/or use or otherwise provide services through the online counseling system. In some embodiments, the qualifications, credentials, insurance, degree, work history, licensing information, etc. that is entered by experts may be individually verified through appropriate channels and/or third party sources.”
Regarding claim 3, Kahn discloses The system as claimed in claim 1,wherein the one or more parameters comprises at least one of a specialization, the available time for the consultation, an experience or a combination thereof. (See at least Para. [0076] – “In some embodiments, clients can search for experts based on their ranking, location, areas of practice, credentials, years of experience, education, and/or other variables.”)
Regarding claim 5, Kahn discloses The system as claimed in claim 1, wherein the processing subsystem comprises a reminder generation module operatively coupled to the integrated database, wherein the reminder generation module is configured to generate one or more alerts to remind the one or more health care professionals regarding the appointment based on the time specified during the reservation of the appointment by the appointment management module. (See at least Para. [0085] – “In some embodiments, if a request for an appointment is confirmed, it automatically schedules a counseling session on the expert's calendar and/or creates automated reminders for the parties involved.”)
Regarding claim 6, Kahn discloses The system as claimed in claim 1, wherein the processing subsystem comprises a status update module operatively coupled to the integrated database, where in the status update module is configured to:
update one or more available timings for consultation by the one or more health care professionals in corresponding the one or more health care professional profiles; (See at least Para. [0011] – “In some configurations, when scheduling an appointment, a client logs in to the expert's website or other type of Internet portal and accesses a scheduling calendar which displays the available counseling sessions.) and
update a real time availability of the one or more healthcare professionals corresponding to the one or more health care professional profiles. (See at least Para. [0011] – “In some configurations, if an expert finds himself or herself without any clients, e.g., due to cancellations, the inability of clients to travel to the office due to severe weather, etc., that expert can place an "available" icon on the homepage so that clients desiring counseling know that they can have immediate access without consulting
the calendar.”
Regarding claim 8, Kahn discloses The system as claimed in claim 1, wherein the processing subsystem comprises a subscription management module operatively coupled to the integrated database, wherein the subscription management module is configured to provide controlled access for the one or more healthcare professionals to the plurality of the modules based on a subscription secured by the one or more healthcare professionals. (See at least Para. [0077] – “In various embodiments, clients pay to use the online counseling system. For example, in some embodiments, clients pay an annual membership fee. In other embodiments, however, only third party payers who license the portal pay a licensing fee. In some embodiments, the portal will be given to an expert or other general practitioners free of charge. In some embodiments, experts or their respective organizations may purchase a monthly licensing fee or purchase a block of session call time which may have an expiration date if not used.”)
Claim 9 features limitations similar to those of claim 1, and is therefore rejected using the same rationale.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US 2019/0147141) in view of Joshi (US 2021/0327582) and Mask R-CNN by Kaiming He, available January 24, 2018, hereinafter referred to as He, and in further view of Official Notice.
Regarding claim 4, Kahn, Joshi, and He do not explicitly disclose The system as claimed in claim 1, wherein the processing subsystem comprises a payment management module configured to:
generate a payment link to the second health care professional to enable a payment of a predefined consultation fee;
enable the payment of the predefined consultation fee via the payment link through one or more payment gateways; and
generate a payment receipt upon successful payment of the predefined consultation fee and update the payment details corresponding to the one or more healthcare professional profiles.
Kahn discloses a client paying a fee to the practitioner as well as a fee associated with using the online services (see Para. [0078]). Kahn also discloses sales processing being handled by service providers such as Google Checkout, and Paypal. However, said sales processing is not in regards to the online services (i.e., consultation fee), and the particular manner that is claimed is not explicitly disclosed. The Examiner takes Official Notice that the claimed limitations describe a standard online payment transaction. Such examples can be found on ecommerce websites such as Amazon or Ebay, wherein payment links are provided to provide payment to merchants and payment receipts are provided to user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Kahn, Joshi, and He to utilize the teachings of Official Notice because all the claimed elements/steps were known in the prior art and one skilled in the art could have combined the elements/steps as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US 2019/0147141) in view of Joshi (US 2021/0327582) and Mask R-CNN by Kaiming He, available January 24, 2018, hereinafter referred to as He, and in further view of Neff (US 2020/0066414).
Regarding claim 7, Kahn, Joshi, and He do not explicitly disclose The system as claimed in claim 1, wherein the processing subsystem comprises an advertisement module operatively coupled to the integrated database, wherein the advertisement module is configured to display one or more advertisements on the one or more devices associated with one or more healthcare professionals upon receiving one or more targeting parameters from the integrated database and geo location data associated with the one or more healthcare professionals. (See Neff, Para. [0315] – “In this invention, discounts are targeted to patient based on their geolocation, medical conditions, and time since last visit.”, and Para. [0318] – “After completion of the telehealth session, and use of the terms and EMR information, coupons can be generated to the patient. The system automatically generates a coupon code or incentive for the patient for follow up care, either through additional telehealth visit or in person visit. The coupon code is dynamically created for each patient. The coupon codes can further include coupons based on a visit or the lack of a recent visit.”. Also see Paras. [0319] – [0330]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Kahn, Joshi, and He to utilize the teachings of Neff since it would provide incentive for users to continue use of the system (Para. [0315]).
Response to Arguments
Applicant's arguments regarding claims rejected under 35 U.S.C. 112(a) and 112(b) have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues concerning “neural network techniques” (now amended to “trained neural network model” which does not appear in the specification at all) are not persuasive. As stated in previous actions, there is no mention of any particular algorithms/methodologies regarding how these totally undisclosed neural network techniques are utilized to recommend medical procedures.
Applicant’s arguments concerning “recommend one or more medical procedures…” are not persuasive as the Applicant has amended the claim language to introduce new matter.
Based on at least the above, the 112(a) rejection is maintained.
Applicant's arguments regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that “a human mind cannot generate segmentation masks, compute feature pyramid outputs, or perform neural network comparisons against prestored data sets”, and further argues that the “integration of NLP-derived textual clinical features with computer vision-derived anatomical features into a unified neural network input represents a technological improvement”. This is not persuasive. As indicated in the body of the rejection above, these features fall under “apply it”.
Applicant’s arguments concerning providing a technical solution are not persuasive. Per Applicant’s statement, the claims feature well-understood and conventional functions. See pages 12-13 of Response – “The specification provides sufficient structural and functional details for a person of ordinary skill in the art to understand the application of neural network techniques in this workflow, while implementation details such as the specific network architecture, loss functions, or training procedures are not required to be described in details, as these are well-understood and conventional to a skilled practitioner in the field of medical machine learning” (emphasis)The performance of well-understood and conventional functions does not provide a technical improvement or constitute “significantly more”.
The Examiner again points to paragraphs [0003-[0004] which state, “Even though, existing systems are offering a variety of the telemedicine services, the existing systems are mainly focusing on establishing a two-way communication channel between the patient and the doctor. Consultation of a specialist doctor may not be possible always for the patient especially when the patient is illiterate or not having sufficient knowledge about how to operate the existing systems. Further, the existing systems are not sufficient to enable the consultation of the specialist doctor by the doctor on behalf of the patient. Furthermore, the existing systems may not be able to generate a transcript of the consultation while availing the telemedicine services and are not efficient enough to support the patient by suggesting additional consultation by referring to medical records of the patient or by reminding the patient about a reserved appointment Also, the existing systems may not be capable of recommending cancer screening, chronic disease screening and immunization to the patient.”, and “Hence, there is a need for an improved system and method for enabling medical consultation online to address the aforementioned issue(s).”, respectively. If anything, this indicates that the claims provide an improvement to the non-technical problems disclosed in paragraph [0003], and does not feature any of the alleged improvements stated by the Applicant in the arguments..
For at least these reasons, the 101 rejection is maintained.
Applicant's arguments regarding claims rejected under 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant’s arguments regarding He not disclosing handling of medical records databases, transcript generation, NLP extraction, etc., are not persuasive as He was cited as disclosing the limitations regarding the image processing techniques.
Applicant’s arguments concerning the loss preservation of function by introducing He into the Kahn/Joshi framework is not persuasive. Joshi has already established the use of optical character recognition (See para. [0056]). One of ordinary skill in the art would understand that OCR features the use of bounding boxes and segmentation masks. Thus, introducing the elements explicitly disclosed by He into the system of Kahn and Joshi would indeed preserve the functionality of Kahn and Joshi.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Design and Application of An Attractiveness Index for Urban Hotspots Based on GPS Trajectory Data” by Li Cai, available September 2018 explicitly states the five types of bounding boxes (See Page 4 – “There are five types of bounding boxes, i.e., a surrounding sphere (SS), an axis-aligned bounding box (AABB), an oriented bounding box (OBB), a fixed-direction hull (FDH), and a convex hull (CH) [26].”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
1 Available at https://arxiv.org/pdf/1703.06870
2 RoI features equate to “first pattern”