DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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 .
Priority
This application claims the priority date of foreign application EP22172674.8 of May 11, 2022.
Status of Claims
Claims 1-19 are pending.
Claim Rejections - 35 USC § 112
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.
Claim 10 is 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. Claim 10 does not appear to be an independent claim, however, it does not refer back to a previous claim. Examiner is interpreting Claim 10 as being dependent on claim 1. Appropriate correction is requested.
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 1-19 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.
Claims 1-19 are drawn to a computer-implemented method, which is within the four statutory categories (i.e., process).
Claims 1-19 recite a computer-implemented method for enriching ambiguous, incomplete or sparse health data, comprising the steps of:
obtaining an input dataset comprising a plurality of electronic health records associated with a patient;
extracting health information that is explicitly recited in the electronic health records from the input dataset, including at least one diagnosis indicated by a name of a disease or a medical classification code which denotes the disease;
generating supplementary health information that is not explicitly documented in the electronic health records based, at least in part, on the extracted health information, the supplementary health information including at least one or more symptoms inferred from the disease directly or indirectly documented in the input dataset; and
validity-scoring at least part of the extracted health information and the supplementary health information to produce an output dataset.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and mathematical concepts, but for the recitation of generic computer components (e.g., analysis module, hospital information system, data processing system, information technology infrastructure, hospital information system, a secured local network connection, a computer system, an automated communication system, a chat bot, a computer program comprising instructions, which, when the program is executed, cause the computer to carry out the method, a data processing system, etc.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-19 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 an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claim 1.
The dependent claims include additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claim 1.
The additional elements from the claims include:
a display (generally linking, MPEP 2106.05(h)).
an electronic device (apply it, MPEP 2106.05(f)).
analysis module (apply it, MPEP 2106.05(f)).
hospital information system (apply it, MPEP 2106.05(f)).
data processing system (apply it, MPEP 2106.05(f)).
information technology infrastructure (apply it, MPEP 2106.05(f)).
hospital information system (apply it, MPEP 2106.05(f)).
a secured local network connection (apply it, MPEP 2106.05(f)).
a computer system (apply it, MPEP 2106.05(f)).
an automated communication system (apply it, MPEP 2106.05(f)).
a chat bot (apply it, MPEP 2106.05(f)).
a computer program comprising instructions, which, when the program is executed, cause the computer to carry out the method (apply it, MPEP 2106.05(f)).
a data processing system (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception – for example, the recitation of an electronic device, analysis module, hospital information system, data processing system, information technology infrastructure, hospital information system, a secured local network connection, a computer system, an automated communication system, a chat bot, a computer program comprising instructions, which, when the program is executed, cause the computer to carry out the method, a data processing system, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g., see Specification Pages 7-9, 11-13, 18, and 21 (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) 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 Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Pages 7-9, 11-13, 18, and 21 discloses that the additional elements (i.e., an electronic device, analysis module, hospital information system, data processing system, information technology infrastructure, hospital information system, a secured local network connection, a computer system, an automated communication system, a chat bot, a computer program comprising instructions, which, when the program is executed, cause the computer to carry out the method, a data processing system) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., a computer);
Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving medication use data, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention obtains input dataset; extracting date from a physical document, e.g., see Content extraction and Transmission, LLC v. Wells Fargo Bank – similarly, the current invention extracts health information in the electronic health records.
Dependent claims 2-19 include other limitations, but none of these functions are deemed significantly more than the abstract idea. Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. 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 any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity or mathematical concepts, for health data enrichment for improved medical diagnostics, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-19 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1-8, 10, 12, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Friedmann (U.S. Pub. No. 2022/0068492 A1) in view of Katouzian (U.S. Pub. No. 2020/0311861 A1).
Regarding claim 1, Friedmann discloses a computer-implemented method for enriching ambiguous, incomplete or sparse health data, comprising the steps of (Paragraph [0009] discusses a method receiving sparse data pertaining to electronic medical records and adding at least a portion of a missing data.):
obtaining an input dataset comprising a plurality of electronic health records associated with a patient (Paragraphs [0009] discuss receiving sparse data pertaining at least to electronic medical records of at least one patient.);
extracting health information that is explicitly recited in the electronic health records from the input dataset, including at least one diagnosis indicated by a name of a disease or a medical classification which denotes the disease (Paragraphs [0080]-[0081] and [0086] discuss receive data from one or more EMRs that may include previous data and/or analysis (e.g., made by a physician, a healthcare professional, and the like) of at least one of a physical, medical, intellectual, social and/or mental condition of the patient, laboratory tests and diagnostic imaging.);
generating supplementary health information that is not explicitly documented in the electronic health records based, at least in part, on the extracted health information, the supplementary health information including at least one or more symptoms inferred from the disease directly or indirectly documented in the input dataset (Paragraphs [0009]-[0012] and [0059] discuss receiving sparse data pertaining to at least EMR of a plurality of patients; preprocessing the sparse data; completing the sparse data, pertaining to EMR of a plurality of patients, by adding at least a portion of a missing data using the cross-validation process; and arranging the parameters in the completed data according to their level of importance, the data received from patients diagnosed with the medical condition; completing parameters missing from the sparse data with parameters having a sufficient similarity, and wherein the similarity is determined based on at least one of: a similarity between patients, similarity between parameters and a combination thereof to predict diseases.); and
validity-scoring at least part of the extracted health information and the supplementary health information to produce an output dataset (Paragraphs [0018]-[0019] and [0112] discuss at least one parameter from the sparse data is a monitored parameter, identifying category dependent parameters in the sparse data; and assigning a score for each category, and completing the sparse data by adding the processed additional data, the cross-validation process may be conducted in order to determine which data element or parameter is reliable and can be used to complete the data.).
Friedmann does not explicitly disclose:
at least one diagnosis indicated by a name of a disease or a medical classification code which denotes the disease.
Katouzian teaches:
at least one diagnosis indicated by a name of a disease or a medical classification code which denotes the disease (Paragraphs [0057] and [0061]-[0062] discuss a biomedical concept extractor, a knowledge repository, and an inference engine for performing biomedical concept detection, which involves mapping words, phrases, medical codes, and other portions of text present in the medical data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, at least one diagnosis indicated by a name of a disease or a medical classification code which denotes the disease, as taught by Katouzian, in order to provide computer aided mechanisms to assist with the analysis of such captured medical images, so as to alleviate the burden on human beings. (Katouzian Paragraph [0003]).
Regarding claim 2, Friedmann discloses wherein of generating the supplementary health information comprises determining, using a mapping, one or more diseases associated with the medical classification documented in the input dataset (Paragraphs [0059] and [0151] discuss completing the sparse data by adding missing data using the cross-validation process which may be conducted in order to determine which data element or parameter is reliable and can be used to complete the data and determine diseases.).
Friedmann does not explicitly disclose:
using a code-disease mapping, one or more diseases associated with the medical classification code documented in the input dataset.
Katouzian teaches:
using a code-disease mapping, one or more diseases associated with the medical classification code documented in the input dataset (Paragraphs [0057] and [0061]-[0062] discuss a biomedical concept extractor, a knowledge repository, and an inference engine for performing biomedical concept detection, which involves mapping words, phrases, medical codes, and other portions of text present in the medical data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, using a code-disease mapping, one or more diseases associated with the medical classification code documented in the input dataset, as taught by Katouzian, in order to provide computer aided mechanisms to assist with the analysis of such captured medical images, so as to alleviate the burden on human beings. (Katouzian Paragraph [0003]).
Regarding claim 3, Friedmann discloses wherein of generating the supplementary health information comprises determining, using a disease-symptom mapping, the at least one or more symptoms associated with the disease documented in the input dataset (Paragraphs [0059], [0151], and [0156] discuss completing the sparse data by adding missing data using the cross-validation process which may be conducted in order to determine which data element or parameter is reliable and can be used to complete the data; the one or more inclusion criteria may include inclusion of the data received from patient in precondition, for example, before the medical condition was diagnosed, suspected and/or showed any known/common symptoms related to (directly or indirectly) and the sparse data may be filtered by, for example, inclusion/exclusion module using inclusion/exclusion criteria and determine diseases.).
Friedmann does not explicitly disclose:
the at least one or more symptoms associated with the disease documented in the input dataset and/or determined using a code-disease mapping.
Katouzian teaches:
the at least one or more symptoms associated with the disease documented in the input dataset and/or determined using a code-disease mapping (Paragraphs [0057] and [0061]-[0062] discuss a biomedical concept extractor, a knowledge repository, and an inference engine for performing biomedical concept detection, which involves mapping words, phrases, medical codes, and other portions of text present in the medical data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, the at least one or more symptoms associated with the disease documented in the input dataset and/or determined using a code-disease mapping, as taught by Katouzian, in order to provide computer aided mechanisms to assist with the analysis of such captured medical images, so as to alleviate the burden on human beings. (Katouzian Paragraph [0003]).
Regarding claim 4, Friedmann discloses wherein the generating the supplementary health information comprises determining, using a drug-symptom mapping and/or a drug-disease mapping, one or more symptoms and/or diseases associated with a drug documented in the input dataset (Paragraphs [0255] discuss some of the modules allow to use the precise patient-diagnosis profiles as an open window for determining novel and personalized medications list per group of patients and/or per specific patient, for example, using any one of the treatment recommendation methods to personalize medicine.).
Regarding claim 5, Friedmann does not explicitly disclose wherein the generating the supplementary health information is based on an ontology;
wherein, the ontology comprises the code-disease mapping, the disease-symptom mapping, the drug-symptom mapping and/or the drug-disease mapping.
Katouzian teaches:
wherein the generating the supplementary health information is based on an ontology (Paragraph [0061] discusses a biomedical concept extractor, a knowledge repository, and an inference engine that comprises logic for performing biomedical concept detection, which involves mapping words, phrases, medical codes, and other portions of text present in the medical data to specific medical concepts, the identification of biomedical concepts is achieved by matching text, codes, etc. from the medical data to terms, codes, the matching may be achieved, for example, using a string matching algorithm, code matching algorithm, ontology based matching algorithm, or the like.);
wherein, the ontology comprises the code-disease mapping, the disease-symptom mapping, the drug-symptom mapping and/or the drug-disease mapping (Paragraphs [0057] and [0061]-[0062] discuss a biomedical concept extractor, a knowledge repository, and an inference engine for performing biomedical concept detection, which involves mapping words, phrases, medical codes, and other portions of text present in the medical data, the matching may be achieved, for example, using a string matching algorithm, code matching algorithm, ontology based matching algorithm.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, wherein the generating the supplementary health information is based on an ontology and wherein, the ontology comprises the code-disease mapping, the disease-symptom mapping, the drug-symptom mapping and/or the drug-disease mapping, as taught by Katouzian, in order to provide computer aided mechanisms to assist with the analysis of such captured medical images, so as to alleviate the burden on human beings. (Katouzian Paragraph [0003]).
Regarding claim 6, Friedmann discloses wherein the validity-scoring comprises ranking diseases and/or symptoms based on a credibility associated with a source of a respective disease and/or a symptom (Paragraphs [0041], [0093]-[0095], and [0124] discuss provide a score for each parameter in the EMR; complete the sparse data by adding at least a portion of a missing data using data that has received a sufficient similarity score and/or a sufficient reliability score.);
wherein documented lab values, signs and/or biosignals indicate a highest credibility (Paragraphs [0041], [0095], and FIG. 9 discuss informative score per parameter of vital signs tests prior to pregnancy and the probability of developing gestational diabetes.);
wherein medical classification used for a diagnosis indicate a second highest credibility (Paragraphs [0034], [0224], [0261], and FIG. 4B discuss an illustration of data hierarchy parameters, the lower the data in the illustrated pyramid the higher is the reliability and relevance of the data, for example, data received from detailed financial transactions (DFT) may be less relevant for prediction of a medical condition than observations and medication - the highest reliability score is given to Admission, Discharge, Transfer (ADT) data received form hospitals. The second highest reliability score is given to laboratory tests, vital signs, demographic data, etc.);
wherein prescribed treatments and/or drugs indicate a third highest credibility (Examiner notes that the prior art does not explicitly indicate third highest.) (Paragraph [0224] discusses hierarchal data, for example, the highest reliability score is given to Admission, Discharge, Transfer (ADT) data received form hospitals. The second highest reliability score is given to laboratory tests, vital signs, demographic data, etc. The third highest reliability score is given to physicians' orders message (ORM) for example, may involve changes to an order such as new orders, cancellations, information updates, discontinuation, etc. The fourth reliability score is given to physician's orders. The fifth highest reliability score is given to medications that were given to the patient.); and
wherein symptoms documented in free text indicate a lowest credibility (Paragraph [0224] discusses hierarchal data, for example, the highest reliability score is given to Admission, Discharge, Transfer (ADT) data received form hospitals. The second highest reliability score is given to laboratory tests, vital signs, demographic data, etc. The third highest reliability score is given to physicians' orders message (ORM) for example, may involve changes to an order such as new orders, cancellations, information updates, discontinuation, etc. The fourth reliability score is given to physician's orders. The fifth highest reliability score is given to medications that were given to the patient. The sixth reliability score is given to observation result (OUR), e.g., allergies, procedures, etc.).
Friedmann does not explicitly disclose:
medical classification codes used for a diagnosis.
Katouzian teaches:
medical classification codes used for a diagnosis (Paragraphs [0057] and [0061]-[0062] discuss a biomedical concept extractor, a knowledge repository, and an inference engine for performing biomedical concept detection, which involves mapping words, phrases, medical codes, and other portions of text present in the medical data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, medical classification codes used for a diagnosis, as taught by Katouzian, in order to provide computer aided mechanisms to assist with the analysis of such captured medical images, so as to alleviate the burden on human beings. (Katouzian Paragraph [0003]).
Regarding claim 7, Friedmann discloses wherein the validity-scoring comprises one or more of the following (Paragraphs [0018]-[0019] and [0112] discuss at least one parameter from the sparse data is a monitored parameter, identifying category dependent parameters in the sparse data; and assigning a score for each category, and completing the sparse data by adding the processed additional data, the cross-validation process may be conducted in order to determine which data element or parameter is reliable and can be used to complete the data.):
scoring a symptom derived from a lab value or a sign with a first validity factor, wherein the first validity factor is preferably 100% (Paragraphs [0018], [0112], [0115], [0170], [0224], and FIG. 9 discuss assigning a score to each category, the second highest reliability score is given to laboratory tests, vital signs, demographic data, etc.; a similarity module gives a similarity score to each parameter based on degree of similarity; examine what is the most accurate and reliable result received; if the algorithm's prediction is perfect, the loss is zero; otherwise, the loss is greater.);
scoring a symptom derived from a biosignal with a second validity factor, wherein the second validity factor preferably depends on an analysis module associated with the biosignal (Paragraphs [0018], [0112], [0224], and FIG. 9 discuss assigning a score to each category, the second highest reliability score is given to laboratory tests, vital signs, demographic data, etc.; a similarity module gives a similarity score to each parameter based on degree of similarity);
scoring a disease derived from a diagnosis or a prescribed treatment with a third validity factor, wherein the third validity factor is based, at least in part, on one or more risk factors of the patient, if present in the input dataset (Paragraphs [0034], [0124], [0224], [0261], and FIGS. 4B and 8-9 discuss an illustration of data hierarchy parameters, the lower the data in the illustrated pyramid the higher is the reliability and relevance of the data; parameters selection module may be configured to complete the sparse data by adding at least a portion of a missing data using data that has received a sufficient similarity score and/or a sufficient reliability score; different parameters affect the medical condition and are assigned a different score, for example, some vital signs, such as, intravascular diastolic BP, intravascular systolic BP, body weight and heartbeat were more relevant to DGM other than other. For example, the measure body weight are more related, thus higher importance in predicting GDM than BMI. Therefore, the processor may give an importance score (e.g., a value) to each vital sign.).
Regarding claim 8, Friedmann discloses wherein each of a plurality of electronic health records comprises a timestamp and wherein the method further comprises (Paragraphs [0159]-[0162] discuss the processor may normalize time dependent parameters in the sparse data received from a patient’s lives to a single timeline, using for example, time normalizing module, may define a timeline for each type of time-dependent parameter, for example, for a specific type of laboratory test the timeline may be the time from conducting the first laboratory test of to the decisive date. The timeline may include the dates and respective value of all the tests of the specific type conducted during the timeline.):
sorting the input dataset by the timestamp (Paragraphs [0159]-[0162] discuss define a timeline for each type of time-dependent parameter, for example, for a specific type of laboratory test the timeline may be the time from conducting the first laboratory test of to the decisive date. The timeline may include the dates and respective value of all the tests of the specific type conducted during the timeline.); and
clustering the input dataset into one or more clusters based, at least in part, on the timestamp (Paragraphs [0159]-[0162] discuss the processor may normalize time dependent parameters in the sparse data received from a patient’s lives to a single timeline, using for example, time normalizing module, may define a timeline for each type of time-dependent parameter, for example, for a specific type of laboratory test the timeline may be the time from conducting the first laboratory test of to the decisive date. The timeline may include the dates and respective value of all the tests of the specific type conducted during the timeline.);
wherein the extracting health information is performed for each cluster (Paragraphs [0017] discuss preprocessing of the sparse data may include: dividing the timeline into time intervals; associating each time dependent parameter with a specific time interval; determining a decay rate parameter for each time interval; and calculating a weight of each time dependent parameter using the corresponding decay rate parameter.).
Regarding claim 10, Friedmann discloses wherein the step of extracting health information comprises processing the input dataset using a feature extraction method for text classification (Paragraphs [0009]-[0010] and [0151] discuss receiving sparse data pertaining to at least EMR of a plurality of patients; preprocessing the sparse data; completing the sparse data, pertaining to EMR of a plurality of patients, by adding at least a portion of a missing data using the cross-validation process; and arranging the parameters in the completed data according to their level of importance; data mining includes identifying similar categorial variables, such as free text from diagnoses, etc.).
Regarding claim 12, Friedmann discloses further comprising providing the output dataset as an input to a computer system for further use, and/or to a machine-learning model (Paragraph [0207] discusses reinforcement between prediction and detection, in order to reduce misdiagnosis, feed the predicted conditions back to the system to further train system to enrich the data and/or to optimize the performances and/or to update the predictions.).
Regarding claim 15, Friedmann discloses further comprising, based at least on the enriched health data, generating a sequence of treatments to be performed (Paragraphs [0018]-[0019], [0056], and [0112] discuss at least one parameter from the sparse data is a monitored parameter and completing the sparse data by adding the processed additional data, the cross-validation process may be conducted in order to determine which data element or parameter is reliable and can be used to complete the data and assist physicians to accurately diagnose and provide correct required treatment.).
Regarding claim 17, Friedmann discloses a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 1 (Paragraph [0026] discusses a memory device, wherein modules of instruction code are stored, and a processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the processor is further configured to perform at least one of the method steps disclosed.).
Regarding claim 18, Friedmann discloses a data processing system comprising means for carrying out the method of claim 1 (Paragraphs [0008]-[0009] discuss a system and method of selecting, by at least one processor, required parameters for prediction or detection of a medical condition and may include: receiving sparse data pertaining at least to electronic medical records (EMR) of at least one patient; preprocessing the sparse data; completing the sparse data by adding at least a portion of a missing data using a cross-validation process; and selecting the required parameters from the completed data.).
Claims 9, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Friedmann in view of Katouzian and in further view of Honke (U.S. Pub. No. 2023/0223144).
Regarding claim 9, Friedmann discloses wherein obtaining the input dataset comprises:
exporting the plurality of electronic health records from a hospital information system (Paragraphs [0003], [0010], and [0062] discuss electronic medical records (EMR) include data collected over a long period of time, and associated with specific patients collected from one or more health care settings, such as, doctors, hospitals, laboratories, diagnostic imaging centers and the like and receiving EMR of a plurality of patients from databases associated with medical care providers, clinical institutes, etc.);
wherein the exported plurality of electronic health records comprises all electronic health records associated with the patient available in the hospital information system (Paragraphs [0080]-[0081] discuss receive EMR of the patient collected from one or more health care settings, such as, doctors, hospitals, laboratories, diagnostic imaging centers and the like.); and
wherein th
Friedmann does not explicitly disclose:
anonymizing the exported plurality of electronic health records; and
anonymizing is performed by a data processing system that is deployed locally within an information technology infrastructure of a hospital comprising the
Honke teaches:
anonymizing the exported plurality of electronic health records (Paragraphs [0025] discuss data access rules executed by the predictive data system permit the PDS to obtain patient data, the PDS assigning anonymized patient identifiers to each set of patient data obtained.); and
anonymizing is performed by a data processing system that is deployed locally within an information technology infrastructure of a hospital comprising the hospital information system, wherein, the data processing system is configured-for communicating with the hospital information system only via a secured local network connection (Paragraphs [0025], [0032], and [0087] discuss a predictive data system in communication with a plurality of healthcare computing devices over a network and data access rules executed by the PDS permit the PDS to obtain patient data, the PDS assigning anonymized patient identifiers to each set of patient data obtained.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, anonymizing the exported plurality of electronic health records and anonymizing is performed by a data processing system that is deployed locally within an information technology infrastructure of a hospital comprising the hospital information system, wherein, the data processing system is configured for communicating with the hospital information system only via a secured local network connection, as taught by Honke, in order to provide aid with data aggregation in detecting time-dependent progression of disease. (Honke Paragraph [0003]).
Regarding claim 11, Friedmann discloses further comprising outputting the output dataset on a display of an electronic device (Paragraph [0082] discusses output devices include user interface adapted to present information to a user and obtain information.).
wherein, optionally, the electronic device is associated with a healthcare professional for use in computer-aided diagnosis (Paragraphs [0057]-[0058], [0082], and FIG. 1A discuss an output device and determine a set of medical tests and optionally parameters that may be required in order to diagnose the future medical condition in the specific person and output devices include user interface adapted to present information to a user and obtain information.).
Friedmann does not explicitly disclose:
or wherein, the electronic device is associated with the patient.
Honke teaches:
or wherein, the electronic device is associated with the patient. (Paragraph [0035] discusses the PDS can send the reconstructed patient data over the network to the healthcare computing devices, user computing device for access by and/or display to individual users or clinicians.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, or wherein, the electronic device is associated with the patient, as taught by Honke, in order to provide aid with data aggregation in detecting time-dependent progression of disease. (Honke Paragraph [0003]).
Regarding claim 19, Friedmann does not explicitly disclose being deployed locally within an information technology infrastructure of a hospital comprising a hospital information system, wherein, optionally the data processing system is for communicating with the hospital information system only via a secured local network connection.
Friedmann does not explicitly disclose:
being deployed locally within an information technology infrastructure of a hospital comprising a hospital information system, wherein, optionally the data processing system is for communicating with the hospital information system only via a secured local network connection.
Honke teaches:
being deployed locally within an information technology infrastructure of a hospital comprising a hospital information system, wherein, optionally the data processing system is for communicating with the hospital information system only via a secured local network connection (Paragraphs [0025], [0032], and [0087] discuss a predictive data system in communication with a plurality of healthcare computing devices over a network and data access rules executed by the PDS permit the PDS to obtain patient data, the PDS assigning anonymized patient identifiers to each set of patient data obtained.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, being deployed locally within an information technology infrastructure of a hospital comprising a hospital information system, wherein, optionally the data processing system is for communicating with the hospital information system only via a secured local network connection, as taught by Honke, in order to provide aid with data aggregation in detecting time-dependent progression of disease. (Honke Paragraph [0003]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Friedmann in view of Katouzian and in further view of Chronis (U.S. Pub. No. 2018/0130555 A1).
Regarding claim 13, Friedmann discloses further comprising, based at least on the enriched health data, prioritizing required treatments, (Paragraphs [0009]-[0012], [0056], and [0059] discuss receiving sparse data pertaining to at least EMR of a plurality of patients; preprocessing the sparse data; completing the sparse data, pertaining to EMR of a plurality of patients, by adding at least a portion of a missing data using the cross-validation process; and assist physicians to diagnose and provide correct treatment.).
Friedmann does not explicitly disclose:
prioritizing patients as to the urgency of required treatments, for in an emergency room.
Chronis teaches:
prioritizing patients as to the urgency of required treatments, for in an emergency room (Paragraph [0013] discusses assist in getting additional information to a triage nurse, or other healthcare individual who needs to triage or otherwise assess a patient, quickly.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, prioritizing patients as to the urgency of required treatments, for in an emergency room, as taught by Chronis, in order to provide monitoring, assessment, and alerts to enable healthcare professionals to proactively intervene, and potentially prevent, adverse health events. (Chronis Paragraph [0012]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Friedmann in view of Katouzian and in further view of Rana (U.S. Pub. No. 2010/0185465 A1).
Regarding claim 14, Friedmann discloses further comprising, based at least on the enriched health data, causing performance of certain treatments, such as an X-ray examination (Paragraphs [0009], [0079], [0147], and [0255] discuss a method receiving sparse data pertaining to electronic medical records and adding at least a portion of a missing data and assist physicians to accurately diagnose the patient and to provide a correct required treatment to the patient; medical insurance companies may conduct an automated and precise prior authorization to determine whether a treatment will be effective on a specific patient or not and will enable to recommend the insurance company to approve it or not.).
Friedmann does not explicitly disclose:
causing performance of certain treatments, such as an X-ray examination, before the first contact with a doctor.
Rana teaches:
causing performance of certain treatments, such as an X-ray examination, before the first contact with a doctor (Paragraph [0039] discusses a procedure can be a visit to a medical practitioner, or consist of a multi-step process including laboratory testing and analysis followed by an appointment with a medical practitioner to review the results.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, causing performance of certain treatments, such as an X-ray examination, before the first contact with a doctor, as taught by Rana, in order to improve efficiency in scheduling appointments.). (Rana Paragraphs [0003]-[0005]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Friedmann in view of Katouzian and in further view of Hame (U.S. Pub. No. 2022/0059238 A1)
Regarding claim 16, Friedmann discloses further comprising, based at least on the enriched health data, recommending one or more actions to the patient (Paragraphs [0018]-[0019], [0056], [0112], [0147] discuss at least one parameter from the sparse data is a monitored parameter and completing the sparse data by adding the processed additional data, the cross-validation process may be conducted in order to determine which data element or parameter is reliable and can be used to complete the data and the trained ML module may predict a medical condition and provide a treatment recommendation based on the parameter.).
Friedmann does not explicitly disclose:
recommending one or more actions to the patient using an automated communication system such as a chat bot.
Hame teaches:
recommending one or more actions to the patient using an automated communication system such as a chat bot (Paragraph [0023] discusses collaborative healthcare system includes patient-specific communication channels that include communication thread-dashboard pairs to facilitate communication among the care providers and virtual healthcare assistants (also referred to as bots).).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Friedmann to include, recommending one or more actions to the patient using an automated communication system such as a chat bot, as taught by Hame, in order to provide complete medical information to assess a condition of a patient.). (Hame Paragraphs [0002]).
Conclusion
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686