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 .
Status of the Claims
Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement submitted on April 14, 2025 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-10 are drawn to a system for generating and displaying a patient report, which is within the four statutory categories (i.e. machine). Claims 11-20 are drawn to a method for generating and displaying a patient report, which is within the four statutory categories (i.e. process).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A computer-implemented system comprising
a computer having a processor and a memory, the memory having stored thereon computer-executable instructions that, when executed by the processor, cause the processor to run a platform for creating and displaying medical information, the platform being configured to:
receive medical data relating to a patient in a Fast Healthcare Interoperability Resources (FHIR) or Longitudinal Patient Records (LPR) format, wherein the medical data comprises a patient record and at least one vital statistic;
perform temporal analysis on the medical data to produce temporal analysis data;
extract features of the medical data to produce features data;
using an artificial intelligence model trained on historical patient data, analyze the medical data, the temporal analysis data and the features data to generate a knowledge graph by: (i) identifying data elements as nodes in the knowledge graph, (ii) determining relationships between the data elements using semantic analysis and natural language processing of the medical data, (iii) generating edges between the nodes based on the determined relationships; and (iv) applying graph database algorithms to ascertain relationships represented by the edges between entries in the medical data; and
create a visual output comprising (i) a health snapshot containing automatically generated prose describing the patient’s health based on the knowledge graph and ranked using a page rank algorithm, (ii) a body map identifying areas of the patient’s body having health issues based on the knowledge graph, wherein the body map includes interactive indicators that, upon user selection, reveal detailed health information extracted from the knowledge graph, and (iii) a health history displayed in an enhanced timeline format with interactive elements allowing drill-down into specific health events.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of a mental process and/or a certain method of organizing human activity because they recite a process that could be practically performed in the human mind (i.e. observations, evaluations, judgments, and/or opinions – in this case, the steps of receiving medical data, temporally analyzing the received data, extracting features from the medical data, analyzing the medical data, the temporal analysis, and the features to generate a knowledge graph are reasonably interpreted as observations and evaluations that are capable of being performed mentally) or using a pen and paper, but for the recitation of generic computer components (i.e. the computing structure, the artificial intelligence model), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of receiving medical data, temporally analyzing the received data, extracting features from the medical data, analyzing the medical data, the temporal analysis, and the features to generate a knowledge graph, and creating a visual output based on the knowledge graph and temporal data are reasonably interpreted as following rules or instructions to evaluate a patient and create a patient report), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract ideas are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claim 11 is identical as the abstract idea for Claim 1, because the only difference between Claims 1 and 11 is that Claim 1 recites a system, whereas Claim 11 recites a method.
Dependent Claims 2-10 and 12-20 include other limitations, for example Claims 2-4, 8, 12-14, and 18 recite types of data to be displayed, Claims 5 and 15 recite types of data in the received medical data, Claims 6 and 16 recite types of data used in generating the knowledge graph, Claims 7 and 17 recite a methodology for ranking health information, Claims 9 and 19 recite types of data to be analyzed, and Claims 10 and 20 recite generating a confidence score for diagnoses, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent Claims 2-10 and 12-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 11.
Hence Claims 1-20 are directed towards the aforementioned abstract ideas.
Prong 2 of Step 2A
Claims 1 and 11 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the computer and the artificial intelligence model) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computer comprising a processor and memory, and the artificial intelligence model, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0036], [0038], and [0066] of the as-filed Specification, and see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language reciting medical data, which amounts to limiting the abstract idea to the field of healthcare, e.g. see MPEP 2106.05(h).
Additionally, dependent Claims 2-10 and 12-20 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 2-6, 8-9, 12-16, and 18-19), adding insignificant extra-solution activity to the abstract idea (e.g. the types of data used in the calculations recited in dependent Claims 7, 10, 17, and 20), and/or do not include any additional elements beyond those already recited in independent Claims 1 and 11, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-20 do not include additional elements that integrate the judicial exceptions into a practical application.
Step 2B
Claims 1 and 11 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the computer and the artificial intelligence model), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0036], [0038], and [0066] of the as-filed Specification discloses that the additional elements (i.e. the computer and the artificial intelligence model) comprise a plurality of different types of generic computing systems, and well-known types of artificial intelligence models;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the additional elements recite receiving medical data over a network, for example the Internet, e.g. see [0037] of the as-filed Specification;
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the additional elements merely recite the creating and maintaining of medical data that is to be retrieved in order to ultimately generate the visual output;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the additional elements recite storing medical data, and retrieving the medical data in order to generate the visual output;
Dependent Claims 2-10 and 12-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 2-6, 8-9, 12-16, and 18-19), performing repetitive calculations (e.g. the calculation of the ranking of the data elements and the confidence score recited in dependent Claims 7, 10, 17, and 20), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1 and 11, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-20 do not include any additional elements that amount to “significantly more” than the judicial exceptions.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-7, 9, 11-14, 16-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (WO 2022/072785 A1) in view of Basu (US 2008/0300872), further in view of Wong (US 2013/0325493).
Regarding Claim 1, Yang teaches the following: A computer-implemented system comprising
a computer having a processor and a memory, the memory having stored thereon computer-executable instructions that, when executed by the processor, cause the processor to run a platform for creating and displaying medical information (The system includes a computer system including a processor and a memory with computer code instructions, e.g. see Yang [0016].), the platform being configured to:
receive medical data relating to a patient in a Fast Healthcare Interoperability Resources (FHIR) or Longitudinal Patient Records (LPR) format, wherein the medical data comprises a patient record and at least one vital statistic (The system receives patient medical data from an EHR, e.g. see Yang [0009]-[0010], wherein the patient medical data incorporates temporal information (i.e. is in LPR format), such as a duration of symptoms, e.g. see Yang [0031] and [00179].);
perform temporal analysis on the medical data to produce temporal analysis data (The system applies temporal graph models (i.e. performs temporal analysis) to EHR data to incorporate temporal information, e.g. see Yang [00179].);
extract features of the medical data to produce features data (The system performs natural language processing (NLP) on the patient medical data and extracts concept-relation-concept triples (i.e. features) data from the data, e.g. see Yang [0011] and [0054].);
using an artificial intelligence model trained on historical patient data, analyze the medical data, the temporal analysis data and the features data to generate a knowledge graph (The system includes a supervised machine learning model that is trained based on SOAP-structured EHR notes as a text to text generation NLP application, e.g. see Yang [0034], wherein the NLP is used to process the received medical data and generate a patient knowledge graph, e.g. see Yang [0011].) by: (i) identifying data elements as nodes in the knowledge graph, (ii) determining relationships between the data elements using semantic analysis and natural language processing of the medical data, (iii) generating edges between the nodes based on the determined relationships; and (iv) applying graph database algorithms to ascertain relationships represented by the edges between entries in the medical data (The system utilizes NLP to generate the knowledge graph including nodes and edges connecting the nodes, wherein the edges define semantic relations between the nodes, e.g. see Yang [0055], Figs. 1-3.); and
create a visual output comprising (i) a health snapshot containing automatically generated prose describing the patient’s health based on the knowledge graph (The system generates medical support text (i.e. a health snapshot) comprising a medical assessment for a patient, for example a treatment plan, based on the knowledge graph, e.g. see Yang [0009], [0015], [0040], and [0064].).
But Yang does not teach and Basu teaches the following:
wherein the generated prose is ranked using a page rank algorithm (The system extracts keywords from content, relevance ranks the extracted keywords, and generates a summarization hierarchy as a function of the relevance ranked keywords, e.g. see Basu [0027]. Additionally, the system determines the keyword ranking based on various calculations including a number of occurrences of the keyword in a document and a number of occurrences of the keyword in a corpus, e.g. see Basu [0033]-[0034].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of summarizing content to modify Yang to incorporate generating the summary based on a ranking of the contents of the summary as taught by Basu in order to efficiently generate useful and accurate summarization of the content, e.g. see Basu [0027].
But the combination of Yang and Basu does not teach and Wong teaches the following:
the visual output further comprising (ii) a body map identifying areas of the patient’s body having health issues based on the knowledge graph, wherein the body map includes interactive indicators that, upon user selection, reveal detailed health information extracted from the knowledge graph (The system generates a 3D model avatar of the human body (i.e. a body map), e.g. see Wong [0037], Figs. 1 and 6, wherein the avatar includes injuries, problems, diseases, and other conditions displayed on the avatar, e.g. see Wong [0045] and [0122], wherein the injuries, problems, diseases, and other conditions are obtained from various patient medical documents, for example a Continuity of Care Document (CCD), e.g. see Wong [0037]. Additionally, the system may display a saved health topic on an organ of the avatar, wherein a user may click on the organ to display the topics, e.g. see Wong [0143].), and (iii) a health history displayed in an enhanced timeline format with interactive elements allowing drill-down into specific health events (The system creates and displays a timeline, e.g. see Wong [0080], Fig. 7, wherein the timeline includes filtering elements enabling a user to filter content based on criteria and retrieve content based on the criteria, for example conditions, procedures, medications corresponding to specific dates and times, e.g. see Wong [0083]-[0087].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of summarizing content to modify the combination of Yang and Basu to incorporate the avatar and timeline with the interactive elements corresponding to body parts and specific dates as taught by Wong in order to guide health and medical related decision-making by aggregating clinical information about the past, present, and future in a personalized, engaging, and interactive way, e.g. see Wong [0005] and [0037].
Regarding Claim 2, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Yang further teaches the following:
The system of claim 1, wherein the visual output further comprises information relating to a prediction generated by the artificial intelligence model based on analysis of the knowledge graph (The system generates a prediction of a medication code and/or a diagnosis code for a future medical appointment for the patient based on obtained patient medical data and the knowledge graph, e.g. see Yang [0014] and [0060].).
Regarding Claim 3, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Yang further teaches the following:
The system of claim 1, wherein the platform is further configured to calculate and display a health score using the knowledge graph and the artificial intelligence model (The system generates an expanded graph for the patient based on the knowledge graph, e.g. see Yang [0009], and utilizes the expanded graph to generate medical support text, wherein the support text may be a medical assessment (i.e. a health score) for the patient, and wherein the support text may be displayed, e.g. see Yang [0015] and [0064], Fig. 1.).
Regarding Claim 4, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Yang further teaches the following:
The system of claim 1, wherein the knowledge graph includes nodes representing prescribed medications and negative drug outcomes, and wherein the artificial intelligence model analyzes relationships between these nodes to identify drug-related risk factors (The knowledge graph includes data indicating allergic reactions to medications (i.e. negative drug outcomes to prescribed medications), e.g. see Yang [0060], Fig. 3.).
Regarding Claim 6, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Yang further teaches the following:
The system of claim 1, wherein generating the knowledge graph further comprises identifying the data elements as nodes by extracting patient medical information relating to organizations, payors, and practitioners associated with the patient (The knowledge graph is constructed utilizing data from an EHR, wherein the knowledge graph is utilized to generate support text, e.g. see Yang [0009]-[0010], wherein the generated support text may identify care providers, e.g. see Yang [0064].).
Regarding Claim 7, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Basu further teaches the following:
The system of claim 1, wherein the page rank algorithm ranks health information based on how frequently it is referenced by other health records in the medical data (The system extracts keywords from content, relevance ranks the extracted keywords, and generates a summarization hierarchy as a function of the relevance ranked keywords, e.g. see Basu [0027]. Additionally, the system determines the keyword ranking based on various calculations including a number of occurrences of the keyword in a document and a number of occurrences of the keyword in a corpus, e.g. see Basu [0033]-[0034].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of summarizing content to modify the combination of Yang and Wong to incorporate generating the summary based on a ranking of the contents of the summary as taught by Basu in order to efficiently generate useful and accurate summarization of the content, e.g. see Basu [0027].
Regarding Claim 9, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Yang further teaches the following:
The system of claim 1, wherein the artificial intelligence model is configured to analyze medical images to identify features correlated with specific diagnoses based on historical patient data (The obtained medical data from the patient EHR includes objective data, e.g. see Yang [0010], wherein the objective data includes patient imaging results and physical exam findings (i.e. specific diagnoses based on historical patient data), e.g. see Yang [0031], wherein the knowledge graph is constructed utilizing a machine learning model and based on the data obtained from the EHR, e.g. see Yang [0009]-[0011] and [0034].).
Regarding Claims 11-14, 16-17, and 19, the limitations of Claims 11-14, 16-17, and 19 are substantially similar to those claimed in Claims 1-4, 6-7, and 9, with the sole difference being that Claims 1-4, 6-7, and 9 recite a system and its associated hardware (i.e. the computer) whereas Claims 11-14, 16-17, and 19 recite a computer-implemented method. Specifically pertaining to Claims 11-14, 16-17, and 19, Examiner notes that Yang teaches computer hardware that implements a method via software instructions, e.g. see Yang [00192], and hence the grounds of rejection provided above for Claims 1-4, 6-7, and 9 are similarly applied to Claims 11-14, 16-17, and 19.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Yang, Basu, and Wong in view of Conjeti (US 2023/0282337).
Regarding Claim 5, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, and Yang further teaches the following:
The system of claim 1, wherein the medical data further comprises a medical image (The obtained medical data from the patient EHR includes objective data, e.g. see Yang [0010], wherein the objective data includes patient imaging results, e.g. see Yang [0031], wherein the knowledge graph is constructed utilizing a machine learning model and based on the data obtained from the EHR, e.g. see Yang [0009]-[0011] and [0034].).
But the combination of Yang, Basu, and Wong does not teach and Conjeti teaches the following:
wherein the knowledge graph includes nodes representing image features and diagnostic correlations identified by the artificial intelligence model (The system includes an automatic processing algorithm that processes medical image data to generate a knowledge graph, wherein the nodes and linked via relationships based on AI or machine learning based automated computer aided detection tools, and wherein a number of key features are extracted using computer aided detection or diagnostic tools, e.g. see Conjeti [0179].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of summarizing content to modify the combination of Yang, Basu, and Wong to incorporate the extraction of key features of the medical images as part of constructing the knowledge graph as taught by Conjeti in order to aid in the interpretation of medical data, e.g. see Conjeti [0007] and [0095].
Regarding Claim 15, the limitations of Claim 15 are substantially similar to those claimed in Claim 5, with the sole difference being that Claim 5 recites a system and its associated hardware (i.e. the computer) whereas Claim 15 recites a computer-implemented method. Specifically pertaining to Claim 15, Examiner notes that Yang teaches computer hardware that implements a method via software instructions, e.g. see Yang [00192], and hence the grounds of rejection provided above for Claim 5 are similarly applied to Claim 15.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Yang, Basu, and Wong in view of Letts (US 2011/0231205).
Regarding Claim 8, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, but does not teach and Letts teaches the following:
The system of claim 1, wherein the interactive indicators on the body map are displayed as dots on specific body parts, and wherein selecting an indicator reveals detailed health information for the corresponding body part (The system includes a digital body map/avatar of a patient, wherein the avatar includes a plurality of markers, for example colored dots, corresponding to disease sites of the patient, wherein the user may interact with the screen to adjust the view and/or access a detailed view of the disease site, e.g. see Letts [0072]-[0074], Figs. 8-10.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of summarizing content to modify the combination of Yang, Basu, and Wong to incorporate the dots and interactivity of the avatar as taught by Letts in order to enable users to easily monitor changes in the patient condition, e.g. see Letts [0007].
Regarding Claim 18, the limitations of Claim 18 are substantially similar to those claimed in Claim 8, with the sole difference being that Claim 8 recites a system and its associated hardware (i.e. the computer) whereas Claim 18 recites a computer-implemented method. Specifically pertaining to Claim 18, Examiner notes that Yang teaches computer hardware that implements a method via software instructions, e.g. see Yang [00192], and hence the grounds of rejection provided above for Claim 8 are similarly applied to Claim 18.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Yang, Basu, and Wong in view of Roh (US 2023/0215560).
Regarding Claim 10, the combination of Yang, Basu, and Wong teaches the limitations of Claim 1, but does not teach and Roh teaches the following:
The system of claim 1, wherein the platform is further configured to: generate a confidence score for predicted diagnoses based on similarity determinations between features identified in current medical data and correlated features from historical patient data (The system receives patient historical information and biometric data obtained from sensors (i.e. current medical data), determines an abnormal condition, predicts a likely diagnosis based on the abnormal condition, and assigns a confidence level to the predicted diagnosis, e.g. see Roh [0008].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of summarizing content to modify the combination of Yang, Basu, and Wong to incorporate determining the confidence of the predicted diagnoses as taught by Roh in order to enable the effective dispatch of healthcare resources, e.g. see Roh [0002]-[0005].
Regarding Claim 20, the limitations of Claim 20 are substantially similar to those claimed in Claim 10, with the sole difference being that Claim 10 recites a system and its associated hardware (i.e. the computer) whereas Claim 20 recites a computer-implemented method. Specifically pertaining to Claim 20, Examiner notes that Yang teaches computer hardware that implements a method via software instructions, e.g. see Yang [00192], and hence the grounds of rejection provided above for Claim 10 are similarly applied to Claim 20.
Conclusion
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/JOHN P GO/Examiner, Art Unit 3681