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.
Claim Objections
Claim 1 is objected to for the following informalities: Claim 1 recites “a plurality of encounter-centered data structures…including temporal attributes defining a time interval of the medical encounter produce temporal analysis data.” In the interest of compact prosecution, this language will be interpreted as “a time interval of the medical encounter to produce temporal analysis data.” Appropriate correction is required.
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:
train an artificial intelligence model on historical patient data;
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;
transform the medical data into a plurality of encounter-centered data structures each associated with a respective medical encounter and including temporal attributes defining a time interval of the medical encounter to produce temporal analysis data;
align the temporal analysis data into a uniform chronological structure;
extract features of the medical data to produce features data, wherein the features data comprises structured data elements corresponding to predefined entity types including at least one of patient, practitioner, medication, condition, procedures, and observation;
using the artificial intelligence model, analyze the medical data, the temporal analysis data and the features data to generate a knowledge graph by: (i) identifying the structured data elements as nodes in the knowledge graph, and assigning node types based on the predefined entity types, (ii) determining relationships between the data elements using semantic analysis and natural language processing of the medical data, including determining candidate relationships based on co-occurrence within a common encounter-centered data structure, (iii) generating edges between the nodes based on the determined relationships, including generating edges only when a relationship criterion is satisfied, the relationship criterion including a correlation condition derived from historical patient data; and (iv) applying graph database algorithms to ascertain relationships represented by the edges between entries in the medical data; and
generate a visual output, by querying the knowledge graph to retrieve interconnected medical events based on the generated edges, the 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 a body of the patient 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, transforming the medical data into a plurality of encounter-centered data structures to produce temporal analysis data, aligning the temporal analysis data into a uniform chronological structure, extracting features from the medical data, analyzing the medical data, the temporal analysis, and the features to generate a knowledge graph, and generating the visual output based on the knowledge graph and temporal data recite observations and evaluations that are capable of being performed mentally, and/or collecting data, analyzing the data, and displaying certain results of the collection and analysis) 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, transforming the medical data into a plurality of encounter-centered data structures to produce temporal analysis data, aligning the temporal analysis data into a uniform chronological structure, extracting features from the medical data, analyzing the medical data, the temporal analysis, and the features to generate a knowledge graph, and generating the visual output based on the knowledge graph and temporal data recite 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, and the training of 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, 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, and the claim language of the artificial intelligence model and its training, which amounts to limiting the abstract idea to the field of machine learning, 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, and the training of 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.
Subject Matter Free From Prior Art
Claims 1-20 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 101. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Yang (WO 2022/072785 A1) teaches a system that obtains patient EHR data, performs natural language processing (NLP) on the EHR data to extract concept-relation-concept triples data, inputs the obtained data into a machine learning model trained on the patient EHR data, and generates a knowledge graph including supporting text for the patient. However, Yang does not teach generating a body map, and does not teach that the supporting text includes prose ranked using a page rank algorithm. Additionally, Yang does not teach that the temporal attributes of the encounter-centered data structures define the time interval of the medical encounter, and generating a timeline. Furthermore, Yang does not teach the specific node types, the relationships being defined as a co-occurrence within a common encounter-centered data structure, and the defining of the edges for defining the knowledge graph.
Basu (US 2008/0300872) teaches generating a summarization hierarchy as a function of relevance of extracted keywords from a document, wherein the keyword ranking is 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. However, Basu does not teach any type of artificial intelligence or machine learning model, and further does not teach the creation of a knowledge graph. Additionally, although Basu does teach displaying keywords on a timeline, it does not teach temporal data and/or the construction of a timeline with regards to patient encounters. Basu further does not teach the construction of a patient body map displaying patient conditions.
Wong (US 2013/0325493) teaches constructing a 3D model avatar of a human body including injuries, problems, diseases, and other conditions displayed on the avatar, based on data extracted from patient medical documents. Wong further teaches creating a timeline and enabling a user to filter elements based on various criteria including specific dates and times. However, Wong does not teach the creation of a knowledge graph, and/or the involvement of any type of artificial intelligence/machine learning model. Furthermore, Wong does not teach that the supporting text accompanying the body model includes prose ranked using a page rank algorithm.
Letts (US 2011/0231205) teaches the construction of a body map/avatar for a patient, wherein the avatar includes a plurality of markers such as colored dots, corresponding to disease sites of the patient, wherein a user may interact with the screen to adjust the view and/or access a detailed view of the disease site. However, Letts does not teach the creation of a knowledge graph, and/or the involvement of any type of artificial intelligence/machine learning model. Additionally, Letts does not teach that the supporting text accompanying the body model includes prose ranked using a page rank algorithm.
Li (US 2022/0093268) teaches generating a knowledge graph for a patient diagnosis that is based on machine learning technology. Furthermore, Li teaches that the data analyzed to create the knowledge graph includes patient visit records, wherein each visit record spans a time period. However, Li does not teach any type of data used to train the machine learning model, and further does not teach the construction of a uniform chronological structure or any type of timeline. Additionally, although Li teaches candidate association node pairs based on a semantic association similarity of the nodes, Li does not teach generating edges between the nodes based on the semantic association similarity or a different type of correlation condition. Furthermore, Li does not teach the generation of any type of body map for the patient showing the patient condition.
The aforementioned references are understood to be the closest prior art. Various aspects of the claimed invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claims 1-20 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed July 13, 2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant alleges that the claimed invention is patent eligible because it improves the way a computer processes and organizes complex data, and hence integrates any abstract idea into a practical application, e.g. see pgs. 10-11 of Remarks – Examiner disagrees.
Examiner notes that, as shown above, the knowledge graph itself and its construction are interpreted as abstract because, given the broadest reasonable interpretation, creating a knowledge graph recites collecting data, analyzing the data, and displaying certain results of the analysis (i.e. a mental process), and/or following rules or instructions for a user to follow in evaluating a patient. Hence, even assuming, arguendo, that the claimed invention improves the computer’s ability for the specific function of creating a knowledge graph, this represents an improvement to the abstract idea, and an improvement in the abstract idea itself is not an improvement in technology, e.g. see MPEP 2106.05(a)(II).
Additionally, even assuming, arguendo, that the claim limitations recite a specific sequence of data transformations and a specific structure for the knowledge graph, the absence of complete preemption does not guarantee that a claim will be eligible, and preemption is not a stand-alone test for patentability, but rather is inherent in the two-part Alice/Mayo framework, e.g. see MPEP 2106.04. That is, even assuming, arguendo, that the claimed invention recites a particular configuration for an abstract idea, a narrow abstract idea nonetheless recites an abstract idea, and the broadness/narrowness of the abstract idea is not, by itself, dispositive of the eligibility of the claim.
Furthermore, as shown above, Examiner has provided evidence demonstrating that the present invention is directed towards at least one court-identified abstract idea that is not integrated into a practical application, and further that the additional elements of the present invention (i.e. any elements not identified as part of the abstract idea) do not represent significantly more than the abstract idea, and hence has addressed any concerns arising from preemption.
For the aforementioned reasons, Claims 1-20 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed July 13, 2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 103 have been fully considered and, in combination with the claim amendments, are persuasive. The rejections of Claims 1-20 under 35 U.S.C. 103 have been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Shao (US 2023/0377068) – teaches a system that obtains various patient knowledge graphs for times of doctor visits including multiple nodes and edges.
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 JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H CHOI can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN P GO/Primary Examiner, Art Unit 3681
