DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/12/2026 has been entered.
Status of Claims
This action is in reply to an amendment filed on 02/12/2026. Claims 1-5, 14, 16, 18-22, 28, and 29 have been amended. No claims have been added or cancelled. Claim 30 has been withdrawn. Therefore, claims 1-29 are currently pending and have been examined.
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 1-29 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a method and system which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea of:
A method for improving accuracy and optimizing storage of patient data for a patient with a medical condition and/or illness, the method comprising: accessing an initial set of data records associated with the patient, the initial set of data records including information regarding the patient, the patient’s illness, and/or the patient’s treatment; extracting a plurality of candidate facts from the accessed initial set of data records, each candidate fact represented as a data set; categorizing each candidate fact as corresponding to an element of a plurality of elements associated with the patient, the plurality of candidate facts including more than one candidate fact corresponding to the element for at least one element in the plurality of elements; for elements that are unchanging over time, identifying in the plurality of candidate facts accessed from the initial set of data records at least one best fact corresponding to each element, the identifying including: where the element has only one corresponding candidate fact, identifying the corresponding candidate fact as the best fact corresponding to the element; and where the element has at least two corresponding candidate facts, identifying at least one of the corresponding candidate facts for the element as the best fact for the element based on reduction rules specific to the element; for each element that can change over time, associating each candidate fact in the plurality of candidate facts accessed from the initial set of data records corresponding to the element with progression period corresponding to a diagnosis or progression milestone; for each element that can change over time, identifying at least one best fact in the plurality of candidate facts accessed from the initial set of data records for each progression period having an associated candidate fact for the element, the identifying including: where the element has only one corresponding candidate fact associated with the progression period, identifying the corresponding candidate fact as the best fact corresponding to the element for the progression period; and where the element has at least two corresponding candidate facts associated with the progression period, identifying at least one best fact corresponding to the element for the progression period from the at least two corresponding candidate facts based on reduction rules specific to the element; responsive to the identifying of the at least one best fact associated with the patient, generating time-series data based on at least one best fact associated with the patient by, for each particular best fact: determining a relative-elapsed-time value for the particular best fact based upon an amount of time elapsed from a start time of the particular best fact, and indexing the particular best fact for the time-series data according to the start time and the relative-elapsed time value of the particular best fact, wherein the at least one best fact associated with the patient comprises a reduced amount of facts from the plurality of candidate facts; receiving an indication of a suggested best fact as a rejected best fact and an acceptance of at least one suggested best fact as an accepted best fact; and responsive to receiving the indication of the rejection and the acceptance, omitting the rejected best fact from the time-series data, wherein the time-series data includes each accepted best fact.
The claims, as illustrated by Claim 1, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite identifying and generating time-series best facts from a patient’s record. Identifying and generating time-series best facts from a patient’s record is a process that merely organizes human activity, as it involves following rules and instructions to identify and generate time-series best facts. It also involves an interaction between a person and a computer. Interaction between a person and computer qualifies as interaction under certain methods of organizing human activity. See MPEP 2106.04(a)(2)(II). For example, the specification discloses that “the graphical user interface enables a user to accept, verify, or identify progressions thereby defining progression periods, and select, accept or verify facts that should represent the progression period for NA assignment, i.e., select, accept or verify the best facts. In some embodiments, progression time ranges (e.g., progression periods) are suggested and facts are bucketed into those windows before suggesting a "best" fact per type, per progression with suggestions noted with computer icon. This process is referred to as enrichment herein. In some embodiments, the user has the ability to override suggestions for some or all of the element and for some or all of the progressions.” See specification, ¶ 00180. As such, the claims recite an abstract idea within the category of certain methods of organizing human activity.
The dependent claims 2-17 and 19-29 recite further abstract concepts such as 2 wherein, for at least some of the elements that are unchanging over time, identifying the at least one best fact corresponding to the element further comprises: presenting the at least one best fact as a suggested at least one best fact corresponding to the element to a user; receiving one or more of: the acceptance of the suggested at least one best fact; an identification of at least one other candidate fact that is not a suggested best fact as at least one best fact; and the rejection of the suggested at least one best fact; and where a rejection of the suggested at least one best fact is received, no longer identifying the suggested at least one best fact as a best fact corresponding to the element, where an acceptance of the suggested at least one best fact is received, identifying the at least one best fact an accepted best fact; and where an identification of at least one other candidate fact that is not a suggested best fact as the at least one best fact is received, identifying the at least one other candidate fact as the at least one accepted best fact; 3 wherein, for at least some of the elements that can change over time, identifying at least one best fact for each progression period having an associated candidate fact for the element further comprises: presenting the at least one best fact for the progression period as a suggested at least one best fact corresponding to the element; receiving one or more of: the acceptance of the suggested at least one best fact as at least one best fact; an identification of at least one other candidate fact that is not a suggested best fact as at least one best fact; and a rejection of the suggested at least one best fact; and where a rejection of the suggested at least one best fact is received, no longer identifying the suggest at least one best fact as a best fact corresponding to the element; 4 generating a progression output based on the at least one best fact associated with the patient; 5 wherein the progression output includes the best facts stored in associated concept tables, each concept table including a progression track identifier and a patient identifier; or wherein the time-series data includes the best facts stored in associated concept tables, each associated concept table indexed according to the start time associated with the best fact in the associated concept table; 6 determining, based on at least some of the candidate facts, one or more progression periods, each progression period corresponding to a period of time beginning at diagnosis or at a progression of the medical condition or illness and ending at a next progression, at the present time, or at death; and assigning each candidate fact to a progression period; 7 presenting the determined one or more progression periods to a user as suggested progression periods; receiving input from a user including one or more of: an acceptance of at least one of the one or more suggested progression periods; an adjustment of a start time or an end time of at least one of the one or more suggested progression periods; an addition of a new progression period; or merging of at least some of the one or more of the suggested progression periods in to a single progression time period; and adjusting the one or more progression periods based on the received input, wherein each candidate fact is assigned to a progression time period after the adjusting; 8 wherein the progressions correspond to one or more of: a physician’s identification that the patient’s disease or condition has progressed; a measured growth of a tumor of the patient; an indication that the patient’s disease has spread and become metastatic; an indication that the patient’s disease or medical condition has not responded to a course of treatment and a physician has decided to switch to a different course of treatment; or an indication that the patient has experienced a relapse in disease or the medical condition; 9 wherein, for each element that can change over time, the associating of each candidate fact corresponding to the element with a progression period is based on time windowing; 10 accessing a new Set of data records; extracting additional candidate facts, each of the additional candidate facts corresponding to an element of the plurality of elements associated with the patient; and determining one or more best facts corresponding to the each element of the plurality of elements based on the plurality of candidate facts extracted from the initial set of data records and the additional candidate facts extracted from the new set of data records; 11 de-duplicating the plurality of candidate facts by, for each element in the plurality of elements, removing each duplicative candidate fact; 12 deriving a candidate fact for at least one element of the plurality of elements associated with the patient based on one or more of the candidate facts extracted from the data and one or more medical rules; 13 wherein, for at least one of the elements, the reduction rules include one or more of: a rule to identify at least one candidate fact as the best fact corresponding to an element based the at least one candidate fact including the most amount of data as compared to other candidate facts corresponding to the same element; a rule to discard a candidate fact that is duplicative of and identical to another candidate fact corresponding to an element for a progression period; and a rule to identify a candidate fact as a best fact based, at least in part, on the candidate fact being the most frequently occurring as compared to other candidate facts corresponding to the same element; 14 for at least one progression period, generating a nodal address for the progression period for the patient; 15 providing predetermined treatment plan information to a health care provider of the patient for facilitation of treatment decisions, the predetermined treatment plan information based on the nodal address for the progression period assigned to the patient; 16 determining a prognosis-related expected outcome with respect to occurrence of a defined end point event for the patient based on the nodal address for the progression period assigned to the patient; 17 wherein the nodal address is a refined nodal address.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claims 1 and 18:
data repositories
computing system
Dependent claims 2, 3, 7, 19 and 24:
graphical user interface
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The data repositories, computing system, and graphical user interface are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to improved data repositories, computing system, and graphical user interface. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract best fact identification and presentation process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract best facts identification and presentation process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, 2 wherein, for at least some of the elements that are unchanging over time, identifying the at least one best fact corresponding to the element further comprises: presenting the at least one best fact as a suggested at least one best fact corresponding to the element to a user; receiving one or more of: an acceptance of the suggested at least one best fact; an identification of at least one other candidate fact that is not a suggested best fact as at least one best fact; and a rejection of the suggested at least one best fact as a best fact; and where a rejection of the suggested at least one best fact is received, no longer identifying the suggested at least one best fact as a best fact corresponding to the element, where an acceptance of the suggested at least one best fact is received, identifying the at least one best fact an accepted best fact; where an identification of at least one other candidate fact that is not a suggested best fact as the at least one best fact is received, identifying the at least one other candidate fact as the at least one accepted best fact; and wherein outputting data regarding the best facts associated with the patient comprises outputting data regarding the accepted best facts associated with the patient; 3 wherein, for at least some of the elements that can change over time, identifying at least one best fact for each progression period having an associated candidate fact for the element further comprises: presenting the at least one best fact for the progression period as a suggested at least one best fact corresponding to the element; receiving one or more of: an acceptance of the suggested at least one best fact as at least one best fact; an identification of at least one other candidate fact that is not a suggested best fact as at least one best fact; and a rejection of the suggested at least one best fact as a best fact; and where a rejection of the suggested at least one best fact is received, no longer identifying the suggest at least one best fact as a best fact corresponding to the element; 4 generating a progression output based on the at least one best fact associated with the patient; 6 determining, based on at least some of the candidate facts, one or more progression periods, each progression period corresponding to a period of time beginning at diagnosis or at a progression of the medical condition or illness and ending at a next progression, at the present time, or at death; and assigning each candidate fact to a progression period; 7 presenting the determined one or more progression periods to a user as suggested progression periods; receiving input from a user including one or more of: an acceptance of at least one of the one or more suggested progression periods; an adjustment of a start time or an end time of at least one of the one or more suggested progression periods; an addition of a new progression period; or merging of at least some of the one or more of the suggested progression periods in to a single progression time period; and adjusting the one or more progression periods based on the received input, wherein each candidate fact is assigned to a progression time period after the adjusting; 10 accessing a new Set of data records; extracting additional candidate facts, each of the additional candidate facts corresponding to an element of the plurality of elements associated with the patient; and determining one or more best facts corresponding to the each element of the plurality of elements based on the plurality of candidate facts extracted from the initial set of data records and the additional candidate facts extracted from the new set of data records; 11 de-duplicating the plurality of candidate facts by, for each element in the plurality of elements, removing each duplicative candidate fact; 12 deriving a candidate fact for at least one element of the plurality of elements associated with the patient based on one or more of the candidate facts extracted from the data and one or more medical rules; 13 wherein, for at least one of the elements, the reduction rules include one or more of: a rule to identify at least one candidate fact as the best fact corresponding to an element based the at least one candidate fact including the most amount of data as compared to other candidate facts corresponding to the same element; a rule to discard a candidate fact that is duplicative of and identical to another candidate fact corresponding to an element for a progression period; and a rule to identify a candidate fact as a best fact based, at least in part, on the candidate fact being the most frequently occurring as compared to other candidate facts corresponding to the same element; 14 for at least one progression period, generating a nodal address for the progression period for the patient; 15 providing predetermined treatment plan information to a health care provider of the patient for facilitation of treatment decisions, the predetermined treatment plan information based on the nodal address for the progression period assigned to the patient; 16 determining a prognosis-related expected outcome with respect to occurrence of the defined end point event for the patient based on the nodal address for the progression period assigned to the patient; 17 wherein the nodal address is a refined nodal address are directed to the abstract idea of organizing human activity where the additional elements are merely used as a tool to perform the abstract idea. Furthermore, the limitations 5 wherein the progression output includes the best facts stored in associated concept tables, each concept table including a progression track identifier and a patient identifier; or wherein the time-series data includes the best facts stored in associated concept tables, each associated concept table indexed according to the start time associated with the best fact in the associated concept table; 8 wherein the progressions correspond to one or more of: a physician’s identification that the patient’s disease or condition has progressed; a measured growth of a tumor of the patient; an indication that the patient’s disease has spread and become metastatic; an indication that the patient’s disease or medical condition has not responded to a course of treatment and a physician has decided to switch to a different course of treatment; or an indication that the patient has experienced a relapse in disease or the medical condition; 9 wherein, for each element that can change over time, the associating of each candidate fact corresponding to the element with a progression period is based on time windowing; 13 wherein, for at least one of the elements, the reduction rules include one or more of: a rule to identify at least one candidate fact as the best fact corresponding to an element based the at least one candidate fact including the most amount of data as compared to other candidate facts corresponding to the same element; a rule to discard a candidate fact that is duplicative of and identical to another candidate fact corresponding to an element for a progression period; and a rule to identify a candidate fact as a best fact based, at least in part, on the candidate fact being the most frequently occurring as compared to other candidate facts corresponding to the same element; 17 wherein the nodal address is a refined nodal address are merely serving to further narrow the abstract idea above. The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. As such, the additional elements do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention. Therefore, the claims are 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 1-6, 8, 10, 12, 18-21, 23, 25, 27, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Yegnanarayanan (US 2014/0164023 A1) in view of Shklarski, et al. (US 2017/0039341 A1) in further view of Brooks, et al. (US 2020/0005916 A1).
With regards to claims 1 and 18, Yegnanarayanan teaches a method and system for improving accuracy and optimizing storage of patient data for a patient with a medical condition and/or illness (Abstract – medical fact extraction may include extracting a first set of one or more medical facts from a first portion of text documenting a patient encounter), the method comprising: one or more data repositories (para [0044] - database); a computing system in communication with the one or more data repositories and configured to execute instructions that when executed cause the computing system to (para [0194] – the processor 1410 may execute one or more instructions stored in one or more computer- readable storage media (e.g., volatile storage 1420), which may serve as tangible, non-transitory computer-readable storage media storing instructions for execution by the processor); accessing an initial set of data records associated with the patient, the initial set of data records including information regarding the patient, the patient’s illness, and/or the patient’s treatment (para [0044]-[0045], [0061] – electronic medical record that generally is maintained by a specific healthcare institution and contains data documenting the care that a specific patient has received from that institution over time; access to the patient's past medical history within and/or external to the healthcare institution, for example in the form of an electronic medical record and/or past clinical documents relating to the patient's care at the institution and/or elsewhere); extracting a plurality of candidate facts from the accessed initial set of data records, each candidate fact represented as a data set (para [0050]-[0051] - one or more medical facts (e.g., clinical facts) may be automatically extracted from the free-form narration (in audio or textual form) or from a pre-processed data representation of the free-form narration using a fact extraction component); categorizing each candidate fact as corresponding to an element of a plurality of elements associated with the patient, the plurality of candidate facts including more than one candidate fact corresponding to the element for at least one element in the plurality of elements (para [0050]-[0051] – the medical facts to be extracted may be defined by a set of fact categories (also referred to herein as "fact types” or "entity types")); for elements…, identifying at least one best fact corresponding to each element, the identifying including: where the element has only one corresponding candidate fact, identifying the corresponding candidate fact as the best fact corresponding to the element (para [0062], [0119], [0174]-[0175] – and the statistical model may come up with multiple alternative hypotheses for a single fact to be extracted; fact review component 106 may generate alerts for contraindications related to a combination of a medication with an allergy, a medication with a diagnosis, a medication with a patient's age or gender, a medication with a condition indicated in the patient's history, a medical procedure with any of the foregoing characteristics, or any other combination of a planned treatment with another clinical fact from the current patient encounter or from the patient's history for which the planned treatment is known to be contraindicated; para [0072] – delete a fact that was extracted from a first portion of the narrative text, and the system may then automatically delete one or more other instances of the same extracted fact ( or of similar extracted facts) from one or more other portions of the narrative text); and where the element has at least two corresponding candidate facts, identifying at least one of the corresponding candidate facts for the element as the best fact for the element based on reduction rules specific to the element (para [0056], [0062], [0119], [0174]-[0175] – two or more of the original facts, when appearing in combination, may imply an additional fact; disambiguate between multiple facts tentatively extracted by the fact extraction component; fact review component 106 may generate alerts for contraindications related to a combination of a medication with an allergy, a medication with a diagnosis, a medication with a patient's age or gender, a medication with a condition indicated in the patient's history, a medical procedure with any of the foregoing characteristics, or any other combination of a planned treatment with another clinical fact from the current patient encounter or from the patient's history for which the planned treatment is known to be contraindicated; para [0008] - narrowing down a hypothesis [reduced amount of facts] from a plurality of alternative hypotheses for a medical fact extracted from a portion of text documenting a patient encounter; para [0072] – delete a fact that was extracted from a first portion of the narrative text, and the system may then automatically delete one or more other instances of the same extracted fact ( or of similar extracted facts) from one or more other portions of the narrative text); …responsive to the identifying of the at least one best fact associated with the patient, generating, by the computing system, …data based on at least one best fact associated with the patient (para [0121], [0126] – the single output hypothesis may correspond to the concept linked in the ontology to the term that is most similar to the token(s) in the text from which the fact is extracted; outputting only the best (e.g., most probable) hypothesis; para [0008] - narrowing down a hypothesis [reduced amount of facts] from a plurality of alternative hypotheses for a medical fact extracted from a portion of text documenting a patient encounter; para [0072] – delete a fact that was extracted from a first portion of the narrative text, and the system may then automatically delete one or more other instances of the same extracted fact ( or of similar extracted facts) from one or more other portions of the narrative text), wherein the at least one best fact associated with the patient comprises a reduced amount of facts from the plurality of candidate facts (para [0008] - narrowing down a hypothesis [reduced amount of facts] from a plurality of alternative hypotheses for a medical fact extracted from a portion of text documenting a patient encounter; para [0072] – facts are deleted [reduced amount of facts]); receiving, by the computing system, an indication of a suggested best fact as a rejected best fact and an acceptance of at least one suggested best fact as an accepted best fact; and responsive to receiving the indication of the rejection and the acceptance, omitting, by the computing system, the rejected best fact from the time-series data, wherein the time-series data includes each accepted best fact (para [0056], [0063], [0065], [0073], [0167], [0178] – original facts are replaced, deleted, or disambiguated from by a user selecting a fact [accepted best fact] to replace, delete, and/or disambiguate another fact [rejected best fact], interpreted as omitting rejected best fact).
Yegnanarayanan does not explicitly teach …that are unchanging over time; for each element that can change over time, associating each candidate fact corresponding to the element with progression period corresponding to a diagnosis or progression milestone; for each element that can change over time, identifying at least one best fact for each progression period having an associated candidate fact for the element, the identifying including: where the element has only one corresponding candidate fact associated with the progression period, identifying the corresponding candidate fact as the best fact corresponding to the element for the progression period; and where the element has at least two corresponding candidate facts associated with the progression period, identifying at least one best fact corresponding to the element for the progression period from the at least two corresponding candidate facts based on reduction rules specific to the element; …time-series …by, for each particular best fact: determining a relative-elapsed-time value for the particular best fact based upon an amount of time elapsed from a start time of the particular best fact, and indexing the particular best fact for the time-series data according to the start time and the relative-elapsed time value against an amount of time elapsed since the start time of the particular best fact.
Shklarski teaches …that are unchanging over time; for each element that can change over time, associating each candidate fact corresponding to the element with progression period corresponding to a diagnosis or progression milestone; for each element that can change over time, identifying at least one best fact for each progression period having an associated candidate fact for the element, the identifying including: where the element has only one corresponding candidate fact associated with the progression period, identifying the corresponding candidate fact as the best fact corresponding to the element for the progression period; and where the element has at least two corresponding candidate facts associated with the progression period, identifying at least one best fact corresponding to the element for the progression period from the at least two corresponding candidate facts based on reduction rules specific to the element (para [0003], [0037]-[0040], [0045] - obtaining, by a computer system, a data model, the data model identifying a type of fact that can be determined from the one or more unstructured documents: project coordinator can identify if the type of facts in the unstructured document that pertain to a field or fields in longitudinal data, that is, data that tracks the same variables over a period of time; progress of a single patient overtime; Facts that are unlikely to change are not collected over multiple time periods, in contrast, longitudinal facts collect only the changed or marginal facts about that patient are collected for the given time period). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to identify facts for elements that are unchanging over time and facts for elements that are changing over time for a progression period as taught by Shklarski to develop a method that can identify the best facts for elements that are unchanging over time and facts for elements that are changing over time since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Shklarski, ¶ 0057).
Brooks teaches …time-series …by, for each particular best fact: determining a relative-elapsed-time value for the particular best fact based upon an amount of time elapsed from a start time of the particular best fact, and indexing the particular best fact for the time-series data according to the start time and the relative-elapsed time value against an amount of time elapsed since the start time of the particular best fact (figures 2-6, each patient problem [best fact] is indexed separately in a timeline including the onset date [start time] and the elapsed time of the problem or the date it was resolved). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to list a timeline of patient treatment facts of Brooks to develop a method that can identify the a timeline of best facts from patient treatments since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Brooks, ¶ 0006).
With regards to claims 2 and 19, Yegnanarayanan teaches identifying the at least one best fact corresponding to the element further comprises: presenting the at least one best fact as a suggested at least one best fact corresponding to the element to a user via a graphical user interface; receiving one or more of: the acceptance of the suggested at least one best fact; an identification of at least one other candidate fact that is not a suggested best fact as at least one best fact (para [0062]-[0063], [0176]-[0178] – score the alternative hypotheses based on probability, confidence, or any other suitable measure of an estimated likelihood that each alternative accurately represents an intended semantic meaning of the portion of text from which it is to be extracted; receive user input to disambiguate between multiple facts tentatively extracted by the fact extraction component; present to the user a certain number of the alternative hypotheses having high estimated likelihood scores; the user may choose one of the options to specify which fact should actually be extracted from the free-form narration; updating of the text narrative may be performed in response to any type of user selection of an option provided by the fact review system); and a rejection of the suggested at least one best fact; and where a rejection of the suggested at least one best fact is received, no longer identifying the suggested at least one best fact as a best fact corresponding to the element, where an acceptance of the suggested at least one best fact is received, identifying the at least one best fact an accepted best fact (para [0062]-[0063], [0159]-[0160] – when the user makes a selection of a fact presented through a structured choice provided by the fact review system, the set of facts extracted by the fact extraction component may be updated accordingly; fact extraction component 104 may learn from the user's correction and may apply it to other texts and/or to other portions of the same text); where an identification of at least one other candidate fact that is not a suggested best fact as the at least one best fact is received, identifying the at least one other candidate fact as the at least one accepted best fact (para [0062], [0121], [0126], [0159]-[0160]).
Shklarski teaches identifying facts for elements that are unchanging over time and are changing over time and for each element that can change over time, associating each candidate fact corresponding to the element with progression period corresponding to a diagnosis or progression milestone (para [0003], [0037]-[0040], [0045]). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to identify facts for elements that are unchanging over time and facts for elements that are changing over time for a progression period as taught by Shklarski to develop a method that can identify the best facts for elements that are unchanging over time and facts for elements that are changing over time since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Shklarski, ¶ 0057).
With regards to claims 3 and 20, Yegnanarayanan teaches for at least some of the elements, identifying at least one best fact further comprises: presenting the at least one best fact as a suggested at least one best fact corresponding to the element; receiving one or more of: an acceptance of the suggested at least one best fact as at least one best fact; an identification of at least one other candidate fact that is not a suggested best fact as at least one best fact; and a rejection of the suggested at least one best fact as a best fact (para [0062]-[0063], [0176]-[0178]); and where a rejection of the suggested at least one best fact is received, no longer identifying the suggest at least one best fact as a best fact corresponding to the element (para [0062]-[0063], [0159]-[0160]).
Shklarski teaches identifying facts for elements that are unchanging over time and are changing over time and for each element that can change over time, associating each candidate fact corresponding to the element with progression period corresponding to a diagnosis or progression milestone (para [0003], [0037]-[0040], [0045]). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to identify facts for elements that are unchanging over time and facts for elements that are changing over time for a progression period as taught by Shklarski to develop a method that can identify the best facts for elements that are unchanging over time and facts for elements that are changing over time since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Shklarski, ¶ 0057).
With regards to claims 4 and 21, Brooks teaches generating, by the computing system, a progression output based on the at least one best fact associated with the patient (figures 2-6, para [0051]-[0052], diagnoses are extracted from raw patient data [best fact] and a timeline [progression output] is generated for a particular diagnosis). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to list a timeline of patient treatment facts of Brooks to develop a method that can identify the a timeline of best facts from patient treatments since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Brooks, ¶ 0006).
With regards to claims 5 and 22, Brooks teaches the progression output includes the best facts stored in associated concept tables, each concept table including a progression track identifier and a patient identifier; or wherein the time-series data includes the best facts stored in associated concept tables, each associated concept table indexed according to the start time associated with the best fact in the associated concept table (figures 2-6, each patient problem [concept] is indexed separately with a timeline treatment with facts about the treatment and the onset date). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to list a timeline of patient treatment facts of Brooks to develop a method that can identify the a timeline of best facts from patient treatments since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Brooks, ¶ 0006).
With regards to claims 6 and 23, Shklarski teaches determining, based on at least some of the candidate facts, one or more progression periods, each progression period corresponding to a period of time beginning at diagnosis or at a progression of the medical condition or illness and ending at a next progression, at the present time, or at death; and assigning each candidate fact to a progression period. (para [0038] – tracks the progress of a single patient over time). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to identify facts for elements that are unchanging over time and facts for elements that are changing over time for a progression period as taught by Shklarski to develop a method that can identify the best facts for elements that are unchanging over time and facts for elements that are changing over time since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Shklarski, ¶ 0057).
With regards to claims 8 and 25, Shklarski teaches the progressions correspond to one or more of: a physician’s identification that the patient’s disease or condition has progressed; a measured growth of a tumor of the patient; an indication that the patient’s disease has spread and become metastatic; an indication that the patient’s disease or medical condition has not responded to a course of treatment and a physician has decided to switch to a different course of treatment; or an indication that the patient has experienced a relapse in disease or the medical condition (para [0037]-[0038] – same variables over a period of time (e.g., tumor size)). It would have been obvious to one of ordinary skill in the art to combine the method taught by Yegnanarayanan to identify the best fact with the method to identify facts for elements that are unchanging over time and facts for elements that are changing over time for a progression period as taught by Shklarski to develop a method that can identify the best facts for elements that are unchanging over time and facts for elements that are changing over time since doing so would improve the efficiency of the system (Yegnanarayanan, ¶ 0048; Shklarski, ¶ 0057).
With regards to claims 10 and 27, Yegnanarayanan teaches accessing a new set of data records; extracting additional candidate facts, each of the additional candidate facts corresponding to an element of the plurality of elements associated with the patient (para [0056]-[0057] – maybe determined that an additional fact may possibly be ascertained from the patient encounter, and that the additional fact would add specificity to the set of clinical facts already collected from the patient encounter); and determining one or more best facts corresponding to the each element of the plurality of elements based on the plurality of candidate facts extracted from the initial set of data records and the additional candidate facts extracted from the new set of data records (para [0056]-[0057], [0145]-[0146] – two or more of the original facts, when appearing in combination, may imply an additional fact, and documenting the additional fact may increase the specificity of the record of the patient encounter; social history fact category 340, fact 342 indicates that patient 122 currently smokes cigarettes with a frequency of one pack per day. Fact 344 indicates that patient 122 currently occasionally drinks alcohol. Indicator 343 indicates that fact 342 was extracted from the words "He smokes one pack per day” in the "Social history" section of the text narrative; and indicator 345 indicates that fact 344 was extracted from the words "Drinks occasionally”).
With regards to claims 12 and 28, Yegnanarayanan teaches deriving a candidate fact for at least one element of the plurality of elements associated with the patient based on one or more of the candidate facts extracted from the data and one or more medical rules (para [0055], [0168] – set of deterministic rules may specify that certain extracted facts, certain combinations of extracted facts, certain combinations of extracted facts and terms in the free-form narration).
Claims 7, 9, 14-17, 22, 24, 26 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Yegnanarayanan (US 2014/0164023 A1) in view of Shklarski, et al. (US 2017/0039341 A1) in further view of Brooks, et al. (US 2020/0005916 A1) in further view of Heywood, et al. (US 2009/0144089 A1).
With regards to claims 7 and 24, neither Yegnanarayanan, Shklarski or Brooks teaches presenting the determined one or more progression periods to a user via a graphical user interface as suggested progression periods; receiving input from a user including one or more of: an acceptance of at least one of the one or more suggested progression periods; an adjustment of a start time or an end time of at least one of the one or more suggested progression periods; an addition of a new progression period; or merging of at least some of the one or more of the suggested progression periods in to a single progression time period; and adjusting the one or more progression periods based on the received input, wherein each candidate fact is assigned to a progression time period after the adjusting. Heywood teaches presenting the determined one or more progression periods to a user via a graphical user interface as suggested progression periods; receiving input from a user including one or more of: an acceptance of at least one of the one or more suggested progression periods; an adjustment of a start time or an end time of at least one of the one or more suggested progression periods; an addition of a new progression period; or merging of at least some of the one or more of the suggested progression periods in to a single progression time period; and adjusting the one or more progression periods based on the received input, wherein each candidate fact is assigned to a progression time period after the adjusting (para [0008], [0014], [0068]-[0072] – Time can be depicted on the x-axis of the chart. The time interval can be one selected from the group consisting of: 24 hours, 1 week, and 1 month. Obtaining a record of at least one medical condition metric over a predetermined time interval, obtaining a record of interventions over the same predetermined time interval, illustrating the record of said medical condition metric and the record of said intervention over a selected time interval that encompasses at least part of said predetermined time interval in a graphic display, and permitting said patient to manipulate said graphic display and the data contained therein to modify the data and determine correlations between such data). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Heywood since doing so would a user to efficiently track the facts over the time period (Heywood, ¶ 0008-0009).
With regards to claims 9 and 26, neither Yegnanarayanan, Shklarski or Brooks teaches for each element that can change over time, the associating of each candidate fact corresponding to the element with a progression period is based on time windowing. Heywood teaches for each element that can change over time, the associating of each candidate fact corresponding to the element with a progression period is based on time windowing (para [0008] – Time can be depicted on the x-axis of the chart. The time interval can be one selected from the group consisting of: 24 hours, 1 week, and 1 month). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Heywood since doing so would a user to efficiently track the facts over the time period (Heywood, ¶ 0008-0009).
With regards to claims 14 and 29, neither Yegnanarayanan, Shklarski or Brooks teaches for at least one progression period, generating a nodal address for the progression period for the patient. Heywood teaches for at least one progression period, generating a nodal address for the progression period for the patient (Fig. 3 and 7; para [0089]-[0090] – the patient can indicate when one or more drugs 714a-714e are administered by placing markers 712 (which may depict pills) on a time scale). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Heywood since doing so would a user to efficiently track the facts over the time period (Heywood, ¶ 0008-0009).
With regards to claim 15, Heywood teaches providing predetermined treatment plan information to a health care provider of the patient for facilitation of treatment decisions, the predetermined treatment plan information based on the nodal address for the progression period assigned to the patient (para [0025], [0076]-[0078] – The treatment graphical element can include treatment dosage, treatment name, or treatment frequency. The method using the graphical element can further include the steps of providing a list of treatment graphical elements, selecting one or more treatment graphical elements from the list, dragging the selection, and dropping the selection at a location representing the time the one or more treatments was administered). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Heywood since doing so would a user to efficiently track the facts over the time period (Heywood, ¶ 0008-0009).
With regards to claim 16, Heywood teaches determining a prognosis-related expected outcome with respect to occurrence of a defined end point event for the patient based on the nodal address for the progression period assigned to the patient (para [0021], [0070]-[0071] – the step of storing can include storing the patient profile and displaying the medical outcome correlation in the graphical element). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Heywood since doing so would a user to efficiently track the facts over the time period (Heywood, ¶ 0008-0009).
With regards to claim 17, Heywood teaches the nodal address is a refined nodal address (Fig. 3 and 7; para [0089]-[0090] – the patient can indicate when one or more drugs 714a-714e are administered by placing markers 712 (which may depict pills) on a time scale). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Heywood since doing so would a user to efficiently track the facts over the time period (Heywood, ¶ 0008-0009).
Claims 11, 13, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Yegnanarayanan (US 2014/0164023 A1) in view of Shklarski, et al. (US 2017/0039341 A1) in further view of Brooks, et al. (US 2020/0005916 A1) in further view of Doan, et al. (US 2017/0286441 A1).
With regards to claims 11 and 28, neither Yegnanarayanan, Shklarski or Brooks teaches de-duplicating the plurality of candidate facts by, for each element in the plurality of elements, removing each duplicative candidate fact. Doan teaches de-duplicating the plurality of candidate facts by, for each element in the plurality of elements, removing each duplicative candidate fact (para [0035] – perform de-duplication on the third object to generate a combined group of duplicate set). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Doan since doing so would improve the efficiency of the system (Doan, ¶ 0005).
With regards to claim 13, neither Yegnanarayanan, Shklarski or Brooks teaches for at least one of the elements, the reduction rules include one or more of: a rule to identify at least one candidate fact as the best fact corresponding to an element based the at least one candidate fact including the most amount of data as compared to other candidate facts corresponding to the same element; a rule to discard a candidate fact that is duplicative of and identical to another candidate fact corresponding to an element for a progression period; and a rule to identify a candidate fact as a best fact based, at least in part, on the candidate fact being the most frequently occurring as compared to other candidate facts corresponding to the same element. Doan teaches for at least one of the elements, the reduction rules include one or more of: a rule to identify at least one candidate fact as the best fact corresponding to an element based the at least one candidate fact including the most amount of data as compared to other candidate facts corresponding to the same element; a rule to discard a candidate fact that is duplicative of and identical to another candidate fact corresponding to an element for a progression period; and a rule to identify a candidate fact as a best fact based, at least in part, on the candidate fact being the most frequently occurring as compared to other candidate facts corresponding to the same element (para [0035] – perform de- duplication on the third object to generate a combined group of duplicate set). It would have been obvious to one of ordinary skill in the art to modify the combination of Yegnanarayanan, Shklarski and Brooks with the features of Doan since doing so would improve the efficiency of the system (Doan, ¶ 0005).
Response to Arguments
Applicant's arguments with respect to the 35 USC § 101 rejections set forth in the previous office action have been considered, but are not persuasive. In an effort to advance prosecution, the Examiner has provided a response to applicant's arguments. Applicant argues:
Applicant’s limitations are subject matter eligible because they “reflect the ‘improvement described in’ the Specification over existing systems …[by] leveraging reduction rules and verification loops to improve accuracy of and optimize storage of patient data”.
In response to Applicant’s argument, the limitations are subject matter eligible because they “reflect the ‘improvement described in’ the Specification over existing systems …[by] leveraging reduction rules and verification loops to improve accuracy of and optimize storage of patient data”, the Examiner respectfully disagrees. The instant application uses rules, such as prioritizing pathologically determined patient data over clinically determined patient data, and retaining patient data that has the most complete detail over patient data that is not totally complete, to narrow multiple candidate patient data down to the best facts about the patient. According to the Applicant, using conventional computer components, such as data repositories, a computing system, a graphical user interface, and these aforementioned rules to process the patient data leads to improved accuracy and optimized storage of the data because “relying on unguided human interpretation to determine what data is best or most accurate complicates the data output process, introduces additional sources of error, and can result in different standards being applied to data from different patients, and different types of data being stored for different patient.” See Specification, ¶ 0002. First of all, the specification does not integrate the data reduction process into a practical application because it does not contemplate optimizing storage of data to improve the functioning of the a computer. It merely reduces data down to the “best facts” using rules. Secondly, processing data using conventional computer components in a manner more efficient than a human could do to save time and reduce errors does not amount to significantly more as “the inability for the human mind to perform each claim step does not alone confer patentability. As we have explained, ‘the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.’ Bancorp Servs., 687 F.3d at 1278.” FairWarning IP, LLC v. Iatric Systems, _ F.3d _, 120 U.S.P.Q.2d 1293 (Fed. Cir. 2016).
Applicant's arguments with respect to the 35 USC § 103 rejections set forth in the previous office action have been considered, but are not persuasive. In an effort to advance prosecution, the Examiner has provided a response to applicant's arguments. Applicant argues:
Yegnanarayanan’s hypothesis does not correspond to the claimed reduced amount of facts or “best facts” and the claimed time-series data, and would not improve accuracy.
Combining Yegnanarayanan with Brooks would result in in multiple different timelines being generated for each hypothesis exacerbating data storage and patient data accuracy problems.
Shklarski is silent with respect to generating a time-series based on identified "best fact[s]" as the claims recite.
In response to Applicant’s argument Yegnanarayanan’s hypothesis does not correspond to the claimed reduced amount of facts or “best facts” and the claimed time-series data, and would not improve accuracy, the Examiner respectfully disagrees. First of all, Yegnanarayanan is not used to explicitly disclose “time-series” data. Yegnanarayanan contemplates “time-series” data by discussing longitudinal medical records that span multiple observations or treatments over time (¶ 0005). Furthermore, one of ordinary skill in the art could reasonably conclude that Yegnanarayanan involves time-series patient records as these records typically involve data about a patient over several dates of encounters. However, as shown above, Brooks, not Yegnanarayanan, is used to explicitly recite time-series patient data. Secondly, Yegnanarayanan teaches narrowing down a hypothesis [reduced amount of facts] from a plurality of alternative hypotheses for a medical fact extracted from a portion of text documenting a patient encounter (¶ 0008), and further teaches deleting a fact that was extracted from a first portion of the narrative text, and the system may then automatically deleting one or more other instances of the same extracted fact ( or of similar extracted facts) from one or more other portions of the narrative text (¶ 0072). Additionally, Yegnanarayanan teaches improving accuracy of the hypothesis by scoring each alternative hypothesis based on probability, confidence, or any other suitable measure of an estimated likelihood that each alternative accurately represents the intended semantic meaning of the portion of text from which it is to be extracted (¶ 0062).
In response to Applicant’s argument combining Yegnanarayanan with Brooks would result in in multiple different timelines being generated for each hypothesis exacerbating data storage and patient data accuracy problems, the Examiner respectfully disagrees. Yegnanarayanan teaches each hypothesis involves a diagnosis/condition with the “best facts” for that diagnosis/condition (¶ 0063, 0105, 0167, 0169, 0172). Brooks discloses a timeline is generated for a particular diagnosis/condition, with each timeline provided together in one user interface to show diagnosis/conditions that are ongoing and diagnosis/conditions that have ended (figures 2-6, ¶ 0051-0052). Therefore, the combination of Yegnanarayanan/Brooks does not exacerbate data storage and patient data accuracy problems because it narrows the timelines based on diagnosis/condition and provides an concise accounting of the “best facts” involved with those particular diagnosis/condition timelines in one easy to access user interface.
In response to Applicant’s argument Shklarski is silent with respect to generating a time-series based on identified "best fact[s]" as the claims recite, the Examiner respectfully disagrees. Shklarski teaches obtaining, by a computer system, a data model, the data model identifying a type of fact that can be determined from the one or more unstructured documents: project coordinator can identify if the type of facts in the unstructured document that pertain to a field or fields in longitudinal data, that is, data that tracks the same variables over a period of time; progress of a single patient overtime; Facts that are unlikely to change are not collected over multiple time periods, in contrast, longitudinal facts collect only the changed or marginal facts about that patient are collected for the given time period (¶ 0003, 0037-0040, 0045).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Olivera, et al. (US 2019/0287660 A1) which discloses techniques herein relate to generating and applying subject event timelines for various purposes. In various embodiments, data indicative of a plurality of medical events associated with a subject may be retrieved (502) from data sources (102-110). For example, natural language processing may be performed (504) on a narrative data record to extract concept(s) associated with a first medical event. Each of the plurality of medical events may be associated (508) with a respective timestamp. Based on the plurality of medical events and associated timestamps, a timeline data structure associated with the subject may be assembled (510) and used to render a visual timeline (232) indicative of the plurality of medical events on a display. Each respective medical event of the plurality of medical events may be represented by a graphical element (234) that is operable to cause additional information (236) about the respective medical event to be output.
Higgins, et al. (US 2014/0092095 A1) which discloses a system for displaying time-based events on a time line is described. A first timeline unit (1) displays a first timeline showing a first plurality of events within a first time segment (3) bounded by a first begin time and a first end time. A second timeline unit (2) displays a second timeline showing a second plurality of events within a second time segment (4) bounded by a second begin time and a second end time, wherein the first timeline and the second timeline are displayed in the same scale. An interaction unit (5) enables a user to indicate a change to the displayed time segments (3, 4). A time segment updater (6) determines an updated first time segment (3) and an updated second time segment (4) based on the indicated change, keeping the scale of the first timeline equal to the scale of the second timeline, and keeping an offset between the first time segment (3) and the second time segment (4) constant. The timeline units (1, 2) are arranged for updating their respective displays according to the updated time segments (3, 4).
X. Zhu, S. Gold, A. Lai, G. Hripcsak and J. J. Cimino, "Using Timeline Displays to Improve Medication Reconciliation," 2009 International Conference on eHealth, Telemedicine, and Social Medicine, Cancun, Mexico, 2009, pp. 1-6, doi: 10.1109/eTELEMED.2009.20 which discloses exploring approaches for integrating and visualizing time-oriented medication data in narrative and structured formats and to address related issues on handling temporal abstraction, granularity, and uncertainty. The ultimate goal is to improve medication reconciliation by providing clinicians with more accurate medication information in patient care. Methods: An event taxonomy was generated to capture different combinations of clinical and temporal uncertainties. A prototype of a temporal visualization system was implemented using an open source software package called Timeline. Medications were parsed and mapped to the event taxonomy, and then represented in Timelines. Seventy-five medications from narrative discharge summary reports and seventy-nine medications from structured orders were used as data input for temporal visualization. Five physicians served as domain experts and answered ten proof-of-concept survey questions. Results: Overall positive feedback from experts suggested the potential value of the proposed timeline visualization method. Challenges were also identified, and future work will include reconciliation of medications from various sources based on temporal attributes and medication classification.
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/JOSEPH D BURGESS/Primary Examiner, Art Unit 3681