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 .
Notice to Applicant
Receipt of Applicant’s Amendment filed June 23, 2026 is acknowledged.
Response to Amendment
Claims 1, 13, 24, 36, and 47-48 have been amended. Claims 2-12, 14-23, 25-35, 37-46, and 49-54 have not been modified. Claims 1-54 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed June 23, 2026 have been fully considered but they are not fully persuasive. A response is provided below.
Applicant argues 35 U.S.C. §101 Rejections, pg. 22 of Remarks:
Regarding 1, Applicant argues that the amended claim limitation of “determining that the first geographic location differs from the address associated with the first patient, [automatically] triggering a healthcare provider system update process” is not abstract as it does not provide a rule or instruction for a medical data specialist to follow and is instead recites technical operations executed by a computing system by automatically triggering an update process. Examiner respectfully disagrees.
The amended claims do not recite the term “automatically”. Even if the claims were amended to recite as such, the use of the term “automatically” does not disqualify the limitation from being categorized as an abstract idea. Language such as automatically or instantly to describe the automation of a manual process is not enough to overcome a subject matter eligibility rejection (MPEP § 2106.05(a)(I) Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality no. (iii) mere automation of manual processes). Examiner also notes that language such as this is not restricted to computer processes, humans can automatically/instantly begin or perform tasks.
Applicant further argues that the examples of “managing personal behavior…” fall under the sub-grouping because they recite rules or instructions for a person to follow, not because they can be followed by a human. Examiner notes that the MPEP makes no such distinction. A human is not required to be present in the claim to fall under the managing personal behavior or relationships or interactions between people sub-grouping. Furthermore, the claims of the instant application may comprise a set of instructions for a medical data specialist to follow to obtain patient data from an outside source, as the broadest reasonable interpretation of the claims are not limited to a particular technological environment.
Under the broadest reasonable interpretation, these actions may be performed by cross-referencing and updating data spreadsheets. As an illustrative example, a non-healthcare data source from “Social Media Source A” may comprise the table below.
Social Media Source A
User
Date
Time
Location where content was uploaded from
Content
Oliver
1/1/2000
09:16
4121 Sunset, St Tacoma, WA 98418
“The mountain is out, but I am too. Broken ankle. Ouch! Headed back to Miami tomorrow.”
Oliver
1/3/2000
15:38
1428 Brickell Ave, Miami, FL 33131
“Ankle still hurts.”
The location data from where the content was uploaded from (4121 Sunset, St Tacoma, WA 98418), which corresponds to the device of the patient that was used to upload to a post to Social Media Source A, may then be used to identify a healthcare provider data repository to execute a query upon to obtain a candidate patient information dataset (a different data spreadsheet from the nearest emergency room, illustrated below).
Tacoma General ER
Patient
Intake date
Intake time
Doctor
Reason
Action
Oliver
1/1/2000
10:05
Smith, John
Broken ankle
Splint
Adrian
1/1/2000
10:15
Smith, John
Wellness checkup
Blood panel
The identified candidate patient information dataset may then be extracted and used to create or update a patient information data set in a second healthcare provider system, which may comprise a third data spreadsheet (updated data extracted from Tacoma General ER highlighted below).
Miami Hospital System
Patient: Oliver
Intake date
Intake time
Doctor
Reason
Action
12/2/1999
11:07
Jones, Chris
Wellness checkup
Blood Panel
1/1/2000
10:05
Smith, Jones
Broken ankle
Splint
1/5/2000
9:01
Jones, Chris
Broken ankle follow up
Cast, physical therapy
Thus, Examiner submits that, under the broadest reasonable interpretation, the claims provide a set of instructions for a medical data specialist to search for patient data in other locations to create or update medical records of a patient in a different institution. Thus, the characterization of the claims as an abstract idea of certain methods of organizing human activity and mental processes is proper.
Regarding 2, Applicant asserts that the amended limitation of “determining that the first geographic location differs from the address associated with the first patient, [automatically] triggering a healthcare provider system update process” cannot be performed mentally, citing Synopsis. However, Synopsis is directed towards “a claim to a specific data encryption method for computer communication involving a several-step manipulation of data, Synopsys., 839 F.3d at 1148, 120 USPQ2d at 1481 (distinguishing the claims in TQP Development, LLC v. Intuit Inc., 2014 WL 651935 (E.D. Tex. 2014))”, and it is conceivable that data encryption with a several-step manipulation of data would not be possible with mental processes. However, fact patterns of Synopsis do not align with the instant application as the “several-step manipulation of data” of the instant application can be performed mentally as demonstrated in the step-by-step explanation below.
Examiner respectfully maintains that a person can perform the steps of the independent claim by:
identifying a first non-healthcare data source associated with a first patient (sift through a folder containing non-healthcare data)
extracting, from the first non-healthcare data source, a non-healthcare dataset comprising a first set of geolocation data corresponding to a mobile device associated with the first patient and personal data associated with the first patient (remove and read file that contains information including a geographic location, such as an address, that is associated with a patient mobile device)
based on the first set of geolocation data, determining a first geographic location corresponding to the device associated with the first patient (making a mental determination of a geographic location, such as a city name or zip code, based on geolocation data)
selecting, from a plurality of healthcare provider data repositories, a first healthcare provider data repository, associated with a first healthcare provider system, for obtaining information associated with the first patient based on the first healthcare provider data repository being associated with the first geographic location corresponding to the mobile device associated with the first patient (choose another folder that contains healthcare information for the patient based on the extracted geographic location)
selectively executing a query on a target subset of healthcare data, from the first healthcare provider data repository, to identify a first candidate patient information dataset corresponding to a first healthcare treatment occurring at a first healthcare facility corresponding to the first geographic location (look in the folder for documents related to the patient)
based at least on the personal data associated with the first patient extracted from the first non- healthcare data source: determining that the first candidate patient information dataset comprises health data associated with the first patient (look at data and make a mental determination that the data is health information associated with the first patient)
based on the first candidate patient information dataset, creating or updating in a second healthcare provider system, a patient information data structure comprising a first patient information dataset comprising the health data associated with the first patient (write up a new document or update an existing document based on the information learned by looking in the other folder)
extracting, from the first non-healthcare data source, a second set of geolocation data corresponding to the mobile device associated with the first patient and personal data associated with the first patient (remove and read a second file that contains information including a geographic location, such as an address, that is associated with a patient mobile device)
determining that a second geographic location associated with the second set of geolocation data corresponds to the address associated with the first patient (making a mental determination as to if the geolocation data is associated with the patient’s address on file)
responsive at least to determining that the second geographic location corresponds to the address associated with the first patient, refraining from executing the healthcare provider system update process with respect to the second geographic location for creating or updating the patient information data structure (not doing an action)
Regarding 3, Applicant argues that the amended claims integrate the judicial exception into a practical application improving technology for healthcare records systems.
Regarding A, Applicant argues that the claimed system enables discovery and obtainment of records from a repository that the system would not otherwise have identified by automatically identifying and retrieving a patient’s healthcare records from a selected data repository using geolocation data corresponding to a patient’s mobile device.
However, this represents the improvement to the abstract idea of creating or updating patient information, which is a human activity typically performed by medical data specialists, and does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). The problem being solved is not a technical problem rooted in a problem with how computing systems function. Rather, it is solving an abstract problem of not knowing where to search for data by providing an indication where desired data might be (geolocation data associated with the patient’s device).
Examiner notes there is a delineation between industrial applicability and a practical application as defined by the MPEP under the 101 analysis.
Although Applicant likens claimed improvement with Desjardins and Enfish, these cases are distinguishable from the instant application.
With regards to Desjardins, the decision is specific to the facts before the Appeals Review Panel and follows the subject matter eligibility analysis set forth in MPEP 2106. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel. The instant application provides no improvements to the field of machine learning as it does not recite any use of machine learning. Thus, the decision of Desjardins is irrelevant to the fact patterns of the instant application.
With regards to Enfish, MPEP 2106.05(a)I recites: “It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691.” This is distinguishable to the instant application, as no such technical improvement to the additional element of the computing system or databases themselves are claimed or supported by Applicant specification. Rather, Applicant specification clarifies that the intent is to the abstract idea of searching for data based on an indication that there may be data elsewhere; [0002] of Applicant specification recites: “Additionally, a patient’s healthcare data associated with different healthcare provider systems may remain unmatched, for example, when one healthcare system is unaware that the patient has additional healthcare data associated with a different system.”
Regarding B, Applicant argues that triggering the execution of the healthcare provider system update process on a determination that geolocation data places the patient’s mobile device at a location that differs from an address associated with the patient improves healthcare data systems, further describing the searching for patient data based on an indication comprising the geolocation data associated with the patient’s mobile device. Examiner maintains that this identification and retrieval process are abstract processes and the usage of computing systems to perform such activities represent mere automation of manual processes, which are insufficient to show improvements to the functioning of a computer or to any other technology or technical field; see MPEP 2106.05(a)I.
With regards to Desjardins and Enfish, Examiner maintains that these are distinguishable from the instant application for the reasons
Applicant further argues that the trigger mechanism of triggering an update process when a geographic location differs from a patient’s address and refraining when the geographic location matches the patient’s address provides an improvement as it is impractical to query every single healthcare provider data repository and systems do not have an independent basis for knowing what repositories to query. However, the described problem is an abstract problem that is not rooted in the technology itself. There is no indication in the claims or Applicant specification that the recited steps of the claim cannot be performed by human means. Providing a trigger mechanism only serves to define the conditions for when and where to perform a query, if a query is to be performed.
Furthermore, claiming the efficiency of performing the steps using computing systems (“impractical to query every single healthcare repository”) is not enough to amount to a practical application via an improvement to computer or technology under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”), and, thus, the combination of the generic computer components do not provide a non-conventional and non-generic arrangement of known, conventional pieces; note this is applied to Step 2B as well as Step 2A Prong 2).
Applicant argues that the instant application is distinguishable from Trading Technologies as the claims do not provide a user interface. Examiner notes that it was only provided an example where an improvement to the abstract idea using generic components was found to be insufficient to show an improvement in computer-functionality. Likewise, the instant application only applies computing systems to improve upon the abstract idea of searching for healthcare data by mere automation of manual processes; MPEP 2106.05(a)I.
Regarding 4, Applicant argues that the claims provide an inventive concept the combination of elements and that the Office fails to make the factual determination required to support a conclusion that the additional elements, in combination, are well-understood, routine, and conventional. Examiner directs Applicant to MPEP 2106.05(I)(A), which notes that “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f))”.
As the only additional elements of the independent claims are the non-transitory computer-readable storage medium and one or more hardware processors, Examiner submits that computing devices are only applied to implement the abstract idea of providing instructions to search for healthcare data based on an indication that a patient was in a location that differs from known locations associated with the patient.
Applicant argues 35 U.S.C. §112 Rejections, pg. 33 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the 35 U.S.C. 112(a) rejection.
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 1-54 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a method and a machine (claims 1-54). Accordingly, claims 1-54 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified product claims 1 and 13 as the claims that represent the claimed invention for analysis, and are similar to method claims 24 and 36, respectively.
Claim 1:
A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising:
identifying a first non-healthcare data source associated with a first patient;
extracting, from the first non-healthcare data source, a non-healthcare dataset comprising a first set of geolocation data corresponding to a mobile device associated with the first patient and personal data associated with the first patient;
determining that a first geographic location associated with the first set of geolocation data differs from an address associated with the first patient;
responsive at least to determining that the first geographic location differs from the address associated with the first patient, triggering a healthcare provider system update process comprising:
selecting, from a plurality of healthcare provider data repositories, a first healthcare provider data repository, associated with a first healthcare provider system, for obtaining information associated with the first patient based on the first healthcare provider data repository being associated with the first geographic location corresponding to the mobile device associated with the first patient;
selectively executing a query on a target subset of healthcare data, from the first healthcare provider data repository, to identify a first candidate patient information dataset corresponding to a first healthcare treatment occurring at a first healthcare facility corresponding to the first geographic location;
based at least on the personal data associated with the first patient extracted from the first non- healthcare data source:
determining that the first candidate patient information dataset comprises health data associated with the first patient;
based on the first candidate patient information dataset, creating or updating in a second healthcare provider system, a patient information data structure comprising a first patient information dataset comprising the health data associated with the first patient;
extracting, from the first non-healthcare data source, a second set of geolocation data corresponding to the mobile device associated with the first patient and personal data associated with the first patient;
determining that a second geographic location associated with the second set of geolocation data corresponds to the address associated with the first patient;
responsive at least to determining that the second geographic location corresponds to the address associated with the first patient, refraining from executing the healthcare provider system update process with respect to the second geographic location for creating or updating the patient information data structure.
These above limitations, under their broadest reasonable interpretation, encompass interpretation, cover performance of the limitation as certain methods of organizing human activity as managing personal behaviors. The claim elements are directed towards identifying non-healthcare data sources, extracting a non-healthcare dataset, selecting a healthcare provider data repository, selectively executing a query on the target subset of data, determining that a dataset comprises health data, creating or updating a patient information data structure from the providers, extracting a second set of data, making a determination based on the second data set, and refraining from creating or updating another patient information data structure, which provides instructions for medical data specialists to follow to not create or update patient records from data located in other healthcare systems.
These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer identifying a data source, extracting a dataset corresponding to a person, executing a query to identify a candidate patient information dataset, creating or updating a patient information data structure, extracting a second set of data, making a determination based on the second data set, and refraining from creating or updating another patient information data structure.
Accordingly, the claim recites at least one abstract idea.
Claim 24 is abstract for similar reasons.
Claim 13:
A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising:
identifying a non-healthcare dataset comprising first set of geolocation data corresponding to a mobile device associated with a first patient and personal data associated with the first patient;
determining that a first geographic location associated with the first set of geolocation data associated with the first patient;
responsive at least to determining that the first geographic location differs from the address associated with the first patient, triggering a healthcare provider system update process comprising:
identifying a first candidate patient information dataset corresponding to a first healthcare provider system associated with the first geographic location;
based at least on the personal data associated with the first patient, computing a match score between the non-healthcare dataset and the first candidate patient information dataset;
responsive to the match score meeting a threshold, determining that the first candidate patient information dataset comprises health data associated with the first patient and selecting the first candidate patient information dataset as a first patient information dataset; and
storing the first patient information dataset in a patient information data structure, wherein the patient information data structure of a second healthcare provider system associates the first patient information dataset with the first patient;
identifying a second non-healthcare dataset comprising a second set of geolocation data corresponding to the mobile device associated with the first patient and personal data associated with the first patient;
determining that a second geographic location associated with the second set of geolocation data corresponds to the address associated with the first patient;
responsive at least to determining that the second geographic location corresponds to the address associated with the first patient, refraining from executing the healthcare provider system update process with respect to the second geographic location for creating or updating the patient information data structure.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity as managing personal behaviors. The claim elements are directed towards identifying data, determining a geographic location, identifying a candidate patient information dataset, computing a match score, selecting a dataset, and storing data if the data matches a candidate patient, identifying a second dataset, making a determination based on the second dataset, and refraining from creating or updating another patient information data structure, which provides instructions for medical data specialists to follow to select patient datasets to store as a healthcare record.
These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass identifying data, determining a geographic location, identifying a candidate patient information dataset, computing a match score, selecting a dataset, storing data, identifying a second information dataset, making a determination based on the second dataset, and refraining from creating or updating another patient information data structure.
Accordingly, the claim recites at least one abstract idea.
Claim 36 is abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements cited in the Claims:
a non-transitory computer-readable storage medium (1,13); one or more hardware processors (1,13,24,36); monitoring service (53)
Any computing devices and their associated components (processor) that would be able to perform the method are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0079] of Applicant specification recites: “the patient information management system 102 may be implemented on one or more digital devices. The term “digital device” generally refers to any hardware device that includes a processor. A digital device may refer to a physical device executing an application or a virtual machine. Examples of digital devices include a computer, a tablet, a laptop, a desktop, a netbook, a server, a web server, a network policy server, a proxy server, a generic machine, a function-specific hardware device, a hardware router, a hardware switch, a hardware firewall, a hardware firewall, a hardware network address translator (NAT), a hardware load balancer, a mainframe, a television, a content receiver, a set-top box, a printer, a mobile handset, a smartphone, a personal digital assistant (PDA), a wireless receiver and/or transmitter, a base station, a communication management device, a router, a switch, a controller, an access point, and/or a client device.” No specific, technical improvements are being made to computing devices as a variety of generic computing devices are simply applied to perform the abstract idea of searching for healthcare data to update patient health records.
The storage devices (non-transitory computer readable medium) are also taught at a high level of generality. [0028] of Applicant specification recites: “The one or more data repositories may include any type of storage unit and/or device (e.g., a file system, database, collection of tables, or any other storage mechanism) for storing data, including multiple different storage units and/or devices.” No specific, technical improvements are being made to storage devices as they are simply used to perform the insignificant extra-solution activity of storing data.
Monitoring services are also applied at a high level of generality. [0142] of Applicant specification recites: “Microservices may provide monitoring services that notify a microservices manager (such as If-This-Then-That (IFTTT), Zapier, or Oracle Self-Service Automation (OSSA)) when trigger events from a set of trigger events exposed to the microservices manager occur.” As commercially available products are simply applied to perform the abstract idea of monitoring, no specific, technical improvements are being made to such technologies.
The device associated with the first patient is not considered an additional element as it only serves to narrow the geolocation data. The claims do not specify the relationship between the geolocation data and the device, as the claim only requires the geolocation data to correspond to a device associated with the first patient. The device itself serves no functional purpose within the scope of the claims.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, 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 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 does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2 and 25: These claims recite wherein the operations further comprise: subsequent to identifying the first candidate patient information dataset, computing a match score between (a) known patient information dataset associated with the first patient and (b) the first candidate patient information dataset; determining that the match score meets a threshold; wherein creating or updating the patient information data structure based on the first candidate patient information dataset is responsive at least to determining that the match score meets the threshold; which teaches an abstract idea of certain methods of organizing human activity and mental processes, as described for claim 13 above.
Claims 3 and 26: These claims recite wherein selecting the target subset of healthcare data corresponding to the first healthcare provider system associated with the first geographic location comprises: extracting digital identity data, corresponding to the first patient, from the non-healthcare dataset corresponding to the first patient; selecting the target subset of healthcare data based on the digital identity data corresponding to the first patient; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data and selecting a target subset of data.
Claims 4 and 27: These claims recite wherein the operations further comprise: identifying a second candidate patient information dataset associated with the first patient, wherein the first candidate patient information dataset corresponds to a first healthcare data source and the second candidate patient information dataset corresponding to a second healthcare data source; and associating the second candidate patient information dataset with the first candidate patient information dataset in the patient information data structure; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of identifying candidate information datasets and associating related datasets.
Claims 5 and 28: These claims recite wherein the operations further comprise: augmenting the second candidate patient information dataset based on at least a portion of the first candidate patient information dataset; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of adding data to a dataset.
Claims 6 and 29: These claims recite wherein the target subset of healthcare data is associated with a first healthcare provider system; and wherein the operations further comprise: identifying a second candidate patient information dataset associated with the first patient, the second candidate patient information dataset corresponding to a second healthcare provider system; and transmitting to the second healthcare provider system, at least one of: (a) an indication of the first candidate patient information dataset having been associated with the first patient; or (b) the first candidate patient information dataset; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors, as a data entry specialist is capable of identifying and transmitting data.
Claims 7 and 30: These claims recite wherein the operations further comprise: identifying a first patient name in the first candidate patient information dataset, wherein the first healthcare provider system corresponds to the first geographic location; identifying a second patient name in the second candidate patient information dataset, wherein the second healthcare provider system corresponds to the second geographic location; determining, based on the non-healthcare dataset, that the first patient name and the second patient name each correspond to the first patient, wherein the first patient name differs from the second patient name; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of identifying patient names and determining that the names correspond to one patient.
Claims 8 and 31: These claims recite wherein selecting the target subset of healthcare data comprises: executing a second query on a set of healthcare data to identify a candidate subset of healthcare data corresponding to the first geographic location; selecting the candidate subset of healthcare data as the target subset of healthcare data; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of executing queries to identify a candidate subset of data and selecting said candidate data subset.
Claims 9 and 32: These claims recite wherein selecting the target subset of healthcare data comprises: extracting digital identity data indicative of the first geographic location from the non-healthcare dataset; executing a second query on a set of healthcare data sources to identify a target subset of healthcare data sources corresponding to the geographic location, the target subset of healthcare data sources comprising at least one healthcare data source; identifying a target set of healthcare data from the target subset of healthcare data sources; and selecting the target subset of healthcare data from the target set of healthcare data; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data, executing queries to identify target data subsets, identifying said target data subset, and selecting data.
Claims 10 and 33: These claims recite wherein the operations further comprise: extracting digital identity data from the non-healthcare dataset, the digital identity data comprising at least one personal data associated with the first patient, and configuring the query based on the at least one personal data; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data and configuring a query.
Claims 11 and 34: These claims recite wherein the query identifies the first candidate patient information dataset and a second candidate patient information dataset, and wherein the operations further comprise: computing a first match score between the non-healthcare dataset and the first candidate patient information dataset, and selecting the first candidate patient information dataset responsive to the first match score meeting a threshold; and computing a second match score between the non-healthcare dataset and the second candidate patient information dataset, and refraining from selecting the second candidate patient information dataset responsive to the second match score falling below the threshold; which teaches an abstract idea of certain methods of organizing human activity and mental processes, as described for claim 13 above.
Claims 12 and 35: These claims recite wherein the operations further comprise: identifying a second non-healthcare dataset associated with the first patient; computing a match score between the second non-healthcare dataset and the first candidate patient information dataset; responsive to the match score meeting a threshold, updating the patient information data structure to indicate that the first candidate patient information dataset comprises patient information corresponding to the first patient; which teaches an abstract idea of certain methods of organizing human activity and mental processes, as described for claim 13 above.
Claims 14 and 37: These claims recite wherein computing the match score between the non-healthcare dataset and the first candidate patient information dataset comprises: extracting digital identity data, corresponding to the first patient, from the non-healthcare dataset corresponding to the first patient; computing the match score between the digital identity data and the first candidate patient information dataset; which teaches an abstract idea of certain methods of organizing human activity and mental processes, as described for claim 13 above.
Claims 15 and 38: These claims recite wherein the operations further comprise: extracting digital identity data, corresponding to the first patient, from the non-healthcare dataset corresponding to the first patient; augmenting the first patient information dataset based on the digital identity data to generate a first augmented patient information dataset; and storing the first augmented patient information dataset in the patient information data structure, wherein the patient information data structure associates the first augmented patient information dataset with the first patient; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data, adding data to a dataset, and storing information. This claim further teaches an insignificant extra-solution activity of storing data.
Claims 16 and 39: These claims recite wherein the operations further comprise: extracting a first set of patient information data from the first patient information dataset; identifying a second patient information dataset associated with the first patient; augmenting the second patient information dataset based on the first set of patient information data to generate a second augmented patient information dataset; and storing the second augmented patient information dataset in the patient information data structure, wherein the patient information data structure associates the second augmented patient information dataset with the first patient; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data, adding data to a dataset, and storing information.
Claims 17 and 40: These claims recite wherein identifying the non-healthcare dataset associated with the first patient comprises: extracting a first set of patient information data from a known patient information dataset associated with the first patient; executing a query on a group of non-healthcare datasets, wherein the query is based on the first set of patient information data; and receiving a first query result identifying a candidate non-healthcare dataset; computing a second match score between the candidate non-healthcare dataset and the known patient information dataset; and responsive to the second match score meeting a second threshold, selecting the candidate non-healthcare dataset as the non-healthcare dataset associated with the first patient; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data, executing a query, receiving a result, computing a match score, and selecting a candidate dataset.
Claims 18 and 41: These claims recite wherein identifying the first candidate patient information dataset comprises: extracting a first set of digital identity data from a known digital identity dataset associated with the first patient; executing a query on a group of healthcare datasets, wherein the query is based on the first set of digital identity data; receiving a query result identifying a subset of healthcare data comprising a plurality of patient information datasets including the first candidate patient information dataset; computing a second match score between the known digital identity dataset and at least some of the plurality of patient information datasets; and selecting the first candidate patient information dataset from the plurality of patient information datasets responsive to the second match score meeting a second threshold with respect to the first candidate patient information dataset; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of extracting data, executing a query, receiving a result, computing a match score, and selecting a candidate dataset.
Claims 19 and 42: These claims recite wherein identifying the first candidate patient information dataset comprises: refraining from selecting as the first candidate patient information dataset, a second candidate patient information dataset from the plurality of patient information datasets responsive to the second match score falling below the second threshold with respect to the second candidate patient information dataset; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of refraining from selecting a dataset.
Claims 20 and 43: These claims recite wherein the operations further comprise: responsive to the match score meeting the threshold, transmitting to at least one healthcare provider system, at least one of: a first indication indicative of the match score meeting the threshold; a second indication indicative of the first patient information dataset having been associated with the first patient, or an augmented patient information dataset associated with the first patient, the augmented patient information dataset having been generated based at least in part on digital identity data extracted from the non-healthcare dataset; which teaches an abstract idea of sharing information.
Claims 21 and 44: These claims recite wherein the operations further comprise: identifying a second candidate patient information dataset; computing a second match score between the non-healthcare dataset associated with the first patient and the second candidate patient information dataset; responsive to the second match score falling below the threshold, creating or updating the patient information data structure to include an indication that the second candidate patient information dataset is unassociated with the first patient; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of identifying a dataset, computing a match score, and creating or updating a patient information data structure.
Claims 22 and 45: These claims recite wherein the operations further comprise: responsive to the second match score falling below the threshold, transmitting to at least one healthcare provider system, at least one of: a first indication indicative of the second match score falling below the threshold; a second indication indicative of the first patient information dataset being unassociated with the first patient, or an augmented patient information dataset unassociated with the first patient; which teaches an abstract idea of sharing information.
Claims 23 and 46: These claims recite wherein the operations further comprise: identifying a second candidate patient information dataset; computing a second match score between the second candidate patient information dataset and the first candidate patient information dataset; responsive to the second match score meeting a second threshold, selecting the second candidate patient information dataset as a second patient information dataset; and storing the second patient information dataset in the patient information data structure, wherein the patient information data structure associates the second patient information dataset with the first patient; which teaches an abstract idea of certain methods of organizing human activity as managing personal behaviors and mental processes, as a data entry specialist is capable of identifying information, computing a match score, selecting a dataset, and storing information.
Claim 47: This claim recites the method further comprising: coordinating, based at least in part on the first patient information dataset, a healthcare treatment for the first patient at a second healthcare facility of the second healthcare provider system associated with the second geographic location, wherein the first geographic location is not associated with the second healthcare provider system; which teaches an abstract idea of coordinating treatment, which is a human activity regularly performed by hospital coordinators for patients.
Claim 48: This claim recites the method further comprising: determining that the first geographic location is different form a known geographic location corresponding to the second healthcare provider system; responsive to determining that the first geographic location is different form the known geographic location: initiating a search for healthcare data at least by selecting the target subset of healthcare data based at least in part on the first geographic location and executing the query on the target subset of healthcare data; which teaches an abstract idea of initiating a search for healthcare data based on a determination, which encompasses an activity performed by data entry specialists and mental processes.
Claim 49: This claim recites the method further comprising: determining that a set of one or more known geographic locations associated the first patient does not include the first geographic location; responsive to determining that the set of one or more known geographic locations does not include the first geographic location: initiating a search for healthcare data at least by selecting the target subset of healthcare data based at least in part on the first geographic location and executing the query on the target subset of healthcare data; which teaches an abstract idea of initiating a search for healthcare data based on a determination, which encompasses an activity performed by data entry specialists and mental processes.
Claim 50: This claim recites wherein extracting the non-healthcare dataset comprising the first geographic location from the first non-healthcare data source comprises: identifying a status update in the first non-healthcare data source; and determining the first geographic location based at least in part on the status update; which teaches an abstract idea of identifying a status update and determining a location based on the update, which encompasses an activity performed by data entry specialists and mental processes.
Claim 51: This claim recites wherein determining the first geographic location based at least in part on the status update comprises: determining that the status update comprises a reference to health data; identifying a candidate geographic location associated with at least one of: the status update or the reference to health data; selecting the candidate geographic location as the first geographic location; which encompasses an abstract idea of determining that the status update comprise health data, identifying candidate geographic locations, and selecting the location, which encompasses an activity performed by data entry specialists and mental processes.
Claim 52: This claim recites wherein extracting the non-healthcare dataset comprising the first geographic location from the first non-healthcare data source comprises: identifying a location notification in the first non-healthcare data source; and determining the first geographic location based at least in part on the location notification; which encompasses an abstract idea of determining location, which encompasses an activity performed by data entry specialists and mental processes.
Claim 53: This claim recites wherein extracting the non-healthcare dataset comprising the first geographic location from the first non-healthcare data source comprises: receiving a notification from a monitoring service indicative of a status update to a social media platform; responsive to receiving the notification, executing a query on the first non-healthcare data source based on one or more items of personal data associated with the first patient; receiving the non-healthcare dataset in response to the query; which teaches monitoring services at a high level of generality such that they are only applied to perform the abstract idea of monitoring a patient’s information as well as executing the query based on a notification.
Claim 54: This claim recites wherein extracting the non-healthcare dataset from the first non-healthcare data source comprises: receiving, from the first non-healthcare data source, a data transmission comprising the non-healthcare dataset, wherein the data transmission is initiated by the first non- healthcare data source; wherein the operations further comprise: responsive to receiving the data transmission initiated by the first non-healthcare data source: determining the first geographic location corresponding to the mobile device associated with the first patient; determining that the first healthcare provider system is unrelated to the second healthcare provider system; responsive to determining that the first healthcare provider system is unrelated to the second healthcare provider system: selecting the first healthcare provider data repository comprising the target subset of healthcare data and selectively executing the query on the target subset of healthcare data; which teaches an abstract idea of certain methods of organizing human activity, by selecting healthcare provider repositories to query for healthcare data which is abstract of the same reasons as claim 1 above, and mental processes, such as making determinations and selections.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-12, 14-23, 25-35, and 37-54, additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-12, 14-23, 25-35, and 37-54, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-12, 14-23, 25-35, and 37-54, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-54 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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