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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 29, 2026 has been entered.
Response to Amendment
The following Office action in response to communications received June 29, 2026. Claims 1-10 and 13-22 are pending, with claims 1, 6, and 13 being independent. Claims 1 and 6 have been amended. Claims 11 and 12 have been cancelled. New claims 13-22 have been added. Therefore, claims 1-5 and 13-22 are pending and addressed below.
Applicant’s amendments to the claims not sufficient to overcome the rejections set forth in the previous office action dated December 29, 2025.
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 1-5 and 13-22 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Step 2A - Judicial exception:
Independent claims 1, 6, and 13 are directed to an abstract idea of managing user preferences and permissions, analyzing historical user-selection information, and generating medical documentation and associated requests based on those preferences. This encompasses mental processes and certain methods of organizing human activity, implemented using generic computer technology.
Independent claim 1 recites, in substance:
A computerized method that tracks user choices and preference information; correlates procedures with diagnoses and patient demographics; generates user profiles, ranked lists, and reordered menus based on historical information; determines whether a user may inherit another person’s preferences based on permission levels; uses either inherited or personal preferences to create medical notes; and electronically requests laboratory tests.
Independent claim 6 recites, in substance:
A computer medical-records system having a user interface, server, adaptive notes-generation module, adaptive learning algorithm, laboratory-request software module, prescription software module, and user database, configured to determine permission to inherit preferences; determine user preferences from frequency correlations; track selections; create medical notes; and electronically request prescriptions and/or laboratory tests.
Independent claim 13 recites, in substance:
A computerized method that receives user credentials, accesses user objects containing pointers to preference and permissions records, determines read and write permission to use another user’s preferences, presents selectable options ranked from historical frequency information, selectively updates user preference information based on trusted or untrusted status, and generates and stores patient notes.
Under the broadest reasonable interpretation, claims 1-10 and 13-22 recite the performance of:
Mental processes, including observing historical selections, evaluating relationships between diagnoses and procedures, judging whether a user is authorized to use another person’s preferences, selecting recommended options, and determining which preference profile should govern future documentation.
Certain methods of organizing human activity, including managing the relationship and permissions between a clinician and assistant or scribe; allocating authority to access, use, or alter another person’s preferences; and managing clinical documentation, prescription, laboratory-request, and billing workflows.
Mathematical concepts, to the extent the claims recite or encompass frequency counting, ranking, correlating procedure selections with diagnoses or demographics, generating priority values, and identifying top-ranked recommendations using frequency-correlated data.
But for the recitation of generic computer components, the claims are directed to the abstract practice of observing a user’s historical choices, determining another person’s preferences and authorization level, applying an authorized preference set to documentation, and updating preference information for future use. The claimed “impression count table,” frequency correlations, rankings, and preference determinations merely organize and evaluate information to select and present options to a user.
Additional elements
The claims additionally recite elements such as: one or more processors; server; computer medical-records system; graphical user interfaces; adaptive notes-generation module; adaptive learning algorithm; database and user database; user objects, preference records, permissions objects, and pointers; laboratory-request, prescription, and billing software modules; electronic storage of patient notes; and electronic transmission of laboratory requests and prescription requests.
These elements are recited at a high level of generality and merely use generic computer components to receive, store, retrieve, correlate, rank, display, update, and transmit information. The recitation of an “adaptive learning algorithm” does not identify a particular technological architecture, training technique, data-processing mechanism, or improvement to computer operation; rather, it is used functionally to analyze preference data and generate ranked options or user profiles.
The specification describes conventional computing infrastructure, including a server, database, client device, network, browser or graphical user interface, and software modules. It expressly identifies conventional client devices such as desktop computers, laptops, tablets, smartphones, and network computers, and describes the modules as receiving input, obtaining data from a database, writing output, generating notes, and communicating requests to laboratory, billing, or prescription functions.
The specification’s impression-count table records user, diagnosis, procedure, and count information; its permissions object maintains read/write privileges between users; and its adaptive module uses stored historical data to prepopulate, rank, or reorder options. These are conventional data-storage, access-control, analysis, and interface-presentation functions performed using generic computer components.
Practical application analysis:
The additional elements do not integrate the judicial exception into a practical application. The claims do not recite a technological improvement to a computer, database, network, user-interface architecture, machine-learning architecture, laboratory system, pharmacy system, or other technological field.
The claimed result—ranked, frequency-ordered prepopulated lists, dynamically reordered menus, inherited preferences, and selectively updated preference records—concerns the content of information displayed to a user and the rules by which information associated with users is accessed or updated. The claims do not recite a specific manner of improving the computer’s processing capability, memory use, communications operation, security architecture, or interface technology itself. Instead, the claims apply abstract preference-management and documentation rules using conventional computing components in the field of electronic medical records.
The laboratory-request, prescription, billing, and patient-record limitations likewise do not provide a practical application. They merely use the results of the abstract preference and documentation process to transmit requests, provide billing information, issue notifications, or store notes. Such data transmission, storage, display, and reporting activities constitute insignificant extra-solution activity when performed by generic computer components.
Dependent claims 2-5 and 7-10 add prompting for the second person’s identity, acceptance of preferences, use of the first user’s preferences if another user’s preferences are not accepted, and read/write preference determinations. These limitations further define abstract permission and preference-management rules and do not supply a technological improvement.
Dependent claims 14-17 and 21-22 add evaluation of prior procedures, extraction of customization data, default selections, priority indicators, diagnosis-to-procedure or billing-code correlations, demographic-based rankings, and top-ranked recommendations. These limitations further collect, analyze, organize, and display information, but do not recite a technical improvement to computing technology.
Dependent claims 18-20 add transmitting prescriptions, providing laboratory or surgical-test requests, and providing billing codes or notifications. These limitations use the results of the abstract idea in conventional medical, pharmacy, laboratory, or billing contexts and do not improve the functioning of the computer system or external systems.
Step 2B - Inventive concept:
The ordered combination of claim elements adds nothing significantly more than the abstract idea itself. The claims use generic processors, servers, databases, memory, interfaces, modules, and electronic communications to perform conventional functions of receiving data, storing data, retrieving records, applying authorization rules, counting or correlating information, ranking options, presenting information, updating records, and transmitting requests.
The claims do not recite a non-conventional arrangement of computer components or a particular technical means that provides an improvement in computer functionality. The claimed modules are invoked according to their intended functions, and the claimed database tables, preference records, user objects, pointers, and permission records are used for their ordinary information-storage and retrieval purposes.
Any additional actions of storing notes, displaying ranked or prepopulated options, prompting a user, sending requests to a laboratory or pharmacy, providing billing codes, and issuing notifications are conventional data-gathering, output, storage, transmission, or post-solution activity.
Accordingly, claims 1-10 and 13-22 are directed to an abstract idea without significantly more and therefore are not patent eligible under 35 U.S.C. § 101.
Examiner notes the following limitations from independent claims 1, 6, and 13 were not expressly taught by the references identified in the prior analysis.
Claim 1
“wherein the adaptive learning algorithm implements an impression count table stored in a database of the computer medical records system, the impression count table recording, on a per-user basis, frequency counts of user selections correlating particular procedures to particular diagnoses and to patient demographics”
“wherein the adaptive notes generation module uses the impression count table to generate ranked, frequency-ordered prepopulated lists and dynamically reordered menus for display on the one or more graphical user interfaces in anticipation of user needs”
“wherein choices made relative to the set of user interfaces are recorded in the impression count table such that a user can inherit such choices as part of their preferences”
“wherein the permission levels define a trusted permission level under which choices made by the user are factored into future preferences of the second person, and an untrusted permission level under which choices made by the user are not factored into future preferences of the second person”
Claim 6
“wherein the adaptive learning algorithm implements an impression count table stored in a database of the computer medical records system, the impression count table recording, on a per-user basis, frequency counts of user selections correlating particular procedures to particular diagnoses”
“wherein the adaptive notes generation software module uses the impression count table to generate ranked, frequency-ordered prepopulated lists of selectable options for display on the graphical user interface in anticipation of user needs, the selectable options being ordered based on the frequency counts stored in the impression count table”
“wherein the permission levels define a write-permitted permission level under which choices made by the user are factored into future preferences of the second person, and a write-restricted permission level under which choices made by the user are not factored into future preferences of the second person”
“wherein choices made relative to the set of user interfaces maybe tracked are recorded in the impression count table such that a user can inherit such choices as part of their preferences”
Claim 13
“a database storing a plurality of user objects, each user object comprising a pointer to a preference record and a pointer to a permissions object, wherein the preference record is generated from an impression count table that stores frequency counts correlating user selections to diagnoses and procedures across a plurality of patient encounters”
“in response to determining the first user has read permission, retrieving the preference record of the second user and generating a graphical user interface screen comprising a visual representation of a patient and data entry fields with selectable options ranked according to frequency-correlated data from the impression count table associated with the second user”
“determining, by the adaptive notes generation module using the permissions object, whether the first user has a write permission relative to the second user, the write permission indicating whether the first user is classified as trusted or untrusted”
“selectively updating the impression count table based on the write permission classification”
“wherein if the first user is classified as trusted, the one or more user selections are factored into the frequency counts of the impression count table associated with the second user to modify future preference-based display for the second user”
“and if the first user is classified as untrusted, the one or more user selections are recorded in the impression count table associated with the first user without modifying the frequency counts associated with the second user”
Response to Arguments
Applicant's arguments, filed on September 29, 2026 with respect to argument in the remarks, have been considered but are moot in view of the new ground(s) of rejection necessitated by the new limitations added to at least Claims 1, 6 and 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub. No.: US 20060184394 A1 to Maughan; A system employs a prediction processor for predicting user demand for a graph in order to create and provide a graph with reduced response time upon user request. A patient medical data graphing system includes a repository for storing patient medical data and an acquisition processor. The acquisition processor acquires medical data concerning a particular patient and stores the acquired data in the repository. A data processor processes the acquired patient medical data derived from the repository to provide graph representative data for storage in a repository, in response to predetermined configuration information indicating a graph is to be pre-stored for the acquired patient medical data. A user interface processor provides data representing a display image including a pre-stored graph in response to user command.
Pub. No.: US 20070198432 A1 to Pitroda et al.; Provided herein are methods and systems for supporting secure electronic transactions, including those that support security at the domain, user and device level.
Pub. No.: US 20090198514 A1 to Rhodes; Paragraph [0059]: Shown in FIG. 2, Sessions List area 2 includes a list of prior data entries relating to treatment sessions of the patient prior to any current windows. FIG. 3 shows this in greater detail, with the lower portion of the window pane as including a list of sessions. Each session is a collection of information about a particular patient visit. The selection of a particular session results in the population of the other window panes of Main UI 104. This allows the practitioner/user to view in one screen a summary of the relevant patient data available electronically, and the practitioner/user may drill down any of the other window panes (as described in greater detail below) to obtain the full data from any of these summaries. Further, the practitioner/user may initiate a new session. When a new session is initiated, the data record or object associated with the new session inherits the information from the immediately prior session, allowing the practitioner/user to avoid having to enter duplicative data, so that only the entry of new data and/or the deletion of old data is required. In the exemplary embodiment, only session information in the current session may be edited and prior session information is available in read-only mode. However, it is possible to configure the software to provide permission-based editing features, so that a user and/or super-user may be able to modify certain prior session data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683
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