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 .
Status of Claims
This is a final office action in response to the amendment filed 30 July 2026. Claims 1 and 11 have been amended. Claims 5-6, 15-16, and 18 have been canceled. Claims 1-4, 7-14, 17, and 19-20 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1 and 11 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that according to MPEP 2106.04, independent claims 1 and 11, and their dependent claims, are allowable under Step 2A and/or 2B of the eligibility analysis. Applicant asserts that the claims do not recite an abstract idea, and further that the independent claims recite a specific, computer-implemented machine-learning pipeline that conditions training data based on signal-to-noise characteristics, trains a defined encoder-and classifier architecture using the sanitized data, and deploys the trained model to control downstream execution operations and dynamic graphical interface behavior, in a manner that integrates any alleged abstract idea into a practical application similar to Example 42 and improves the operation of a computer based system. Applicant further asserts that, like BASCOM, the sequence of claim limitations in amended claim 1 are sufficient to amount to significantly more than any alleged judicial exception because the elements in combination are not well-understood, routine, conventional activity in the field. Examiner respectfully disagrees.
Example 42 includes exemplary claim 1, which recites “converting . . . non-standardized updated information into the standardized format,” “storing the standardized updated information,” “automatically generating a message containing the updated information,” and “transmitting the message to all of the users over the computer network in real time, so that each user has immediate access to up-to-date patient information.” The analysis of claim 1 of Example 42 states that the claim as a whole recites a combination of additional elements, including storing information, providing remote access, converting updated information, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users. The analysis of claim 1 in Example 42 further states that the abstract idea is integrated into a practical application because “the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user.” The Federal Circuit held in BASCOM that the claimed Internet content filtering, which featured an implementation “versatile enough that it could be adapted to many different users’ preferences while also installed remotely in a single location,” expressed an inventive concept in “the non-conventional and nongeneric arrangement of known, conventional pieces.” Bascom Glob. Internet Servs., Inc. v. AT & T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016).
The claim limitations herein are unlike those of Example 42 and BASCOM. Here, the recited limitations improve a business decision making process for monitoring the status of a workflow by filtering/ reducing the size of the data input into the machine learning model for tracking process and status of a task, not the functioning of the computer that is used as a tool to implement the recited abstract concept. The amended limitations recite an abstract idea of processing project/task data for monitoring progress and assigning additional instructions to a user for completion of a task or project. The claim limitations fall within the certain methods of organizing human activity abstract concept grouping because the limitations track user completion or progress towards completion of a task based on analysis of user input data, which is a form of managing personal behavior. While the amended claims herein recite limitations for sanitizing tonality training data, training a tonality machine learning model comprising an encoder component for generating a plurality of textual encodings by converting raw, unstructured user input into a plurality of structured numerical representations configured to capture at least contextual and semantic features of the user input and a classifier configured to classify the plurality of textual encodings into a tone classifier, per paragraph [0025] of the Specification “A ‘classifier,’ as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm.” Because the claimed machine learning process is described as a mathematical model in the specification, which is not necessarily rooted in computing technology. Training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. Training machine learning based encoding network and encoding data are generic data processing steps. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a).
As a result, the combined claim limitations do not integrate the recited abstract idea into a practical application and do not amount to significantly more. Therefore, the 35 U.S.C. rejection is proper, maintained, and updated below.
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 1-4, 7, 9-14, 17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites a system and independent claim 11 recites a process for generating user-sensitive operations using artificial intelligence. The claims recite an abstract idea of collecting data, analyzing it, and outputting certain results of the collection and analysis. Independent claims 1 and 11 recite substantially similar limitations.
Taking independent claim 1 as representative, claim 1 recites at least the following limitations:
generate a task module using a matching model, wherein the matching model is configured to:
match governance data with entity data;
and assign the matched governance and entity data to one or more action items;
generate a first set of execution operations as a function of the task module;
generate a graphical user interface, wherein the graphical user interface comprises a plurality of visual elements, wherein each of the plurality of visual elements comprises an execution operation of the first set of execution operations and an input event handler of a plurality of input event handlers;
receive user input corresponding to at least one of the first set of execution operations as a function of one of the plurality of input event handlers;
sanitize tonality training data comprising exemplary user inputs correlated to exemplary user input tones, wherein sanitizing the tonality training data comprises:
determining that at least one training data entry of the tonality training data has a signal to noise ratio below a threshold value; and
removing the at least one training data entry from the tonality training data to create sanitized tonality training data;
train a tonality machine-learning model using the sanitized tonality training data, which increases a training efficiency and predictive accuracy of the tonality machine-learning model, and output tone data which is part of an assigned status, wherein the tonality machine-learning model comprises: an encoder component for generating a plurality of textual encodings as a function of the user input, wherein the encoder component converts raw, unstructured user input into a plurality of structured numerical representations configured to capture at least contextual and semantic features of the user input;
and a classifier configured to classify the plurality of textual encodings into a tone classification;
determine, as a function of the user input, the assigned status corresponding to the first set of execution operations using a machine-learning model including at least a tonality machine-learning model,
and generate a second set of execution operations as a function of the assigned status including the tone data, wherein the tone classification controls generation of the second set of execution operations; and
display, at a display device, the first set of execution operations and the second set of execution operations, wherein the second set of execution operations is configured to update the graphical user interface by at least one of:
dynamically adjusting the graphical user interface based on a status of the execution operations;
tracking progress of the second set of execution operations; and
generating contextual data for subsequent execution operations.
Under Step 1 independent claims 1 and 11 recite at least one step or act, including determining an assigned status corresponding to the first set of execution operations. Thus, the claims fall within one of the statutory categories of invention. See MPEP 2106.03.
Under Step 2A Prong One the limitations recited in claim 1 for generating a task module using a matching model, matching governance data with entity data, assigning the matched data to action items, generating a first set of execution operations, receiving user input, sanitizing tonality training data, determining that at least one training data entry of the tonality training data has a signal to noise ratio below a threshold value, removing the at least one training data entry, training a tonality machine-learning model, determining the assigned status, generating a second set of execution operations, tracking progress of the second set of execution operations, and generating contextual data for subsequent execution operations, as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (collecting, analyzing, and displaying certain results of the collection and analysis).
None of the additional elements preclude the steps from practically being performed in the human mind, or by a human using a pen and paper. A facilities auditor could use a checklist and mentally determine whether the facility (such as the healthcare environment – patient rooms detailed in paragraph [0017] of the Specification, and paragraph [0019] patient feedback and or survey data as input) is in compliance with regulatory and governance protocol, assign action items based on the observations, and determine the status of the assigned tasks. Applicant’s specification at [para. 0041] states: “… in an embodiment, a status machine-learning model 164 may be used to assign and track assigned statuses 156 by incorporating various techniques to analyze and manage data related to compliance regulations, liability metrics, tasks, projects and/or resources. Status machine-learning model 164 may be trained using status training data 168 such as historical data on execution operation progress and status changes. In an embodiment, status features may be defined, such as categorical variables (e.g., assigned, in-progress, completed), time-related features (e.g., time spent in each status, deadlines), and/or user-specific features (e.g., user experience, past performance).” The steps for processing user input status data and classifying the data into the categorical variables exemplified in paragraph [0041] of the Specification for updating a task/project progress visualization and presentation of task/project operations for execution could be performed mentally or through use of a pen and paper as observation, judgments, or evaluations of a task/project based on information from a user. Therefore, the claim limitations fall within the mental processes grouping of abstract ideas.
The limitations recite an abstract idea of processing project/task data for monitoring progress and assigning additional instructions to a user for completion of a task or project. The recited claim limitations fall within the abstract concept grouping of certain methods of organizing human activity because the limitations track user completion or progress towards completion of a task based on analysis of user input data, which is a form of managing personal behavior. Therefore, the limitations fall into the mental processes grouping and certain methods of organizing human activity abstract concept groupings, and accordingly the claims recite an abstract idea.
Under Step 2A Prong Two the judicial exception of claim 1 is not integrated into a practical application. In particular, the claims recite a processor, graphical user interface, machine learning model comprising an encoder component, and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0010] states: “system 100 includes at least a processor 108 and a memory 112 communicatively connected to the at least a processor 108 and containing instructions 116 configuring the at least a processor 108 to generate user-sensitive operations using AI.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations/comparisons, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h).
The limitation for “generating a graphical user interface, wherein the graphical user interface comprises a plurality of visual elements” is broadly and generically claimed and construed as using graphical user interface technology to display processed data, without significantly more. The claim limitations fail to detail any functional improvement or modification to the functioning of the graphical user interface that goes beyond generic data input and output functions, therefore the steps for generating execution operations fail to implement the abstract idea into a practical application.
The claimed matching model, task module, input event handlers, and a machine learning model are additional elements but do not transform the recited abstract idea into eligible subject matter. The matching model and task model are construed as computing modules programmed to perform a data correlation function based on pre-determined business rules, in a manner that could be performed by a human mentally. The input event handler, per paragraph [0037] of the Specification is defined as “functions or methods designed to respond to specific events in a program, particularly in user interface contexts.” An “event” is “an occurrence that is detected by the program. For example, this may include a mouse click, keyboard input, and/or a change in a form field. In an embodiment, event handlers 184 may include a listener and or binding process. A listener is a function that listens for specific events on an element. For instance, a button or an input field.” As defined, the input event handlers are broadly and generically claimed, and as a result are reasonably construed as generic graphical user interface components for receiving user input, without significantly more. The claim lacks sufficient technical details regarding how the claimed input event handlers cause the graphical user interface to perform any functions beyond generic interface functions of receiving, transmitting, and displaying data. As a result, the claimed input event handlers do not transform the recited abstract idea into patent eligible subject matter.
While the claim includes steps for sanitizing tonality training data, and determining an assigned status corresponding to the first set of execution operations using a machine learning model trained, the training of specific data is construed as pre-processing data does not make a claim patent eligible. Further the Specification at [para. 0071] states that sanitization or noise ratio determination could be a mathematical operation. The specification at paragraph [0025-0027] supports generic machine learning. The machine learning models, as claimed, include the application of mathematical models (for example linear regression) that are abstract ideas applied using generic computing technology, and thus cannot provide a practical application of the recited abstract idea. Training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a).
The recited machine-learning model is broadly and generically claimed, such that it is construed as a tool to process the data and generate an output, without significantly more. While the recitation of machine learning models may provide a practical application for a recited judicial exception, there must be an improvement in the application of machine learning technology that goes beyond a general link. The recited claim limitations herein for updating the data used to classify the tone of the user input and determine an assigned status corresponding to a set of execution operations (per Spec. at [para. 0040]: completed, in progress, overdue) is insufficient to transform the recited abstract idea into a practical application. While the claimed machine learning model includes an encoder, which is defined in the Specification as “a component within a machine-learning model that converts raw input data, such as text, into a set of structured numerical representations, known as encodings” and a “classifier may be configured to use the textual encodings to categorize the user response data into various tone classifications (e.g. positive, negative, neutral)” (See Specification [para. 0042]), the trained machine learning model is used as a tool to implement the underlying abstract idea. The model improves the data used for analysis by reducing the dataset, the machine learning model itself is not improved. Further, the output of the data analysis step is not used in a meaningful way that improves or alters the functioning of the graphical user interface or the computer processor.
The claimed “set of execution operations” is broadly and generically claimed to include the display of data. The display of data is insignificant extra solution activity that cannot transform an abstract idea into patent eligible subject matter. The same rules for character analysis may be mentally applied using the human mind to compare phrases or terms and determine the “underlying tone or sentiment of a user.” The limitation for “generating a second set of execution operations as a function of the assigned status including the tone data,” is broadly and generically claimed to include generating a set of steps or “action items” for task completion or business decision making to be performed by a human per the Specification at [para. 0035], which states “’execution operations’ refer to the processes or actions that are carried out to implement a specific function, task, or operation within a system. …Task module 124 may encapsulate the logic needed to perform certain actions, therefore first set of execution operations 140 are automatically generated or defined within task module 124. When system 100 processes a request a specific set of action items may be generated without being manually chosen.” As described in the specification the output of the data analysis could be the presentation of a list of action required to perform a task. This is not a technical solution to a technical problem, nor a practical application of the recited abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea beyond generally linking the abstract idea to generic data processing and visualization technology.
Under Step 2B the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept. See MPEP 2106.05. The focus of Applicant’s invention is not to improve the performance of computers or any underlying technology; instead, the focus is to use generic computer components as a tool to gather and analyze business data to administer, create, or modify task/project progress data based on a user input. The additional elements, when considered individually or in combination, do not amount to significantly more than the recited abstract idea because the additional elements recited in the claims merely improve the analysis and output of known and input data for human visualization and decision-making using machine learning and computer components as data processing tools without technological improvement.
Dependent claims 2-4, 7, 9-14, 17, and 19-20 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process abstract idea by describing how the data is processed, analyzed, and displayed. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, independent claim 11 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore, claims 1-4, 7, 9-14, 17, and 19-20 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gupta et al. (US 11,531,940) - Computing device state or activity based task reminders and automatic tracking of statuses of task-related activities are provided. Users are enabled to create reminders that are triggered based on a device state of the user's device or activity signals from the operating system, an application, or a user file. The status of a task item can be inferred from signals collected from one or more sources. The signals provide information associated with tasks that the user performs in various life events. Machine learning, statistical analysis, behavioral analytics, and data mining techniques are applied to the signals, and the user's activities are mapped to task items that the user has created. An inferred status of a task activity can be shared with other systems, or can be used for a variety of functions (e.g., to automatically update the user's task list, or to remind the user of an uncompleted task item).
Williamson (US 12,572,862) - In a first step, work or tasks may be scheduled based on client-specified input regarding required frequency, durations, time-of-day (TOD) preferences, and any other appropriate descriptors. For example, stores and/or facilities may be provided with a template for their custom scheduling of company-required work/tasks (which could be a template for scheduling on a daily, weekly, and/or monthly basis, as non-limiting examples. In a second step the customized “work/task” assignments created in step 1 may be sent as “push” notifications appropriately as assignments. Assignments may be listed in a “truncated” fashion, with the assumption that assigned workers “know what they are doing”. For example, a push notification may display “CLEAN-Men's Restroom”. The above described guidance button (S-Guidance) may be provided to and displayed for less knowledgeable workers, where tapping the guidance button reveals listing of features, attributes, and/or special instructions. The user can tap the title of the task to reveal the process (e.g. it may not exactly be an “s-guidance” button). In a third step, workers (e.g. facility workers) may conduct work/tasks, and “mark off” or “check off” (i.e. as a task status verification step) work assignments as completed. Each work/task may have a “Start” and “Stop” button which may be tapped or activated appropriately. In some embodiments, a “Done” button may be displayed or provided to a user (e.g. employee or worker).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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