DETAILED ACTION
This Office Action is in reply to Applicants response after non-final rejection received on April 30, 2026. Claim(s) 1-20 is/are currently pending in the instant application. The application claims priority to provisional application 62/980,529 filed on February 25, 2020.
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 .
Response to Amendment
The Examiner acknowledges the applicants amendments to claims 1-3, 9-11, and 17-19 in the response on October 9, 2025. No claims are canceled at this time.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system, method, or product and is a method, system, and product for tracking task availability which is a process (Step 1: YES).
The Examiner has identified independent method Claim 9 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 1 and product Claim 17. Claim 9 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
Storing historical driving data in a storage device;
training a machine learning model to identify shifts not expressly defined based on geolocation information and timing information within the historical driving data, wherein a shift comprises a continuous period of time during which a person is devoted to at least one particular task and the historical driving data includes historical task data of a plurality of other users than a first user including geopositional data, timing data, and task data, from a mobile device associated with each of the respective plurality of other users;
identifying a plurality of tasks performed by the first user during a range of time based on changes in live data including one or more of geospatial data and timing data captured via a software application on a mobile device in the first user and determine shift data including a start time and a stop time of the range of time via execution of the trained machine learning model receiving as an input thereto latitude and longitude values in the live data including geospatial data, timing data, and task type data captured via the mobile device of the user associated with identified tasks performed by the user, and amount of time between an end of a first task and a start time of an immediate subsequent task identified from the data of the user, wherein the instance of the end of the first task and the start time of the immediately subsequence task is within a predetermined range of time, the first task and the immediately subsequent task are determined to belong to a same shift, otherwise a new shift is determined to start between the first task and the immediately subsequent task, the identifying including generating a prediction including at least one or more of the start time of the first task and the immediately subsequent task and one or more of an end time of the first task and the immediately subsequent task via execution of the trained machine learning model on the geopositional data and the timing data associated with the plurality of other users;
binning the historical task data of the plurality of other users based on the identified shifts in the shift data into one or more groups corresponding to the identified shifts;
generating a query that includes the one or more predicted start time of the range of time as first query parameter and the stop time of the range of time as a second query parameter;
querying a data store including the shift data based on the generated query to retrieve data records of tasks performed by one or more of the plurality of other users between the one or more predicted start time and the one or more predicted stop time of the range of time;
executing a second machine learning model, based on as an input thereto including data records of tasks performed by one or more of the plurality of other users corresponding to the binned data, to generate a predicted insight for the user with respect to the range of time; and
transmitting a notification including the predicted insight for the first user to a user interface via an application programming interface (API) of the software application on the mobile device of the first user, the predicted insight including one or more recommended tasks to be performed by the first user in at least one shift of a specified range of time and the software application displaying, in response to receiving the transmission of the notification via the API, a visualization of the optimized task work schedule in the user interface of the software application on the mobile device of the first user.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Collecting available time, location, and task data, tracking and identifying task completion with start and stop times, generating insights, and transmitting a notification including an optimized schedule recites a concept performed in the human mind. But for the “machine learning model”, “via the processor”, “computing device”, “second machine learning model”, “a data store”, “user interface” and “API” language, the claim encompasses a person collecting various forms of task data, performing analysis, and output based on location and timing data where task are assigned based on timing, location, and availability and transmitting a notification and displaying a schedule using his/her mind. The mere nominal recitation of generic computer and processor hardware and well known machine learning techniques does not take the claim limitation out of the mental processes grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The computer with a processor in Claim 1 is just applying generic computer components to the recited abstract limitations. The non-transitory computer readable medium comprising instructions which when executed by a processor cause the computer to perform in Claim 17 appears to be just software. Claims 1 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
Additionally, these limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Collecting available time, location, and task data, tracking and identifying task completion with start and stop times, generating insights, and transmitting a notification including an optimized schedule recites a concept performed in the managing personal behaviors or relationships. The mere nominal recitation of generic computer and processor hardware does not take the claim limitation out of the mental processes grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept of managing personal behavior or relationship, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The computer with a processor in Claim 1 is just applying generic computer components to the recited abstract limitations. The non-transitory computer readable medium comprising instructions which when executed by a processor cause the computer to perform in Claim 17 appears to be just software. Claims 1 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a processor with a data store, machine learning models, and user devices with displays and API’s (Claims 1 and 9) and/or non-transitory computer readable medium comprising instructions which when executed by a processor cause the computer to perform and a machine learning model (Claim 17). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 9, and 17 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification [pg. 23, lines 19-30] about implantation using general purpose or special purpose computing devices [The computing system 800 may include a computer system/server, which is
operational with numerous other general purpose or special purpose computing system
environments or configurations. Examples of well-known computing systems,
environments, and/or configurations that may be suitable for use as computing system
800 include, but are not limited to, personal computer systems, server computer systems,
thin clients, thick clients, hand-held or laptop devices, tablets, smart phones, databases, multiprocessor systems, microprocessor-based systems, set top boxes, programmable
consumer electronics, network PCs, minicomputer systems, mainframe computer
systems, distributed cloud computing environments, databases, and the like, which may
include any of the above systems or devices, and the like.] and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 9, and 17 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-8, 10-16, and 18-20 further define the abstract idea that is present in their respective independent claims 1, 9, and 17 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 2-8, 10-16, and 18-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Response to Arguments
The Applicants remarks begin on page 11 of the response on April 30, 2026. The Applicant starts with a summary of the claims and moves to the arguments for the rejection under 35 U.S.C § 101.
The Applicant begins with traversal of the rejection in the Non-Final Office Action dated December 16, 2025. The arguments cites receiving and processing information related to an entity for users of an app. The received information is processed by a machine learning model to generate insights including recommendations of an optimized work task schedule (remarks pages 11-12). The argument points out that two different models are used, the first trained on data associated with a first user, and the second model trained on data collected from mobile device of other users. Further, the argument is that the predicted insight (recommendation) may be presented as a notification on an app related to a shift.
The argument further cites the specification and the first machine learning model using patterns and data associated with a user to identify ranges of time to generate a prediction and the second machine leaning model that uses the range of time as an input to generate a predicted insight where the recommendation is sent to the user mobile device.
The Examiner in not convinced. The training for the model is included at a very high level where the specification states “may use machine learning to train a model based on historic task data including…”. This is the most basic training of a model based on collected data. The details of the training is nonexistent and it’s also the most basic, generic, and necessary training a model needs to create and validate a model. The machine learning is also claimed in a generic manner and cannot be seen as more than use of a computer as a tool to perform the judicial exception. Additionally, the claim does not tie the two machine learning models together as argued. The first model is identifying tasks during a range of time. The second model is taking in data records of tasks performed by other users corresponding to binned data, where the binned data is just filtering or categorization of the historic task data. There is no indication that the first model or any model is doing the binning. Further, the result of the data processing by two models is simply the transmission of a recommendation to a user device. This is not more than sending data over a network for one. For two, the recommendation is simply that, a suggestion which may or may not be followed. The claims does not achieve more than data processing by a computer, collecting known historical data, and utilizing known methods of machine learning. The generic claim to a technological environment does not overcome the judicial exception nor does it integrate the claim into a practical application.
Additionally, as referenced in the previous response to arguments, the position that the information is being digested and then insights and recommendations being provided by machine learning models is insufficient for the reasons as follows. First, only the first model involves the generic necessary training step for the model to perform the desired function. After the training the model seems to only ingest data and produce a result using the same trained function each time. The data being analyzed to is define a “shift” based on location and time data, while a person is performing at least one task. This is simply a data collection and analysis step performed by a computer. This equates to MPEP 2106.05(g)(3) Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis [and display], Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Here the activity is a function or activity the courts have found as insignificant extra solution activity. The use of a generically trained machine learning model is not more than applying to the technological field [or using the computer as a tool to perform the judicial exception]. The same citation is a list listed as a limitation the courts have described as merely indicating a field of use of technological environment in which to apply a judicial exception MPEP 2106.05(h) Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Additionally the predicted insight (recommendation) is displayed on the mobile device which is a display step as the third step of the Electric Power Group citation.
The arguments continue with the Applicants position that the claims are not directed to mental processes or certain methods of organizing human activity. Applicant asserts that the claims are not directed to any abstract idea (remarks page 12) and cites at least claim 1 (remarks pages 12-14) where applicants argue the limitations of claim 1 clarify (see remarks page 15) that the amended limitations are not merely reciting collecting time, location, and tasks data to track and identify task completion with start and stop times to generate insights and transmit notifications. The arguments (remarks page 16) point to “train a machine learning model” on specific data where the trained model receives specific inputs to generate a prediction, binning historical data based on the shift data of one or more groups, query a data store, and execute a second machine learning model based on task data of other users corresponding to binned data for generating predicted insights of a first user and range of time, and transmit a notification to a user interface of an application.
The Examiner is not persuaded. The collection of data for training of a model and use of specific data to provide insight that is transmitted to a user device via an application is applying a computer or technological environment to a judicial exception. The steps involved are examples which have been found by the courts to be well-understood, routing, and conventional activity, or mere instructions to apply the exception, or insignificant extra solution activity. The mere application of generic machine learning with a most basic training step is not sufficient to overcome the rejection. Further, transmitting a suggest or recommendation to a user interface is not more than displaying a result through transmitting data over a network.
The arguments move on to the Applicants assertion that the claims are not simply the same as collecting, processing, and analysis of data as part of Electric Power Group, LLC v. Alstom S.A.. Significantly the claim including training a first machine learning model, generating a prediction by the first mode, binning the historical data, querying a data store, executing a second machine learning model to generate an insight and transmitting the insight. Applicant agues these steps are not equivalent to collecting, analyzing, and displaying data. Applicant argues that each of the two machine learning models generate a particular prediction based on specific inputs. Such operations are not merely collection and analysis of data (Remarks page 16). Arguments also submit that the MLM is not a collection, analysis, or display of data and binning of data is not collection or analysis either. Further, Applicant argues transmitting data is not reasonable characterized as displaying data.
The arguments are not persuasive. First, the collection and use of historical data regardless of geopositional, timing, or task data constitutes collecting data. Binning or grouping of data constitutes analysis of data. The form of analysis is not particular especially when it’s a commonly known or used technique. Sending data to be displayed constitutes displaying of data, regardless of the manner of display. While specific functions such as training machine learning and transmitting data do not fall within the Electric Power Group LLC steps, it does not automatically remote the court recognized functions and activities out of the analysis. Further, the court has established 4 cases in the transmitting and receiving data over a network. Regarding the machine learning, the element is generically claims including the initial and necessary training step. There is not additional or second step of training similar to Example 39 to show improvement to the machine learning. Additionally, the specification is silent to the training aspects of the machine learning other than it’s done with historical data. This is not more than developing an algorithm to identifying timing information. This once trained model is simply used over and over again. This is not indicative of practical application rather use of a computer as a tool to perform the judicial exception. The speed as which this is accomplished is simply provided by the application of a computer to execute the algorithm quicker.
Also, there is no training step for the second machine learning model. It’s simply a pretrained model to take the collected data and output a recommendation or insight. This is not more than use of generic computer and well known machine learning techniques.
Applicants move the argument that the second machine learning model does not have a requirement that the recitation must include data sets to train the model. The arguments are directed to the input(s) and output(s) are sufficient to inform one of skill in the art that the second machine learning is not generic computer execution (remarks page 17).
The Examiner does not agree. The claimed machine learning does not including any additional limitations in the claim regarding training. Further, it does not have specific training claimed in the specification. As disclosed and claimed, at least the second machine learning is generic well understood machine learning that is generating a predicted insight for a first user with respect to a range of time regarding possible tasks available. This does not require a computer or machine learning model to perform, therefore it is simply application of a generic computer and well understood model to perform the steps.
Applicant continues the argument (remarks page 17) with the position that the claims emphasize and highlight features rooted in technology and provide technical solution. Specifically the Applicant argues that it’s a mechanism to determine shift data by the execution of a model and generate a prediction insight for the user with respect to a range of time, by the execution of a second machine learning model. The system of claim 1 transmits a notification including the predicted insight via an application programming interface of a software application.
Applicant further argues that the claims are not merely reciting a solution but recites details of how the solution is accomplished. In the present applicator the argument is the particular manner the first and second machine learning models receive particular inputs to achieve the desired outcome where the software displays a visualization of the optimized task (remarks pages 17-18).
The Examiner does not agree. Determination of shifts and tasks withing a certain time range and determinations for recommending insights and transmitting for display is a technical soliton to a non-technical problem. Assessing available tasks over a range of time is not a technical problem. The solution is using the technological field to provide a solution. The mere application of a computer and one or more models on the computer is not indicative of practical application.
The arguments (remarks page 18) also take the position that the function aspects of software cannot be performed in the mind. Applicant cites the August 4, 2025 memo remingind that mental processes are limited to judgement, evaluation, observation, and opinion. Applicant points specifically to software are reason for the conclusion that software applications cannot be mental processes.
The Examiner disagrees because if the software is performing aspects which a human can perform in their mind and the computer is generically claims then the combination is merely used as a tool to perform the judicial exception. In the current claims, just because the transmitted data uses and API, does not overcome the application of the exception. An API is a data handler so that the communication between two computer elements is handled correctly. Every communication between two different software applications needs an API to handle the data conversion to make sure the data is transferred correctly and does not introduce, destroy, or otherwise alter the data.
The arguments also push back on the certain methods of organizing human activity and submit the actions of collecting available time, location, and task data, tracking and identifying start and stop times, generating insights, and transmitting notifications including an optimized schedule are not consistent with the enumerated aspects of organizing human activity. Applicant argues the claims do not recite fundamental economic practices (remarks page 19), also does not recite risk or payment processing. Further, Applicant argues at least claim 1 does not recite commercial or legal interactions relating to contracts, legal obligations, advertising, or sales activities or behaviors. Applicant further argues the claims do not recite managing personal behavior or relationships including social activities, teaching, and rule following (remarks page 19). Applicant concludes with the position tht the clasim place meaningful limits on the scope of the claimed features and does not monopolize the abstract idea(s).
The Examiner does not agree. The rejection of the claims did not cite fundamental practices, nor did they cite commercial or legal interactions. The claims are searching data to recommend insights for tasks to be performed by a user. These recommendations fall into managing personal behavior or relationships as a user has the option to chose what tasks available they wish to complete. This is managing a persons behavior through the insights gained. Additionally, since this is geared to gig economy then the argument could be made that commercial interactions are occurring. The Applicant is reminded that the sub-groupings of the abstract ideas under each of the three main categories are simply a few examples of each and are not exhaustive lists which the Examiner must fit the claims into. The Examining corps is reminded to stay within the three main groupings.
In summary, the arguments and amendments directed to the rejection under 35 U.S.C. § 101 are not persuasive. The claims still stand rejected and the application is not in condition for allowance.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN C WHITE whose telephone number is (571)272-1406. The examiner can normally be reached M-F 7:30-4:00 EST.
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/DYLAN C WHITE/Primary Examiner, Art Unit 3683 June 23, 2026.
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