DETAILED ACTION
This Office Action is in response to Applicants Request for Continued Examination received on October 9, 2025. 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, 9, and 17 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 working;
collecting data associated with a plurality of other users including goepositional 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 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 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 predicting 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;
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 stop time of the range of time;
executing a second machine learning model receiving as an input thereto the data records of tasks performed by the one or more of the plurality of other users 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 user to a user interface via an application programming interface (API) of the software application on the mobile device of the user, the predicted insight including one or more recommended tasks to be performed by the 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 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, 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 October 9, 2025. The Applicant starts with a summary of the claims and moves to the rejection under 35 U.S.C § 101.
The Applicant begins with traversal of the rejection in the Final Office Action dated April 22, 2025. The arguments cites the receiving and processing, in specific manners, information related to a an entity for users of an application on a mobile device to be processed by a machine learning model to generate insight (a recommendation) of optimized task work schedule. Further, the Applicant argues that in some instances the predicted insight may be generated using two different machine learning models, a first trained on data associated with particular use and a second trained on data collected form mobile devices of a plurality of other users. The predicted insight may present information displayed via the app on the mobile device regarding job opportunities accomplished at higher value during a range of time.
Specifically the Applicants points to the first machine learning model analyzing patterns and data associated with a user to identify a range of time the user is working and the second machine learning model uses that range of time while the second machine learning model uses the range of time to analyze data of other users. The results generated by the first and second models generate predicted insight for the user based on a huge amount of data (remarks page 12). The arguments further include that the data is collected and evaluated as individuals and communities of workers to make sense of the data, and “shifts” may be identified based on timing information related to tasks performed by the user. The predetermined time period identified by the model is based on carious factors such as location of the shift information, and type of job performed. Ultimately the data analysis is used to provide insight and recommendations.
The Examiner is not persuaded by the argument. 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. The same citation is a list listed as a limitation the coufts 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.
Second, the second machine learning model is not trained on any particular data an is a model with an input to produce an output. This is simply executed by a computer. It’s not expressly clear that the output of the first model is provided as an input to the second model which is defined as having an input of data records of other users. Additionally, neither the trained or second machine learning models are clear to have any sort of actual learning function. While the specification discloses the training of the first model, neither one has any sort of learning beyond an initial and necessary training to develop the model. There is no update or feedback in which the model produces a refined or upgraded/updated version which improves the result.
Third, the analysis of the data from two models is to provide one or more “insights” which are not more than recommendations to the user based on the analyzed data. This mere display of data relating to recommendations related to jobs or tasks available is seen as Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); under MPEP 2106.05(f)(2) which are examples the courts have established as additional elements to be mere instructions to apply an exception, because they do no more than invoke computers or machinery as tools to perform an existing process.
Lastly, the assertion of a huge volume of data to be processed is not persuasive as the amount of data available and the time to analyze said data is not indicative of practical application and results in mere use of a computer as a tool for handling the large volume of data more quickly.
The Arguments move to citation of the specification (remarks pages 12-13) where the conclusion is that application relates to a technical problem of organizing and processing data by an app on a mobile device to generate insights to the user. The Applicant submits the claims are not directed to an abstract idea (either mental processes or methods of organizing human activity). In particular, the applicant cites claim 1 (remarks page 14-15) and argues the amendments clarify various aspects of a computing system and recites limitations (remarks page 15) including “identifying a plurality of tasks…” and “determining shift data including start and stop times…” The Applicant points to the specification (Remarks page 16) for the use of machine learning to train a model based on historical task data of other users including geolocation information, driving information, timing information, and source of work to generate a predictive model capable of retrieving live data such as that captured by mobile devices and predict start and stop times of each shift. Also, Applicants cite where a processor determines tasks performed in a first shift and a second shift based on the timing information.
Additionally, Applicant cites claim amendments where a second machine learning model receiving as inputs, the data records or tasks performed by one or more other users and transmit a notification including the predicted insight for the user via an interface of the application on the mobile device (remarks page 17). Applicant argues that the amended claim recites a specific manner to solve a technical problem.
The Examiner is not convinced. The claim limitations are not a technical solution to a technical problem, rather a technical solution to a business problem of optimizing time for one or more tasks to increase earnings. This can be a mental process or organizing tasks relative to available work time. A commercial interaction related to optimizing and maximizing income for available working hours, and managing personal behavior for directing a user to task to be performed. Additionally, the mere use of an application to manage the display of job listings is mere use of the technological field and results in displaying information.
Regarding the machine learning limitations, the claims and further the disclosure include training a model based on historical task data of other users including geolocation information, driving information, timing information, source of work, to generate a predictive model. Predictive models are simply mathematical algorithms executed on a computer. In this case the disclosure does not provide support for any kind of actual learning. The model is trained the first and necessary time to train and validate that the model produced desirable and correct data. In this case there is no feedback, update, adjustment, or retraining of models based on additional collected data over time. This is the basis for machine learning where the model has some form of update. See Example 39, where the neural network is further trained with new data added to the dataset of images which were incorrectly identified. This produced a further trained model with a better recognition model. The instant application does not have this kind of update of either model.
The Applicants further argue that the pending claims do not merely recite the idea of a solution or outcome, instead actually “recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome”. Citing the August 4th Memo, the application claims recite first and second machine learning models receiving particular inputs to achieve a desired outcome of transmitting a notification including predicted insight for the user to a user interface via an application programming interface (API) of the software application on the mobile device. Applicants argue the software is displaying a visualization of the optimized task (remarks page 19-20).
Furthermore, the argument is that aspects of claim 1 cannot be performed of the human mind. Applicant reminds the Office that limitation of the mind include observation, judgement, evaluation, and opinion (remarks page 20).
Additionally, the argument also submits that the claims are not certain methods of organizing human activity and points to the rejection under 35 U.S.C § 101 where collecting data, tracking task completion, outputting data, and transmitting notification including a schedule is not consistent with the enumerated groupings of certain methods of organizing human activity. The Applicant goes through each grouping and each of the listed position in the subgroupings (remarks page 21).
The Examiner does not agree. The argument of machine learning models receiving particular inputs to output a display via an API is not persuasive. The machine learning models do not include the characteristic of actual leaning that is common with machine learning limitations. Based on the specification, the models are generic and basic one time trained models which constitutes a simple model, a series of algorithms, executed on a computer. Without any kind of actual update or feedback to improve the model to produce better outcomes, the claimed machine learning is not providing any learning step, rather they are simply computer models or programs. Additionally, simply transmitting a notification, recommendation, or insight to a display is equivalent to the third step of Electric Power Group with collecting available data and analyzing it, which is current claims follow the same steps. Calling it a visualization is not sufficient for integration into practical application or as significantly more because it’s just applied to a display of a mobile device. Both elements which are well know and in this cause used in their ordinary capacity. Further, the claim of use of an API is not found to be significantly more as it’s a generic API is being applied in it’s intended ordinary manner. API’s are used all the time to handle data to make sure it’s being provided correctly between devices and software applications.
The Claim limitations definitely include concepts which are grouped into the mental processes category. Additionally the claims fall into both managing personal behavior or relationships as the insight or recommendation is directing a user to potential work or tasks to be completed. Additionally, based on the disclosure the method us used to provide recommendations for a user to make more money which would put the claims into the commercial interaction grouping.
Applicants conclude the arguments with a reminder to be careful in consideration to distinguish claims that recite an exception from claims that involve an exception. Applicants state that the claims do not recite a judicial exception. Further, the Applicants position is that the claims are integrated into a practical application and the office is oversimplifying the claims limitations to “apply it”. Applicant further notes that claims are meaningful limitations and that claim is at least is a practical application.
The Examiner is not convinced. The claims follow the collect, analyze, and display data framework as outlined in Electric Power Group. The inclusion of a mobile device and generic models executed on the data to provide insights or recommendations to a user is not integrated into a practical application as the computer is merely used as a tool to perform the abstract idea.
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 at this time.
Conclusion
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 December 10, 2025.