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 .
DETAILED ACTION
Status of Claims
This action is a Final action on the merits in response to the application filed on 10/03/2025.
Claims 1-20 are currently amended. Claims 1 – 20 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
In the remarks Applicant argues, “Extracting this depth of features to create an aggregate feature set in order to enable a deep learning model to accurately estimate and output a time until availability of a task requires real time processing of dense sets of information that could not be practically performed in the human mind, and therefore the claim is not abstract.” (pg. 12)
Examiner notes the claims encompass Certain Methods of Organizing Human Activities related to managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components (e.g. a processor). For example, receiving an indication of picker interest in being assigned a task, predicting task availability and a predicted gap in a geographic area of a picker to determine task availability is related to managing personal behavior. Here, generic computer components are utilized to implement human activity, task availability for a picker.
Additionally, the claims could be seen as Mental Processes (although not claimed) related to observation and evaluation of information. Based on MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer.” Here, the limitations are reflective of mental steps that reasonably could be performed using the human mind or pen/paper such as generic computer components (e.g. a processor and memory) are performing generic computer functions such as receiving an indication of picker interest, generating a prediction of task availability includes steps such as a level of task availability, extracting an aggregate feature set comprising a first feature setoff level of picker availability, accessing logged data, computing aggregate gap measures, grouping the aggregate gap measures, identifying the geographic region of the picker, predicting a gap for the geographic region, a second feature set, inputting an aggregate feature set into a deep learning model (complex mathematics), receiving an estimate of time until availability of a first task to the picker from a retailer location involve collecting and analyzing data.
Applicant argues, “Similar to example 37, under a prong one analysis, the claims are not abstract as they output a display of the estimated time based on activity by a processor and deep learning model that cannot be practically performed in the human mind.” (pg. 13)
Examiner respectfully disagrees. In Example 37, the additional elements recite a specific manner of automatically moving and displaying icons to the picker based on usage which provides a specific improvement, resulting in an improved picker interface for electronic devices. Unlike Example 37 the instance claims are directed to generic computer components performing generic computer functions such as receiving an indication of picker interest, generating a prediction of task availability includes steps such as a level of task availability, extracting an aggregate feature set comprising a first feature setoff level of picker availability, accessing logged data, computing aggregate gap measures, grouping the aggregate gap measures, identifying the geographic region of the picker, predicting a gap for the geographic region, a second feature set, inputting an aggregate feature set into a deep learning model (complex mathematics), etc. The claims demonstrate abstract concepts related to Certain Methods of Human Activity and Mental Processes and do not provide for an improvement in a technology or a technical field.
Applicant argues, “Under a prong two analysis, the claims integrate what is claimed into a practical application by reciting a specific manner of outputting the estimated time until availability of a task in connection with an option to be eligible to be assigned tasks based on underlying usage of pickers in the picker's location, thereby resulting in an improved picker interface.” (pg. 12)
Examiner respectfully disagrees. The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of a computer system comprising a processor, a computer-readable medium, a non-transitory computer readable storage medium and a client device and a picker interface. These are generic computer components recited at a high level of generality as performing generic computer functions (see ¶0012).
For instance, the step of receiving a picker interest in being assigned a task involves data input (data gathering functionality). The step of generating a prediction of task availability includes steps such as a level of task availability, extracting an aggregate feature set comprising a first feature setoff level of picker availability, accessing logged data, computing aggregate gap measures, grouping the aggregate gap measures, identifying the geographic region of the picker, predicting a gap for the geographic region, a second feature set, inputting an aggregate feature set into a deep learning model (complex mathematics), receiving an estimate of time until availability of a first task to the picker from a retailer location involve collecting and analyzing data (data gathering activities). The step of causing the client device of the picker to display the prediction of task availability involves generic display functionality. The step of outputting for display to a picker a picker interface comprising the estimate of time until availability is outputting a result of analyzing data and the step of selecting an interactive element that renders the picker eligible is merely selecting an element on the screen which is generic display functionality.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Applicant’s arguments, see Remarks pgs. 13 - 14, filed 10/03/2025, with respect to Luong et al. have been fully considered and are persuasive. The 35 U.S.C 103 rejection has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112, (b)/second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 recites, “…a second feature set comprising historical picker activities of the picker; inputting the aggregate feature set into a deep learning model; …” at lines 32-34. It is unclear how the second feature set is being utilized as the aggregate feature set is input into the deep learning model. The aggregate feature set comprises the first feature set of a level of picker task availability and the second aggregate feature set comprises historic picker activities. Is it Applicant’s intent to utilize the combined feature sets to be utilized in the deep learning model? Claims 8 and 15 are rejected based on the same rationale. Claims 2-7, 9-14 and 16-20 are rejected based on their dependency on Claims 1, 8 and 15 respectively.
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 an abstract idea without significantly more. Claim 1 recites:
receiving, from a client device of a picker, an indication of picker interest in being assigned a task, and a current location of the picker;
generating, based at least in part on the current location of the picker, a prediction of task availability; and causing the client device of the picker to display, in a picker interface thereof, a representation of the prediction of task availability;
wherein the prediction of task availability comprises: extracting an aggregate feature set comprising: a first feature set of a level of picker task availability relative to historical levels in a geographic region encompassing the current location of the picker, the level of task availability generated by:
accessing logged data comprising geographic location, time, and aggregate gap measures computed from work sessions of pickers, the aggregate gap measures representing an aggregate difference between numbers of customer requests for performance of a task and numbers of pickers requesting to be assigned a task to perform;
grouping the aggregate gap measures according to geographic location;
for each of the groups, performing percentile analysis of the aggregate gap measures to identify boundary values defining a set of ranges;
identifying the geographic region encompassing the current location of the picker;
predicting a gap for the geographic region at a predetermined future time; and
a second feature set comprising historical picker activities of the picker;
inputting the aggregate feature set into a deep learning model;
receiving, as output from the deep learning model,
an estimate of time until availability of a first task to the picker from a retailer location within a given radius of the current location of the picker, and
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components (e.g. a processor). For example, receiving an indication of picker interest in being assigned a task, predicting task availability and a predicted gap in a geographic area of a picker to determine task availability is related to managing personal behavior. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
Additionally, the limitations encompass Mathematical Concepts related to mathematical calculations based on using a deep learning model to predicting an estimating time of availability.
Independent Claims 8 and 15 substantially recite the subject matter of Claim 1 and also include the abstract idea identified above. The dependent claims encompass the same abstract idea. For instance, Claim 2 is directed to prediction of task availability comprises estimate of time until availability of a first task to a picker; Claim 3 is directed to predicted wait time until assignment; Claim 4 is directed to identifying features of a current work session; Claim 5 is directed to accessing logged values for features associated with geographic zone; Claim 6 is directed to features associated with a retail location and Claim 7 is directed to features associated with a picker. Claims 9-14 and 16-20 substantially recite the subject of Claims 2-7 and encompass the same abstract idea. Thus, the dependent claims further limit the abstract concepts found in the independent claims.
The judicial exception is not integrated into a practical application. Claims 1 and 8 recite the additional elements of a computer system comprising a processor, a computer-readable medium, a client device and a picker interface. Claim 15 recites the additional elements of a non-transitory computer readable storage medium storing instructions executed by a computer processor, a client device and a picker interface. These are generic computer components recited at a high level of generality as performing generic computer functions (see ¶0012).
For instance, the step of receiving a picker interest in being assigned a task involves data input (data gathering functionality). The step of generating a prediction of task availability includes steps such as a level of task availability, extracting an aggregate feature set comprising a first feature setoff level of picker availability, accessing logged data, computing aggregate gap measures, grouping the aggregate gap measures, identifying the geographic region of the picker, predicting a gap for the geographic region, a second feature set, inputting an aggregate feature set into a deep learning model (complex mathematics), receiving an estimate of time until availability of a first task to the picker from a retailer location involve collecting and analyzing data (data gathering activities). The step of causing the client device of the picker to display the prediction of task availability involves generic display functionality. The step of outputting for display to a picker a picker interface comprising the estimate of time until availability is outputting a result of analyzing data and the step of selecting an interactive element that renders the picker eligible is merely selecting an element on the screen which is generic display functionality.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a picker device, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-20 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Love et al. (US 11790240) discloses During a training phase of the machine network, separate sets of channel features can be collected or aggregated to create one or more training data sets that are utilized to train one or more of the demand prediction models.
O’Mahoney et al. (US 2017/0227370) discloses the wait time for prior providers in a zone may be used to train the model to predict wait times in the future based on various factors, such as the time of day, day of the week, the current wait time for the zone, etc.
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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625