DETAILED ACTION
This final Office action is responsive to amendments filed April 13th, 2026. Claims 1, 11, and 20 have been amended. Claim 10 has been cancelled. Claims 1-9 and 11-20 are presented for examination.
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 Arguments
Applicant’s arguments, see page 15, filed 04/13/26, with respect to claims 1, 11, and 20 have been fully considered and are persuasive. The objections of 01/12/26 have been withdrawn.
Applicant's arguments filed 04/13/26 have been fully considered but they are not persuasive.
On pages 15-16 of the provided remarks, Applicant argues that the amended claims present an improvement to the technical field of graphical user interfaces. Specifically on pages 15-16 of the provided remarks, Applicant argues that the amended claims are “providing a dynamic, interactive visualization that integrates machine-learning generated scheduling data with a responsive heat map display.” Examiner respectfully disagrees and asserts that while the suggested schedule is utilized to display the heat map within the user interface, the argued “machine-learning generated scheduling data” is not the sole input required in generating the suggested scheduling data. The claims recite the high-level “computation of estimated earnings”, “computation of a total estimated amount of earnings”, and an “identification of a suggested schedule for achieving the goal” that are separate computer functions from the argued machine-learning scheduling data. As written, the claims utilize machine-learning scheduling data as an input for further calculation regarding the suggested schedule of the picker, but as Examiner has previously stated, this additional calculation is regarded as a mental evaluation of the human mind. The claimed “machine learning scheduling data” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Applicant’s arguments are not persuasive.
Continuing on page 16 of the provided remarks, Applicant argues “By combining predicted earnings and availability data into a unified interface that responds to user interactions, the invention enhances how complex scheduling information is presented and navigated on computing devices. This improvement is achieved through a particular arrangement and processing of data: generating shading for map portions based on the underlying computed earnings values, linking those visual elements to interactive regions of the display, and updating the interface in real time as the user interacts. These features improve the computer's functionality by enabling more efficient human-computer interaction, facilitating decision- making through real-time visualization of multidimensional predictive information.” Examiner respectfully disagrees and begins by asserting that the present claims do no recite the argued “linking those visual elements to interactive regions of the display” but simply recites “generating the heat map based on those generated shadings” which recited at a high-level of generality is an observation and judgment of the human mind. Further, the updating of the user interface display occurs as a result of human interaction which would not enhance how complex scheduling information is presented and navigated on computing devices as argued by Applicant. MPEP 2106.05(a) recites ‘Improvements to Computer Functionality’. Within the provided examples, the following relate to the functioning of a user interface, “viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).” Examiner asserts that the argued heat map display is analogous to the cited arrangement of transactional information above, which the courts have indicated may not be sufficient to show an improvement in computer-functionality. Therefore, the 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 16-17, filed 04/13/26, with respect to claims 1-20 have been fully considered and are persuasive. The 35 USC 103 rejection of 01/12/26 has been withdrawn.
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 1-9 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Claims 1-9
Step 1: Independent claims 1 (method), and dependent claims 2-9 fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a method (i.e. process).
Step 2A Prong 1: The independent claim recites receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot- location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; inputting, to a first machine learning model that is trained to predict a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair, each time slot-location pair and a set of attributes associated with a respective time slot-location pair, wherein the first machine learning model is trained on a set of training examples using supervised learning, the set of training examples including a set attributes for each of a plurality of time slot-location pairs, each set of attributes labeled with an indication of whether an order placed with the online concierge system was available for another picker to service for a corresponding time slot- location pair, wherein the plurality of time-slot locations pairs in the training examples include at least a first time slot-location pair associated with a first day and a first location and a second time slot-location pair associated with a second day and second location; receiving, from the first machine learning model for each time slot-location pair, a predicted likelihood that an order placed with the online concierge system will be available for the picker to service for the time slot-location pair; inputting, to a second machine learning model that is trained to predict an amount of earnings for the picker if the picker services an order placed with the online concierge system, each time slot-location pair and set of attributes associated with a respective time-slot location pair; receiving, from the second machine learning model for each time slot-location pair, a predicted amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot- location pair; for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted likelihood and the predicted amount of earnings for a corresponding time slot-location pair; generating a set of suggested schedules from the set of time slot-location pairs, wherein each suggested schedule comprises one or more time slot-location pairs of the set of time slot-location pairs and corresponds to availability information for the picker; for each suggested schedule, computing a total estimated amount of earnings for the picker by combining the estimated amount of earnings associated with each time slot-location pair of a corresponding suggested schedule and one or more costs for the picker associated with the corresponding suggested schedule; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal, wherein the total estimated amount of earnings for the identified suggested schedule is greater than the earnings from the goal; sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule (Certain Method of Organizing Human Activity, Mental Process, & Mathematical Concepts), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are generating a set of suggested schedules from the set of time slot-location pairs; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal; and generating a heat map, based on user selection of location, of the estimated amount of earnings for the location, which is managing personal behavior. The Applicant’s claimed limitations are generating and identifying a suggested schedule for a picker of an online concierge, which recite the abstract idea of Certain Methods of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are predicting a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair; predicting the likelihood that an order placed with the online concierge system will be available for the picker to service for each time slot-location pair; predicting an amount of earnings for the picker if the picker services an order placed with the online concierge system for a time slot-location pair; for each time slot-location pair, computing an estimated amount of earnings for the picker based at least in part on the predicted likelihood and the predicted amount of earnings for a corresponding time slot-location pair; generating a set of suggested schedules from the set of time slot-location pairs; for each suggested schedule, computing a total estimated amount of earnings for the picker based at least in part on the estimated amount of earnings and one or more costs for the picker associated with a corresponding suggested schedule; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal; generating a shading for the corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule, which are functions of the human mind in the form of observation, judgement, and evaluation. The Applicant’s claimed limitations are generating and identifying a suggested schedule for a picker of an online concierge, which recite the abstract idea of Mental Process.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mathematical Concepts because the claimed limitations are training a set of training examples using supervised learning, which under broadest reasonable interpretation, per paragraphs [0054-55] of the provided Specification, requires mathematical calculations to perform the training and therefore encompasses mathematical concepts. The Applicant’s claimed limitations are training examples using supervised learning, which recite the abstract idea of Mathematical Concepts.
In addition, dependent claims 2-4 and 6-9 further narrow the abstract idea and recite to further defining the training of the model using supervised learning; identification of the suggested schedule for achieving the goal; the goal selection; the one or more costs; the generation of the set of suggested schedules. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal behavior as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they are directed to abstract ideas. Dependent claims 3-5 will be discussed in Prong 2 analysis below.
Step 2A Prong 2: In this application, the above “receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot- location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; receiving, from the first machine learning model for each time slot-location pair, a predicted likelihood that an order placed with the online concierge system will be available for the picker to service for the time slot-location pair; receiving, from the second machine learning model for each time slot-location pair, a predicted amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot- location pair; sending the suggested schedule to the picker client device; sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule; accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; receiving a user interaction with the heat map through the graphical user interface; updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “a computer system comprising a processor and a computer-readable medium; a picker client device associated with a picker of an online concierge system; a graphical user interface” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claim 1 recites the following limitation, “inputting, to a first machine learning model that is trained”; “receiving, from the first machine learning model”; “inputting, to a second machine learning model that is trained”; and “receiving, from the second machine learning model”. Additionally, dependent claims 3-4 and 13-14 recite “wherein the first machine learning model is trained by” and “wherein the second machine learning model is trained by”. The “first machine learning model”, “second machine learning model”, are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-4 and 6-9 further narrow the abstract idea and dependent claims 3-5 additionally recite “receiving a plurality of attributes associated with a plurality of time slot-location pairs, wherein the plurality of attributes describes a demand side, a supply side, and a set of supply gaps associated with the online concierge system”; “receiving, for each of the plurality of time slot-location pairs, a label indicating whether an order placed with the online concierge system was available for a picker to service for a corresponding time slot-location pair”; “the suggested schedule is sent to the picker client device in association with the total estimated amount of earnings” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “processor” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “a computer system comprising a processor and a computer-readable medium; a picker client device associated with a picker of an online concierge system; a graphical user interface” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 1-9 recite “a computer system comprising a processor and a computer-readable medium; a picker client device associated with a picker of an online concierge system; a graphical user interface”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0010 and 0083 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot- location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; receiving, from the first machine learning model for each time slot-location pair, a predicted likelihood that an order placed with the online concierge system will be available for the picker to service for the time slot-location pair; receiving, from the second machine learning model for each time slot-location pair, a predicted amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot- location pair; sending the suggested schedule to the picker client device; sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule; accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; receiving a user interaction with the heat map through the graphical user interface; updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” of independent claim 1 as well as dependent claims 3-4 is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a first and second learning model does not add significantly more to the claim.
In addition, claims 2-4 and 6-9 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 3-5 additionally recite “receiving a plurality of attributes associated with a plurality of time slot-location pairs, wherein the plurality of attributes describes a demand side, a supply side, and a set of supply gaps associated with the online concierge system”; “receiving, for each of the plurality of time slot-location pairs, a label indicating whether an order placed with the online concierge system was available for a picker to service for a corresponding time slot-location pair”; “the suggested schedule is sent to the picker client device in association with the total estimated amount of earnings” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “processor” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 11-20
Step 1: Independent claims 11 (computer program product) and 20 (system) and dependent claims 12-19, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 11 is directed to a computer program product comprising a non-transitory computer-readable medium (i.e. manufacture) and claim 20 is directed to a system (i.e. machine).
Step 2A Prong 1: The independent claims recite receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot-location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; inputting, to a first machine learning model that is trained to predict a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair, each time slot-location pair and a set attributes associated with a respective time slot-location pair, wherein the first machine learning model is trained on a set of training examples using supervised learning, the set of training examples including a set attributes for each of a plurality of time slot-location pairs, each set of attributes labeled with an indication of whether an order places with the online concierge system was available for another picker to service for a corresponding time slot-location pair, wherein the plurality of time slot-location pairs in the training examples include at least a first time slot-location pair associated with a first data and a first location and a second time slot-location pair associated with a second day and second location; receiving, from the first machine learning model for the time slot-location pair a predicted likelihood that an order placed with the online concierge system will be available for the picker to service for each time slot-location pair; inputting, to a second machine learning model that is trained to predict an amount of earnings for the picker if the picker services an order placed with the online concierge system, each time slot-location pair and set of attributes associated with a respective time-slot location pair; receiving from the second machine learning model for each time slot-location pair, a predicted amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot-location pair; for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted likelihood and the predicted amount of earnings for a corresponding time slot-location pair; generating a set of suggested schedules from the set of time slot-location pairs, wherein each suggested schedule comprises one or more time slot-location pairs of the set of time slot-location pairs and corresponds to availability information for the picker; for each suggested schedule, computing a total estimated amount of earnings for the picker by combining the estimated amount of earnings associated with each time slot-location pair of a corresponding suggested schedule and one or more costs for the picker associated with the corresponding suggested schedule; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal, wherein the total estimated amount of earnings for the identified suggested schedule is greater than the earnings from the goal; sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule (Certain Method of Organizing Human Activity, Mental Process, & Mathematical Concepts), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above are directed to the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are generating a set of suggested schedules from the set of time slot-location pairs; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal; and generating a heat map representing the amount of estimated earnings for each time slot-location pair in the suggested schedule, which is managing personal behavior. The Applicant’s claimed limitations are generating and identifying a suggested schedule for a picker of an online concierge, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are predicting a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair; predicting the likelihood that an order placed with the online concierge system will be available for the picker to service for each time slot-location pair; predicting an amount of earnings for the picker if the picker services an order placed with the online concierge system for a time slot-location pair; for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted likelihood and the predicted amount of earnings for a corresponding time slot-location pair; generating a set of suggested schedules from the set of time slot-location pairs and corresponds to availability information for the picker; for each suggested schedule, computing a total estimated amount of earnings for the picker by combining the estimated amount of earnings associated with each time slot-location pair of a corresponding suggested schedule and one or more costs for the picker associated with a corresponding suggested schedule; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal, wherein the total estimated amount of earnings for the identified suggested schedule is greater than the earnings from the goal; generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule, which are functions of the human mind in the form of observation, judgement, and evaluation. The Applicant’s claimed limitations are generating and identifying a suggested schedule for a picker of an online concierge, which recite the abstract idea of Mental Process.
The steps/functions disclosed above and in the independent claims as well as dependent claims 13-14 recite the abstract idea of Mathematical Concepts because the claims are training a first and second machine learning model using supervised learning with the labeled plurality of attributes. Under broadest reasonable interpretation, per paragraphs [0054-55] of the provided Specification, supervised learning requires mathematical calculations to perform the training and therefore encompasses mathematical concepts. The Applicant’s claimed limitations are training examples using supervised learning, which recite the abstract idea of Mathematical Concepts.
In addition, dependent claims 12 and 16-19 further narrow the abstract idea and recite to further defining the identification of the suggested schedule for achieving the goal; the goal selection; the one or more costs. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal behavior as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they are directed to abstract ideas. Dependent claims 13-15 will be discussed in Prong 2 analysis below.
Step 2A Prong 2: In this application, the above “receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot-location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule; accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform actions; a picker client device; online concierge system; a graphical user interface; A computer system comprising: a processor; and a non-transitory computer readable storage medium storing instructions that, when executed by the processor” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 11 and 20 recite the following limitation, “inputting, to a first machine learning model that is trained”; “receiving, from the first machine learning model”; “inputting, to a second machine learning model that is trained”; and “receiving, from the second machine learning model”. Additionally, dependent claims 13-14 recite “wherein the first machine learning model is trained by” and “wherein the second machine learning model is trained by”. The “first machine learning model”, “second machine learning model”, and subsequent “training the [first, second] machine learning model based at least in part on the plurality of attributes and the label for each of the plurality of time slot-location pairs” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 12 and 16-19 further narrow the abstract idea and dependent claims 13-15 additionally recite “receiving a plurality of attributes associated with a plurality of time slot-location pairs, wherein the plurality of attributes describes a demand side, a supply side, and a set of supply gaps associated with the online concierge system”; “receiving, for each of the plurality of time slot-location pairs, a label indicating whether an order placed with the online concierge system was available for a picker to service for a corresponding time slot-location pair”; “the suggested schedule is sent to the picker client device in association with the total estimated amount of earnings” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “computer program product” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform actions; an online concierge system; a picker client device; a graphical user interface; A computer system comprising: a processor; and a non-transitory computer readable storage medium storing instructions that, when executed by the processor” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, computer program product claims 11-19; and system claim 20 recite “A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform actions; an online concierge system; picker client device; a graphical user interface; A computer system comprising: a processor; and a non-transitory computer readable storage medium storing instructions that, when executed by the processor”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0010 and 0083 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot-location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule; accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” of independent claims 11 and 20 as well as dependent claims 13-14 is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a first and second learning model does not add significantly more to the claim.
In addition, claims 12 and 16-19 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 13-15 additionally recite “receiving a plurality of attributes associated with a plurality of time slot-location pairs, wherein the plurality of attributes describes a demand side, a supply side, and a set of supply gaps associated with the online concierge system”; “receiving, for each of the plurality of time slot-location pairs, a label indicating whether an order placed with the online concierge system was available for a picker to service for a corresponding time slot-location pair”; “the suggested schedule is sent to the picker client device in association with the total estimated amount of earnings” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “computer program product” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: Claims 1, 11, and 20 recite a combination of claim limitations that, as drafted, under considerations of the broadest reasonable interpretation of the claimed invention, are rendered neither obvious nor anticipated by the available field of prior art. The prior art of the record fails to explicitly teach, disclose, or suggest the combination of claim limitations, including at least: sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule. The closest prior art of the record discloses:
Kneiper (U.S 2009/0043630 A1) discloses a scheduling method achieving revenue objectives in a given time frame including: receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot- location pairs and each time slot-location pair corresponds to a time slot and a location; predict an amount of earnings for the picker if the picker services an order placed with the online concierge system, each time slot-location pair and set of attributes associated with a respective time-slot location pair; a predicted amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot-location pair; for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted likelihood and the predicted amount of earnings for a corresponding time slot-location pair; generating a set of suggested schedules from the set of time slot-location pairs, wherein each suggested schedule comprises one or more time slot-location pairs of the set of time slot-location pairs; for each suggested schedule, computing a total estimated amount of earnings for the picker by combining the estimated amount of earnings associated with each time slot-location pair of a corresponding suggested schedule and one or more costs for the picker associated with the corresponding suggested schedule; identifying, from the set of suggested schedules, a suggested schedule for achieving the goal, wherein the total estimated amount of earnings for the identified suggested schedule is greater than the earnings from the goal; and sending the suggested schedule to the picker client device. However, Kneiper does not disclose sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule.
Kim (U.S 10,769,588 B1) discloses a system and method employing machine-learning techniques to identify trends and predict fulfillment center of camp demand to make assignments including: inputting, to a first machine learning model that is trained to predict a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair, each time slot-location pair and a set of attributes associate with the respective time slot-location pair; receiving, form the first machine learning model for each time slot-location pair, a predicted likelihood that an order placed with the online concierge system will be available for the picker to service for the time slot-location pair; and sending the suggested schedule to the picker client device. However, Kim does not disclose sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule.
Vasagiri (U.S 2022/0245595 A1) discloses receiving, from a picker client device associated with a picker of an online concierge system, a goal associated with earnings for the picker and availability information for the picker, wherein the availability information describes a set of time slot- location pairs and each time slot-location pair corresponds to a time slot and a location for which the picker is available to service orders placed with the online concierge system, the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; concierge system; inputting, to a first machine learning model that is trained to predict a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair, each time slot-location pair and a set of attributes associated with a respective time slot-location pair, wherein the first machine learning model is trained on a set of training examples using supervised learning, each set of attributes labeled with an indication of whether an order placed with the online concierge system was available for another picker to service for [[the]] a corresponding time slot- location pair, wherein the plurality of time slot-locations pairs in the training examples include at least a first time slot-location pair associated with a first day and a first location and a second time slot-location pair associated with a second day and second location. However, Vasagiri does not disclose sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule.
Reiss (U.S 10,346,889 B1) discloses receiving, from a picker client device associated with a picker of an online concierge system, a location for which the picker is available to service orders placed with the online concierge system; inputting, to a first machine learning model that is trained to predict a likelihood that an order placed with the online concierge system will be available for the picker to service for a time slot-location pair; inputting, to a second machine learning model that is trained to predict an amount of earnings for the picker if the picker services an order placed with the online concierge system, each time slot-location pair and set of attributes associated with a respective time-slot location pair; receiving, from the second machine learning model for each time slot-location pair, a predicted amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot-location pair; for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted likelihood and the predicted amount of earnings for a corresponding time slot-location pair; and sending the suggested schedule to the picker client device. However, Vasagiri does not disclose sending the suggested schedule to the picker client device for display to a user through a graphical user interface, wherein the graphical user interface displays the suggested schedule and a heat map representing the estimated amount of earnings for each time slot-location pair in the suggested schedule, wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule.
Ezry (U.S 2016/0140463 A1) discloses the generation of a heat map based at least in part on the estimated amount of earnings and sending the heat map to the picker client device. However, Ezry does not disclose wherein generating the heat map comprises: accessing each of the estimated amounts of earnings for each of the time slot- location pairs in the suggested schedule; for location of the time slot-location pairs in the suggested schedule, generating a shading for a corresponding portion of a map based on the corresponding estimated amount of earnings; generating the heat map based on the generated shadings; receiving a user interaction with the heat map through the graphical user interface; identifying, based on the user interaction with the heat map, a selected location of a selected time slot-location pair of the time slot-location pairs of the suggested schedule; and updating the graphical user interface to display an estimated amount of earnings corresponding to the selected time slot-location pair of the suggested schedule.
Therefore, the combination of claim limitations, when considered in view of the available field of prior art, are rendered neither obvious nor anticipated.
However, the present claims are not in condition for allowance because the claims are rejected under 35 USC 101, as set forth in the current office action. Therefore, the claims are not in condition for allowance at this time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lioznova, Anna, et al. "Prediction of hourly earnings and completion time on a crowdsourcing platform." Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2022/0198353 A1
Aslam et al.
SYSTEM AND METHOD FOR SHIFT SCHEDULE MANAGEMENT
CN111784290A
Chen et al.
Wage Unified Management System Based On Internet of Things
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 KRISTIN ELIZABETH GAVIN whose telephone number is (571)270-7019. The examiner can normally be reached M-F 7:30-4:30 PM EST.
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
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