DETAILED ACTION
This non-final Office action is responsive to amendments filed 8/15/25. Claims 1, 11, and 20 have been amended. Claims 1-20 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/15/25 has been entered.
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC 101 filed 8/15/25 have been fully considered but they are not persuasive.
On pages 13-15 of the provided remarks, Applicant argues that the amended claims present a technical solution to a technical problem. Beginning on page 13 of the provided remarks, Applicant argues that the technical problem of the current application is “when a model lacks sufficient training data, which leads to underfitting and poor predictive performance”. Continuing on page 14 of the provided remarks, Applicant argues “Claim 1 addresses this technical problem by using predictive and optimization techniques that do not rely on historical data from an individual picker to estimate earnings for pickers "associated with servicing less than an average number of orders serviced by [other] pickers." Instead, the claims machine learning models are trained on population-level data from other pickers, using features associated with time slot-location pairs to predict both the likelihood that an order will be available and the expected earnings if the order is serviced.” Examiner respectfully disagrees and begins by citing MPEP 2106.05(a) “An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” In reviewing the as-filed Specification, the Background identifies worker discouragement in completely orders and meeting earnings goals as the problem addressed by the claimed method. The argued “lack of sufficient training data” is not present within the Specification as the technical problem solved. Additionally, Examiner asserts that a “lack of sufficient training data” is not a technical problem and cites MPEP 2106.05(a)(II) for examples of improvements to technology. Applicant’s arguments are not persuasive.
Applicant continues on page 14 of the provided remarks to cite MPEP 2106.05(a), “Generally, examiners are not expected to make a qualitative judgement on the merits of the asserted improvement.” However, Examiner notes that prior to this statement, MPEP 2106.05(a) recites, “During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement.” As stated above, the explanation of the problem and improvement within the Specification does not align with the argument presented by Applicant. Therefore, the 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 15-16, filed 8/15/25, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Knieper (U.S 2009/0043630 A1) in view of Kim (U.S 10,769,588 B1) in view of Vasagiri (U.S 2022/0245595 A1) in view of Reiss (U.S 10,346,889 B1).
Claim Objections
Claims 1, 11, and 20 are objected to because of the following informalities:
The limitation beginning “inputting, to a first machine learning model” recites “the respective time slot-location pair” which lacks antecedent basis;
Appropriate correction is required.
Claim 1 is objected to because of the following informalities:
The limitation beginning “inputting, to a first machine learning model” recites in the final “wherein” clause “the corresponding time slot-location pair” which lacks antecedent basis;
Appropriate correction is required.
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 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 1-10
Step 1: Independent claims 1 (method), and dependent claims 2-10 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 the 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 of 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 the 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 the 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; and sending the suggested schedule to the picker client device (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 and identifying, from the set of suggested schedules, a suggested schedule for achieving the goal, 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, 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 6-10 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; and generating a heat map based at least in part on the estimated amount of earnings for each time slot-location pair. 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” 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” 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 6-10 further narrow the abstract idea and dependent claims 3-5 and 10 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” 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 1-10 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”; 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” 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 6-10 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 and 10 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 of attributes associated with the respective 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 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 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 the 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; and sending the suggested schedule to the picker client device (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 and identifying, from the set of suggested schedules, a suggested schedule for achieving the goal, 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, 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; the generation of the set of suggested schedules; and generating a heat map based at least in part on the estimated amount of earnings for each time slot-location pair. 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, even if not directed toward the abstract idea, 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” 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 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 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 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” 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7, 11-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Knieper (U.S 2009/0043630 A1) in view of Kim (U.S 10,769,588 B1) in view of Vasagiri (U.S 2022/0245595 A1) in view of Reiss (U.S 10,346,889 B1).
Claim 1
Regarding Claim 1, Knieper discloses the following:
A method comprising, at a computer system comprising a processor and a computer-readable medium [see at least Paragraph 0012 for reference to the scheduling method for achieving revenue objective in a given time period; Paragraph 0019 for reference to the method may be carried out by software stored on a computer readable medium, the Software containing instructions executable by a processor when loaded into main memory; Figure 1 and related text regarding the steps of the scheduling method achieving revenue objectives in a given time frame; Figure 2 and related text regarding the scheduling method for achieving revenue objectives in a given time frame; Figure 5 and related text regarding the exemplary system upon which the scheduling method for achieving and/or exceeding revenue objectives]
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 [see at least Paragraph 0013 for reference to the scheduling method is intended for use by a business or service provider for achieving revenue objectives in a given time period and includes the steps of recording a revenue goal for a specified time period and determining the number of working days within the specified time period for which the revenue goal was determined; Paragraph 0015 for reference to the business determining the number of working days that are going to be contained within that specified time period, taking into consideration such matters as holidays, desired vacations, days off, lunch breaks, office meeting times, etc.; Figure 1 and related text regarding item 110 ‘Determine a revenue (income) goal for a specified time period (e.g., week, month, year)’ and item 120 ‘Determine the number of working days within the specified time period for which the revenue goal was determined’]
predict 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, each time slot-location pair and a set of attributes associated with the respective time slot-location pair [see at least Paragraph 0013 for reference to the method recording the income generated for each task; Figure 1 and related text regarding item 150 ‘Assign the income generated to each task ($100-$250, etc.)’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’]
predict the amount of earnings for the picker if the picker services an order placed with the online concierge system for each time slot-location pair and set of attributes associated with the respective time-slot location pair [see at least Paragraph 0013 for reference to the method recording the income generated for each task; Figure 1 and related text regarding item 150 ‘Assign the income generated to each task ($100-$250, etc.)’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’]
for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted amount of earnings for a corresponding time slot-location pair [see at least Paragraph 0013 for reference to the method recording the income generated for each task; Figure 1 and related text regarding item 150 ‘Assign the income generated to each task ($100-$250, etc.)’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’]
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 [see at least Abstract and related text regarding The scheduling method arranges and schedules tasks to ensure the completion of a revenue goal within a specified period of time; Paragraph 0013 for reference to scheduling categories to fill the task completion time available for each single working day in the specified time period; Figure 1 and related text regarding item 180 ‘Schedule tasks to fill the task completion time available for each single working day’]
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 [see at least Paragraph 0017 for reference to for each day in which tasks are completed, the minimum income generated total can be calculated; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
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 [see at least Paragraph 0013 for reference to the system arranging the categories in a pattern so that the sum of the lowest low-end (minimum) income generated is equal to or exceeds the revenue goal for the specified time period; Paragraph 0017 for reference to a single week of the schedule may be planned out for a typical dental practitioner using the patterned coding system established in the completion time and minimum income generated categories chart; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
sending the suggested schedule to the picker client device [see at least Paragraph 0018 for reference to the method steps can be performed on a digital computer in operable communication with at least one memory storage device and at least one user interface Claim 8 and related text regarding the processor to display the revenue goal for each of the specified time periods to a user; Claim 16 and related text regarding a means for displaying a calendar filled with the distinctive codes in a pattern scheduling the categories according to the revenue goal for the specified time period]
While Knieper discloses the limitations above, it does not disclose 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, 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 the 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 of 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 the 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 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 the 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; and 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.
However, Kim discloses the following:
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 [see at least Col 4 lines 43-46 for reference to the disclosed system and method employing machine-learning techniques to identify trends and predict fulfilment center of camp demand to make assignments; Col 4 lines 62-67 and Col 5 lines 1-2 for reference to the system analyzing previous orders and performance of fulfilment centers or camps to determine forecasted demand and using machine-learning predictive models to determine which process has the most effective outcomes; Figure 8 and related text regarding item 806 ‘Forecast Demand for Multiple Camps and/or Fulfilment Centers Based on Predictive Model’]
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 each time slot-location pair [see at least Col 4 lines 43-46 for reference to the disclosed system and method employing machine-learning techniques to identify trends and predict fulfilment center of camp demand to make assignments; Col 4 lines 62-67 and Col 5 lines 1-2 for reference to the system analyzing previous orders and performance of fulfilment centers or camps to determine forecasted demand and using machine-learning predictive models to determine which process has the most effective outcomes; Figure 8 and related text regarding item 806 ‘Forecast Demand for Multiple Camps and/or Fulfilment Centers Based on Predictive Model’]
sending the suggested schedule to the picker client device [see at least Col 24 lines 24-29 for reference to the scheduling system generating scheduling notification GUIs for workers; Figure 2 and related text regarding item 224A & 224B ‘workers’; Figure 8 and related text regarding item 816 ‘Generate scheduling notifications GUIs for Administrator and Workers’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Kim. Doing so provides the users of the system with interactive tools to efficiently manage curriers and prioritize work, as stated by Kim (Col 4 lines 36-39).
While the combination of Knieper and Kim discloses the limitations above, they do not disclose the picker client device associated with servicing less than an average number of orders serviced by pickers associated with the online concierge system; 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 of 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 the 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; 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 the 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; and 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.
However, Vasagiri discloses the following:
receiving, from a picker client device associated with a picker of an online concierge system, 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 [see at least Paragraph 0033 for reference to workforce availability data identifying how many pickers and/or drivers are available over a future period of time; Paragraph 0053 for reference to database storing capacity data including store employee (e.g., pickers) schedules and delivery employee schedules for each of a plurality of locations; Paragraph 0076 for reference to a request for the plurality of timeslots being received by the web server and the timeslot determination device obtaining employee schedules and/or delivery employee schedules that correspond to one or more of the plurality of timeslots; Figure 3 and related text regarding item 380 ‘Capacity Management Engine’ and item 332 ‘Store Employee Schedules’; Figure 9A and related text regarding item 904 ‘OBTAIN WORKFORCE AVAILABILITY DATA FOR THE PLURALITY OF TIMESLOTS FOR THE PREVIOUS PERIOD’; Figure 9B and related text regarding item 956 ‘OBTAIN WORKFORCE AVAILABILITY DATA FOR THE PLURALITY OF TIMESLOTS FOR THE FUTURE DATE’]
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 the respective time slot-location pair [see at least Paragraph 0056 for reference to the slot forecasting engine obtaining a slot opening model to determine timeslot availability time for timeslots; Paragraph 0077 for reference to features being generated based on the historical timeslot data and the workforce availability data; Paragraph 0077 for reference to a timeslot capacity for each of the plurality of timeslots for the future date is determined based on applying a trained machine learning model to the generated features; Figure 9B and related text regarding item 958 ‘GENERATE FEATURES BASED ON HISTORICAL TIMESLOT DATA AND WORKFORCE AVAILABILITY DATA’ and item 960 ‘DETERMINE A TIMESLOT CAPACITY FOR EACH OF THE PLURALITY OF TIMESLOTS FOR THE FUTURE DATE BASED ON APPLYING A TRAINED MACHINE LEARNING MODEL TO THE HISTORICAL TIMESLOT DATA AND THE WORKFORCE AVAILABILITY DATA’]
wherein the first machine learning model is trained on a set of training examples using supervised learning [see at least Paragraph 0034 for reference to apply a trained machine learning model to the generated features to determine a timeslot capacity for each of the timeslots wherein the machine learning model is a supervised machine learning model that predicts a timeseries; Paragraph 0057 for reference to the slot forecasting engine applying slot capacity models to generated features to determine timeslot capacities for the timeslots, wherein the capacity models may be a supervised machine learning model that predicts a time-series]
the set of training examples including a set attributes of 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 the 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 [see at least Paragraph 0053 for reference to database storing capacity data including store employee (e.g., pickers) schedules and delivery employee schedules for each of a plurality of locations; Paragraph 0059 for reference to feature data including feature vectors for one or more features including features based on historical timeslot selections (e.g., pickup and delivery timeslot), timeslot demand over previous periods of time (e.g., slot requested), timeslot sales over previous periods of time (e.g., slot requested), timeslot sales over previous periods of time (e.g., purchase amount), and workforce availability; Paragraph 0075 for reference to a timeslot determination computing device training a machine learning model with the generated features; Figure 9A and related text regarding the method for training a machine learning model, including item 908 ‘TRAIN A MACHINE LEARNING MODEL WITH THE HISTORICAL TIMESLOT DATA AND THE WORKFORCE AVAILABILITY DATA’; Figure 9B and related text regarding item 958 ‘GENERATE FEATURES BASED ON HISTORICAL TIMESLOT DATA AND WORKFORCE AVAILABILITY DATA’ and item 960 ‘DETERMINE A TIMESLOT CAPACITY FOR EACH OF THE PLURALITY OF TIMESLOTS FOR THE FUTURE DATE BASED ON APPLYING A TRAINED MACHINE LEARNING MODEL TO THE HISTORICAL TIMESLOT DATA AND THE WORKFORCE AVAILABILITY DATA’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the machine learning techniques of Kim to include the supervised machine learning techniques of Vasagiri. Doing so not only learns an optimal number of slots that need to be opened, it also adaptively, without human - intervention, can make those adjustments on an occasional (e.g., daily basis) to the slot capacity so that discrepancy between the customer - demand for slots, and the ability to open the maximum number of slots for that hour given the constraint of store/fulfilment center picker workforce availability and other fulfilment related logistics challenges, is minimized, thereby leading to an optimal slot availability for customers where customer pick-up or order delivery times can be met, as stated by Vasagiri (Paragraph 0037).
While the combination of Knieper, Kim, and Vasagiri discloses the limitations above, they do not disclose 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 the 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; and 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.
However, Reiss discloses the following:
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 [see at least Col 5 lines 10-14 for reference to the service provider identifying available couriers within a threshold proximity to the merchant pickup location; Col 5 lines 52-57 for reference to each courier device including a GPS receiver or other location sensor to periodically provide updated location information to the service provider; Figure 6 and related text regarding item 602 ‘RECEIVE, FROM A PLURALITY OF COURIER DEVICES, OVER A PERIOD OF TIME, RESPECTIVE GEOGRAPHIC LOCATIONS OF THE COURIER DEVICES’; Figure 9 and related text regarding item 136 ‘courier device’]
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 [see at least Col 5 lines 10-14 for reference to the service provider identifying available couriers within a threshold proximity to the merchant pickup location based on the predicted courier travel times; Col 16 lines 59-67 for reference to the courier effort and/or payment determining logic including one or more computational models for determining predicted courier travel times to the merchant pickup location to the delivery location based on courier historic information; Col 17 lines 4-8 for reference to the courier effort and/or payment determining logic determining a confidence score for a prediction of how long it will take a courier to travel from a first point to a second point within the service region at a particular time; Col 18 lines 5-25 for reference to the use of the statistical models being trained using training data for predicting at least one of the predicted courier travel times or other times based on the confidence score exceeding a specified threshold of confidence; Figure 5 and related text regarding item 542 ‘Predicted Courier Travel Time For New Order’ and item 544 ‘Predicted Courier Other Time For New Order’]
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 the respective time-slot location pair [see at least Col 14 lines 31-34 for reference to target hourly rates being selected or adjusted based on suitable revenue models of the service for the service region; Col 16 lines 35-39 for reference to courier effort and/or payment determining logic for determining courier payment for one or more delivery jobs corresponding to one or more respective orders; Col 16 lines 39-45 for reference to the courier effort and/or payment determining logic including one or more algorithms, one or more computational models, a plurality of decision-making rules, or the like, configured to determine normalized distributions and/or aggregated past order information; Col 18 lines 37-40 for reference to the courier travel time rate and the courier other time rate being determined in advance based on a target revenue model of the service for the service region; Figure 5 and related text regarding item 536 ‘Courier Effort and/or Payment Determining Logic’]
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 [see at least Col 14 lines 31-34 for reference to target hourly rates being selected or adjusted based on suitable revenue models of the service for the service region; Col 16 lines 35-39 for reference to courier effort and/or payment determining logic for determining courier payment for one or more delivery jobs corresponding to one or more respective orders; Col 16 lines 39-45 for reference to the courier effort and/or payment determining logic including one or more algorithms, one or more computational models, a plurality of decision-making rules, or the like, configured to determine normalized distributions and/or aggregated past order information; Col 18 lines 37-40 for reference to the courier travel time rate and the courier other time rate being determined in advance based on a target revenue model of the service for the service region; Figure 5 and related text regarding item 536 ‘Courier Effort and/or Payment Determining Logic’]
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 [see at least Col 18 lines 28-36 for reference to the determined courier payment being calculated based on the predicted courier travel time and other time of the new order; Figure 5 and related text regarding item 546 ‘Courier Payment For Delivery Job Corresponding to the New Order’]
sending the suggested schedule to the picker client device [see at least Col 22 and related text regarding the computing device sending to the courier device information about the order; Figure 6 and related text regarding item 618 ‘SEND, TO THE COURIER DEVICE, INFORMATION ABOUT THE ORDER INCLUDING A COURIER PAYMENT AMOUNT THAT IS BASED ON THE FIRST PAYMENT AMOUNT PLUS THE SECOND PAYMENT AMOUNT; Figure 7 and related text regarding item 710 ‘SEND, TO A COURIER DEVICE, INFORMATION RELATED TO THE ORDER AND AN INDICATION OF THE COURIER PAYMENT AMOUNT’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Reiss. Doing so would assist in determining appropriate courier payment for particular delivery jobs, as stated by Reiss (Col 2 lines 32-34).
Claim 2
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, regarding Claim 2, Knieper discloses the following:
wherein identifying the suggested schedule for achieving the goal is further based at least in part on a number of contiguous time slots included in the suggested schedule [see at least Paragraph 0013 for reference to the system arranging the categories in a pattern so that the sum of the lowest low-end (minimum) income generated is equal to or exceeds the revenue goal for the specified time period; Paragraph 0017 for reference to a single week of the schedule may be planned out for a typical dental practitioner using the patterned coding system established in the completion time and minimum income generated categories chart; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
Claim 3
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, Knieper does not disclose the first machine learning model is trained by: 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; and training the first machine learning model using supervised learning with the labeled plurality of attributes.
Regarding Claim 3, Kim discloses the following:
the first machine learning model is trained by: 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 [see at least Col 19 lines 59-65 for reference to the scheduling system performing operations of generating a predictive model based on the training data set associating request information and fulfilment centers; Figure 9 and related text regarding the predictive model training process]
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 [see at least Col 19 lines 59-65 for reference to the scheduling system performing operations of generating a predictive model based on the training data set associating request information and fulfilment centers and validating the predictive model using the validation data set; Figure 9 and related text regarding the predictive model training process]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Kim. Doing so provides the users of the system with interactive tools to efficiently manage curriers and prioritize work, as stated by Kim (Col 4 lines 36-39).
While Kim discloses the limitations above, it does not disclose training the first machine learning model using supervised learning with the labeled plurality of attributes.
However, Vasagiri discloses the following:
training the first machine learning model using supervised learning with the labeled plurality of attributes [see at least Paragraph 0034 for reference to apply a trained machine learning model to the generated features to determine a timeslot capacity for each of the timeslots wherein the machine learning model is a supervised machine learning model that predicts a timeseries; Paragraph 0057 for reference to the slot forecasting engine applying slot capacity models to generated features to determine timeslot capacities for the timeslots, wherein the capacity models may be a supervised machine learning model that predicts a time-series]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the machine learning techniques of Kim to include the supervised machine learning techniques of Vasagiri. Doing so not only learns an optimal number of slots that need to be opened, it also adaptively, without human - intervention, can make those adjustments on an occasional (e.g., daily basis) to the slot capacity so that discrepancy between the customer - demand for slots, and the ability to open the maximum number of slots for that hour given the constraint of store/fulfilment center picker workforce availability and other fulfilment related logistics challenges, is minimized, thereby leading to an optimal slot availability for customers where customer pick-up or order delivery times can be met, as stated by Vasagiri (Paragraph 0037).
Claim 4
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, Knieper does not disclose wherein the second machine learning model is trained by: receiving a plurality of attributes associated with a plurality of time slot-location pairs; receiving, for each of the plurality of time slot-location pairs, a label indicating an amount of earnings for a picker who serviced an order placed with the online concierge system for a corresponding time slot-location pair; and training the second machine learning model using supervised learning with the labeled plurality of attributes.
Regarding Claim 4, Reiss discloses the following:
wherein the second machine learning model is trained by: receiving a plurality of attributes associated with a plurality of time slot-location pairs [see at least Col 17 lines 64-67 and Col 18 lines 1-6 for reference to the one or more computational models used by the courier effort and/or payment determining logic including one or more trained statistical models that account for numerous pieces of information included in the past order information, as well as current information, such as time, day, and date information, map information, traffic information, weather information, local event information, current and recent courier locations, and the like]
receiving, for each of the plurality of time slot-location pairs, a label indicating an amount of earnings for a picker who serviced an order placed with the online concierge system for a corresponding time slot-location pair [see at least Col 14 lines 31-34 for reference to target hourly rates being selected or adjusted based on suitable revenue models of the service for the service region; Col 16 lines 35-39 for reference to courier effort and/or payment determining logic for determining courier payment for one or more delivery jobs corresponding to one or more respective orders]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Reiss. Doing so would assist in determining appropriate courier payment for particular delivery jobs, as stated by Reiss (Col 2 lines 32-34).
While Reiss discloses the limitations above, it does not disclose training the second machine learning model using supervised learning with the labeled plurality of attributes.
However, Vasagiri discloses the following:
training the second machine learning model using supervised learning with the labeled plurality of attributes [see at least Paragraph 0034 for reference to apply a trained machine learning model to the generated features to determine a timeslot capacity for each of the timeslots wherein the machine learning model is a supervised machine learning model that predicts a timeseries; Paragraph 0057 for reference to the slot forecasting engine applying slot capacity models to generated features to determine timeslot capacities for the timeslots, wherein the capacity models may be a supervised machine learning model that predicts a time-series]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the machine learning techniques of Kim to include the supervised machine learning techniques of Vasagiri. Doing so not only learns an optimal number of slots that need to be opened, it also adaptively, without human - intervention, can make those adjustments on an occasional (e.g., daily basis) to the slot capacity so that discrepancy between the customer - demand for slots, and the ability to open the maximum number of slots for that hour given the constraint of store/fulfilment center picker workforce availability and other fulfilment related logistics challenges, is minimized, thereby leading to an optimal slot availability for customers where customer pick-up or order delivery times can be met, as stated by Vasagiri (Paragraph 0037).
Claim 5
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, regarding Claim 5, Knieper discloses the following:
wherein the suggested schedule is sent to the picker client device in association with the total estimated amount of earnings [see at least Paragraph 0017 for reference to for each day in which tasks are completed, the minimum income generated total can be calculated; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
Claim 6
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, regarding Claim 6, Knieper discloses the following:
wherein a goal is selected from the group consisting of: a maximized amount of earnings and a target amount of earnings [see at least Paragraph 0013 for reference to the scheduling method is intended for use by a business or service provider for achieving revenue objectives in a given time period and includes the steps of recording a revenue goal for a specified time period and determining the number of working days within the specified time period for which the revenue goal was determined; Figure 1 and related text regarding item 110 ‘Determine a revenue (income) goal for a specified time period (e.g., week, month, year)’]
Claim 7
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, Knieper does not disclose wherein the one or more costs are based at least in part on one or more selected from a group consisting of: a cost of fuel and a cost of a toll.
Regarding Claim 7, Reiss discloses the following:
wherein the one or more costs are based at least in part on one or more selected from a group consisting of: a cost of fuel and a cost of a toll [see at least Col 2 lines 48-67 for reference to features for quantifying courier effort including gasoline expenditures]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the cost consideration of Reiss. Doing so would assist in determining appropriate courier payment for particular delivery jobs, as stated by Reiss (Col 2 lines 32-34).
Claims 11 and 20
Regarding Claim 11, Knieper discloses the following:
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 comprising [see at least Paragraph 0019 for reference to the method may be carried out by software stored on a computer readable medium, the Software containing instructions executable by a processor when loaded into main memory; Figure 1 and related text regarding the steps of the scheduling method achieving revenue objectives in a given time frame; Figure 2 and related text regarding the scheduling method for achieving revenue objectives in a given time frame; Figure 5 and related text regarding the exemplary system upon which the scheduling method for achieving and/or exceeding revenue objectives]
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 [see at least Paragraph 0013 for reference to the scheduling method is intended for use by a business or service provider for achieving revenue objectives in a given time period and includes the steps of recording a revenue goal for a specified time period and determining the number of working days within the specified time period for which the revenue goal was determined; Paragraph 0015 for reference to the business determining the number of working days that are going to be contained within that specified time period, taking into consideration such matters as holidays, desired vacations, days off, lunch breaks, office meeting times, etc.; Figure 1 and related text regarding item 110 ‘Determine a revenue (income) goal for a specified time period (e.g., week, month, year)’ and item 120 ‘Determine the number of working days within the specified time period for which the revenue goal was determined’]
predict 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, each time slot-location pair and a set of attributes associated with the respective time slot-location pair [see at least Paragraph 0013 for reference to the method recording the income generated for each task; Figure 1 and related text regarding item 150 ‘Assign the income generated to each task ($100-$250, etc.)’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’ wherein the chart displays based on the time-slot the specific tasks within each and the corresponding income generated]
a predicted 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 the respective time-slot location pair [see at least Paragraph 0013 for reference to the method recording the income generated for each task; Paragraph 0016 for reference to each task having an identical completion time is arranged into the same category, and within each category each task is ordered from the lowest low-end income generated to the highest income generated; Figure 1 and related text regarding item 150 ‘Assign the income generated to each task ($100-$250, etc.)’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’ wherein the chart displays based on the time-slot the specific tasks within each and the corresponding income generated]
for each time slot-location pair, computing an estimated amount of earnings for the picker by combining the predicted amount of earnings for a corresponding time slot-location pair [see at least Paragraph 0013 for reference to the method recording the income generated for each task; Paragraph 0013 for reference to the system determining the sum of the income generated; Paragraph 0017 for reference to for each day in which tasks are completed, the minimum income generated total can be calculated, providing an easy way to calculate weekly or other shorter term totals thereafter, in an attempt to reach the ultimate longer-term revenue goal; Figure 1 and related text regarding item 150 ‘Assign the income generated to each task ($100-$250, etc.)’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’]
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 [see at least Abstract and related text regarding The scheduling method arranges and schedules tasks to ensure the completion of a revenue goal within a specified period of time; Paragraph 0013 for reference to scheduling categories to fill the task completion time available for each single working day in the specified time period; Figure 1 and related text regarding item 180 ‘Schedule tasks to fill the task completion time available for each single working day’]
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 [see at least Paragraph 0017 for reference to for each day in which tasks are completed, the minimum income generated total can be calculated; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
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 [see at least Paragraph 0013 for reference to the system arranging the categories in a pattern so that the sum of the lowest low-end (minimum) income generated is equal to or exceeds the revenue goal for the specified time period; Paragraph 0017 for reference to a single week of the schedule may be planned out for a typical dental practitioner using the patterned coding system established in the completion time and minimum income generated categories chart; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
sending the suggested schedule to the picker client device [see at least Paragraph 0018 for reference to the method steps can be performed on a digital computer in operable communication with at least one memory storage device and at least one user interface Claim 8 and related text regarding the processor to display the revenue goal for each of the specified time periods to a user; Claim 16 and related text regarding a means for displaying a calendar filled with the distinctive codes in a pattern scheduling the categories according to the revenue goal for the specified time period]
While Knieper discloses the limitations above, it does not disclose 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, 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 the respective 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 the 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; and 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..
However, Kim discloses the following:
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 the respective time slot-location pair [see at least Col 4 lines 43-46 for reference to the disclosed system and method employing machine-learning techniques to identify trends and predict fulfilment center of camp demand to make assignments; Col 4 lines 62-67 and Col 5 lines 1-2 for reference to the system analyzing previous orders and performance of fulfilment centers or camps to determine forecasted demand and using machine-learning predictive models to determine which process has the most effective outcomes; Figure 8 and related text regarding item 806 ‘Forecast Demand for Multiple Camps and/or Fulfilment Centers Based on Predictive Model’]
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 [see at least Col 4 lines 43-46 for reference to the disclosed system and method employing machine-learning techniques to identify trends and predict fulfilment center of camp demand to make assignments; Col 4 lines 62-67 and Col 5 lines 1-2 for reference to the system analyzing previous orders and performance of fulfilment centers or camps to determine forecasted demand and using machine-learning predictive models to determine which process has the most effective outcomes; Figure 8 and related text regarding item 806 ‘Forecast Demand for Multiple Camps and/or Fulfilment Centers Based on Predictive Model’]
sending the suggested schedule to the picker client device [see at least Col 24 lines 24-29 for reference to the scheduling system generating scheduling notification GUIs for workers; Figure 2 and related text regarding item 224A & 224B ‘workers’; Figure 8 and related text regarding item 816 ‘Generate scheduling notifications GUIs for Administrator and Workers’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Kim. Doing so provides the users of the system with interactive tools to efficiently manage curriers and prioritize work, as stated by Kim (Col 4 lines 36-39).
While the combination of Knieper and Kim discloses the limitations above, they do not disclose 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, 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 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 the respective 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 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; and 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.
However, Vasagiri discloses the following:
receiving, from a picker client device associated with a picker of an online concierge system, 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 [see at least Paragraph 0033 for reference to workforce availability data identifying how many pickers and/or drivers are available over a future period of time; Paragraph 0053 for reference to database storing capacity data including store employee (e.g., pickers) schedules and delivery employee schedules for each of a plurality of locations; Paragraph 0076 for reference to a request for the plurality of timeslots being received by the web server and the timeslot determination device obtaining employee schedules and/or delivery employee schedules that correspond to one or more of the plurality of timeslots; Figure 3 and related text regarding item 380 ‘Capacity Management Engine’ and item 332 ‘Store Employee Schedules’; Figure 9A and related text regarding item 904 ‘OBTAIN WORKFORCE AVAILABILITY DATA FOR THE PLURALITY OF TIMESLOTS FOR THE PREVIOUS PERIOD’; Figure 9B and related text regarding item 956 ‘OBTAIN WORKFORCE AVAILABILITY DATA FOR THE PLURALITY OF TIMESLOTS FOR THE FUTURE DATE’]
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 the respective time slot-location pair [see at least Paragraph 0056 for reference to the slot forecasting engine obtaining a slot opening model to determine timeslot availability time for timeslots; Paragraph 0077 for reference to features being generated based on the historical timeslot data and the workforce availability data; Paragraph 0077 for reference to a timeslot capacity for each of the plurality of timeslots for the future date is determined based on applying a trained machine learning model to the generated features; Figure 9B and related text regarding item 958 ‘GENERATE FEATURES BASED ON HISTORICAL TIMESLOT DATA AND WORKFORCE AVAILABILITY DATA’ and item 960 ‘DETERMINE A TIMESLOT CAPACITY FOR EACH OF THE PLURALITY OF TIMESLOTS FOR THE FUTURE DATE BASED ON APPLYING A TRAINED MACHINE LEARNING MODEL TO THE HISTORICAL TIMESLOT DATA AND THE WORKFORCE AVAILABILITY DATA’]
wherein the first machine learning model is trained on a set of training examples using supervised learning [see at least Paragraph 0034 for reference to apply a trained machine learning model to the generated features to determine a timeslot capacity for each of the timeslots wherein the machine learning model is a supervised machine learning model that predicts a timeseries; Paragraph 0057 for reference to the slot forecasting engine applying slot capacity models to generated features to determine timeslot capacities for the timeslots, wherein the capacity models may be a supervised machine learning model that predicts a time-series]
the set of training examples including a set attributes of 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 the 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 [see at least Paragraph 0053 for reference to database storing capacity data including store employee (e.g., pickers) schedules and delivery employee schedules for each of a plurality of locations; Paragraph 0059 for reference to feature data including feature vectors for one or more features including features based on historical timeslot selections (e.g., pickup and delivery timeslot), timeslot demand over previous periods of time (e.g., slot requested), timeslot sales over previous periods of time (e.g., slot requested), timeslot sales over previous periods of time (e.g., purchase amount), and workforce availability; Paragraph 0075 for reference to a timeslot determination computing device training a machine learning model with the generated features; Figure 9A and related text regarding the method for training a machine learning model, including item 908 ‘TRAIN A MACHINE LEARNING MODEL WITH THE HISTORICAL TIMESLOT DATA AND THE WORKFORCE AVAILABILITY DATA’; Figure 9B and related text regarding item 958 ‘GENERATE FEATURES BASED ON HISTORICAL TIMESLOT DATA AND WORKFORCE AVAILABILITY DATA’ and item 960 ‘DETERMINE A TIMESLOT CAPACITY FOR EACH OF THE PLURALITY OF TIMESLOTS FOR THE FUTURE DATE BASED ON APPLYING A TRAINED MACHINE LEARNING MODEL TO THE HISTORICAL TIMESLOT DATA AND THE WORKFORCE AVAILABILITY DATA’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the machine learning techniques of Kim to include the supervised machine learning techniques of Vasagiri. Doing so not only learns an optimal number of slots that need to be opened, it also adaptively, without human - intervention, can make those adjustments on an occasional (e.g., daily basis) to the slot capacity so that discrepancy between the customer - demand for slots, and the ability to open the maximum number of slots for that hour given the constraint of store/fulfilment center picker workforce availability and other fulfilment related logistics challenges, is minimized, thereby leading to an optimal slot availability for customers where customer pick-up or order delivery times can be met, as stated by Vasagiri (Paragraph 0037).
While the combination of Knieper, Kim, and Vasagiri discloses the limitations above, they do not disclose 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 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 the 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; and 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.
However, Reiss discloses the following:
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 [see at least Col 5 lines 10-14 for reference to the service provider identifying available couriers within a threshold proximity to the merchant pickup location; Col 5 lines 52-57 for reference to each courier device including a GPS receiver or other location sensor to periodically provide updated location information to the service provider; Figure 6 and related text regarding item 602 ‘RECEIVE, FROM A PLURALITY OF COURIER DEVICES, OVER A PERIOD OF TIME, RESPECTIVE GEOGRAPHIC LOCATIONS OF THE COURIER DEVICES’; Figure 9 and related text regarding item 136 ‘courier device’]
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 [see at least Col 5 lines 10-14 for reference to the service provider identifying available couriers within a threshold proximity to the merchant pickup location based on the predicted courier travel times; Col 16 lines 59-67 for reference to the courier effort and/or payment determining logic including one or more computational models for determining predicted courier travel times to the merchant pickup location to the delivery location based on courier historic information; Col 17 lines 4-8 for reference to the courier effort and/or payment determining logic determining a confidence score for a prediction of how long it will take a courier to travel from a first point to a second point within the service region at a particular time; Col 18 lines 5-25 for reference to the use of the statistical models being trained using training data for predicting at least one of the predicted courier travel times or other times based on the confidence score exceeding a specified threshold of confidence; Figure 5 and related text regarding item 542 ‘Predicted Courier Travel Time For New Order’ and item 544 ‘Predicted Courier Other Time For New Order’]
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 the respective time-slot location pair [see at least Col 14 lines 31-34 for reference to target hourly rates being selected or adjusted based on suitable revenue models of the service for the service region; Col 16 lines 35-39 for reference to courier effort and/or payment determining logic for determining courier payment for one or more delivery jobs corresponding to one or more respective orders; Col 16 lines 39-45 for reference to the courier effort and/or payment determining logic including one or more algorithms, one or more computational models, a plurality of decision-making rules, or the like, configured to determine normalized distributions and/or aggregated past order information; Col 18 lines 37-40 for reference to the courier travel time rate and the courier other time rate being determined in advance based on a target revenue model of the service for the service region; Figure 5 and related text regarding item 536 ‘Courier Effort and/or Payment Determining Logic’]
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 [see at least Col 14 lines 31-34 for reference to target hourly rates being selected or adjusted based on suitable revenue models of the service for the service region; Col 16 lines 35-39 for reference to courier effort and/or payment determining logic for determining courier payment for one or more delivery jobs corresponding to one or more respective orders; Col 16 lines 39-45 for reference to the courier effort and/or payment determining logic including one or more algorithms, one or more computational models, a plurality of decision-making rules, or the like, configured to determine normalized distributions and/or aggregated past order information; Col 18 lines 37-40 for reference to the courier travel time rate and the courier other time rate being determined in advance based on a target revenue model of the service for the service region; Figure 5 and related text regarding item 536 ‘Courier Effort and/or Payment Determining Logic’]
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 [see at least Col 18 lines 28-36 for reference to the determined courier payment being calculated based on the predicted courier travel time and other time of the new order; Figure 5 and related text regarding item 546 ‘Courier Payment For Delivery Job Corresponding to the New Order’]
sending the suggested schedule to the picker client device [see at least Col 22 and related text regarding the computing device sending to the courier device information about the order; Figure 6 and related text regarding item 618 ‘SEND, TO THE COURIER DEVICE, INFORMATION ABOUT THE ORDER INCLUDING A COURIER PAYMENT AMOUNT THAT IS BASED ON THE FIRST PAYMENT AMOUNT PLUS THE SECOND PAYMENT AMOUNT; Figure 7 and related text regarding item 710 ‘SEND, TO A COURIER DEVICE, INFORMATION RELATED TO THE ORDER AND AN INDICATION OF THE COURIER PAYMENT AMOUNT’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Reiss. Doing so would assist in determining appropriate courier payment for particular delivery jobs, as stated by Reiss (Col 2 lines 32-34).
Regarding claim 20, the claim recites limitations already addressed by the rejection of claim 11. Regarding claim 20, Knieper teaches a computer system comprising a processor and a non-transitory computer readable storage medium storing instructions [Paragraph 0019 & Figure 5]. Therefore, claim 20 is rejected as being unpatentable over the combination of Knieper, Kim, Vasagiri, and Reiss.
Claim 12
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, regarding Claim 12, Knieper discloses the following:
wherein identifying the suggested schedule for achieving the goal is further based at least in part on a number of contiguous time slots included in the suggested schedule [see at least Paragraph 0013 for reference to the system arranging the categories in a pattern so that the sum of the lowest low-end (minimum) income generated is equal to or exceeds the revenue goal for the specified time period; Paragraph 0017 for reference to a single week of the schedule may be planned out for a typical dental practitioner using the patterned coding system established in the completion time and minimum income generated categories chart; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
Claim 13
While the combination of Knieper, Kim, and Reiss disclose the limitations above, Knieper does not disclose the first machine learning model is trained by: 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; and training the first machine learning model using supervised learning with the labeled plurality of attributes.
Regarding Claim 13, Kim discloses the following:
the first machine learning model is trained by: 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 [see at least Col 19 lines 59-65 for reference to the scheduling system performing operations of generating a predictive model based on the training data set associating request information and fulfilment centers; Figure 9 and related text regarding the predictive model training process]
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 [see at least Col 19 lines 59-65 for reference to the scheduling system performing operations of generating a predictive model based on the training data set associating request information and fulfilment centers and validating the predictive model using the validation data set; Figure 9 and related text regarding the predictive model training process]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Kim. Doing so provides the users of the system with interactive tools to efficiently manage curriers and prioritize work, as stated by Kim (Col 4 lines 36-39).
While Kim discloses the limitations above, it does not disclose training the first machine learning model using supervised learning with the labeled plurality of attributes.
However, Vasagiri discloses the following:
training the first machine learning model using supervised learning with the labeled plurality of attributes [see at least Paragraph 0034 for reference to apply a trained machine learning model to the generated features to determine a timeslot capacity for each of the timeslots wherein the machine learning model is a supervised machine learning model that predicts a timeseries; Paragraph 0057 for reference to the slot forecasting engine applying slot capacity models to generated features to determine timeslot capacities for the timeslots, wherein the capacity models may be a supervised machine learning model that predicts a time-series]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the machine learning techniques of Kim to include the supervised machine learning techniques of Vasagiri. Doing so not only learns an optimal number of slots that need to be opened, it also adaptively, without human - intervention, can make those adjustments on an occasional (e.g., daily basis) to the slot capacity so that discrepancy between the customer - demand for slots, and the ability to open the maximum number of slots for that hour given the constraint of store/fulfilment center picker workforce availability and other fulfilment related logistics challenges, is minimized, thereby leading to an optimal slot availability for customers where customer pick-up or order delivery times can be met, as stated by Vasagiri (Paragraph 0037).
Claim 14
While the combination of Knieper, Kim, and Reiss disclose the limitations above, Knieper does not disclose wherein the second machine learning model is trained by: receiving a plurality of attributes associated with a plurality of time slot-location pairs; receiving, for each of the plurality of time slot-location pairs, a label indicating an amount of earnings for a picker who serviced an order placed with the online concierge system for a corresponding time slot-location pair; and training the second machine learning model using supervised learning with the labeled plurality of attributes.
Regarding Claim 14, Reiss discloses the following:
wherein the second machine learning model is trained by: receiving a plurality of attributes associated with a plurality of time slot-location pairs [see at least Col 17 lines 64-67 and Col 18 lines 1-6 for reference to the one or more computational models used by the courier effort and/or payment determining logic including one or more trained statistical models that account for numerous pieces of information included in the past order information, as well as current information, such as time, day, and date information, map information, traffic information, weather information, local event information, current and recent courier locations, and the like]
receiving, for each of the plurality of time slot-location pairs, a label indicating an amount of earnings for a picker who serviced an order placed with the online concierge system for a corresponding time slot-location pair [see at least Col 14 lines 31-34 for reference to target hourly rates being selected or adjusted based on suitable revenue models of the service for the service region; Col 16 lines 35-39 for reference to courier effort and/or payment determining logic for determining courier payment for one or more delivery jobs corresponding to one or more respective orders]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the machine learning techniques of Reiss. Doing so would assist in determining appropriate courier payment for particular delivery jobs, as stated by Reiss (Col 2 lines 32-34).
While Reiss discloses the limitations above, it does not disclose training the second machine learning model using supervised learning with the labeled plurality of attributes.
However, Vasagiri discloses the following:
training the second machine learning model using supervised learning with the labeled plurality of attributes [see at least Paragraph 0034 for reference to apply a trained machine learning model to the generated features to determine a timeslot capacity for each of the timeslots wherein the machine learning model is a supervised machine learning model that predicts a timeseries; Paragraph 0057 for reference to the slot forecasting engine applying slot capacity models to generated features to determine timeslot capacities for the timeslots, wherein the capacity models may be a supervised machine learning model that predicts a time-series]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the machine learning techniques of Kim to include the supervised machine learning techniques of Vasagiri. Doing so not only learns an optimal number of slots that need to be opened, it also adaptively, without human - intervention, can make those adjustments on an occasional (e.g., daily basis) to the slot capacity so that discrepancy between the customer - demand for slots, and the ability to open the maximum number of slots for that hour given the constraint of store/fulfilment center picker workforce availability and other fulfilment related logistics challenges, is minimized, thereby leading to an optimal slot availability for customers where customer pick-up or order delivery times can be met, as stated by Vasagiri (Paragraph 0037).
Claim 15
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, regarding Claim 15, Knieper discloses the following:
wherein the suggested schedule is sent to the picker client device in association with the total estimated amount of earnings [see at least Paragraph 0017 for reference to for each day in which tasks are completed, the minimum income generated total can be calculated; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
Claim 16
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, regarding Claim 16, Knieper discloses the following:
wherein a goal is selected from the group consisting of: a maximized amount of earnings and a target amount of earnings [see at least Paragraph 0013 for reference to the scheduling method is intended for use by a business or service provider for achieving revenue objectives in a given time period and includes the steps of recording a revenue goal for a specified time period and determining the number of working days within the specified time period for which the revenue goal was determined; Figure 1 and related text regarding item 110 ‘Determine a revenue (income) goal for a specified time period (e.g., week, month, year)’]
Claim 17
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, Knieper does not disclose wherein the one or more costs are based at least in part on one or more selected from a group consisting of: a cost of fuel and a cost of a toll.
Regarding Claim 17, Reiss discloses the following:
wherein the one or more costs are based at least in part on one or more selected from a group consisting of: a cost of fuel and a cost of a toll [see at least Col 2 lines 48-67 for reference to features for quantifying courier effort including gasoline expenditures]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the cost consideration of Reiss. Doing so would assist in determining appropriate courier payment for particular delivery jobs, as stated by Reiss (Col 2 lines 32-34).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Knieper (U.S 2009/0043630 A1) in view of Kim (U.S 10,769,588 B1) in view of Vasagiri (U.S 2022/0245595 A1) in view of Reiss (U.S 10,346,889 B1), as applied in claim 1, in view of Ezry (U.S 2016/0140463 A1).
Claim 10
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, they do not disclose generating a heat map based at least in part on the estimated amount of earnings for each time slot-location pair; and sending the heat map to the picker client device, wherein sending the heat map to the picker client device causes the picker client device to display the heat map.
Regarding Claim 10, Ezry discloses the following:
generating a heat map based at least in part on the estimated amount of earnings for each time slot-location pair [see at least Paragraph 0097 for reference to the decision support system tracking performances in real time in a performance measure drift heat map; Paragraph 0097 for reference to zones in the heat map displaying current levels of performance, new actions that can be taken, and expected performance; Paragraph 0097 for reference to an engine executes new compensation actions (for example, adjusting employee wages by the optimized set (X) of factors), and continue with monitoring and model adaptation steps]
sending the heat map to the picker client device, wherein sending the heat map to the picker client device causes the picker client device to display the heat map [see at least Paragraph 0047 for reference to the computer server communicating with one or more external device; Paragraph 0097 for reference to the GUI display device of the heat map; Figure 1 and related text regarding item 14 ‘external devices’ and item 24 ‘display’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the heat map display of Ezry. Doing so can establish a more nuanced relationship between compensation action and employee retention, which is also useful in modeling relationships between retention and productivity, as stated by Ezry (Paragraph 0095).
Claim(s) 8-9 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Knieper (U.S 2009/0043630 A1) in view of Kim (U.S 10,769,588 B1) in view of Vasagiri (U.S 2022/0245595 A1) in view of Reiss (U.S 10,346,889 B1), as applied in claims 1 and 11, in view of Lo (U.S 2011/0119604 A1).
Claim 8
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, they do not disclose wherein generating the set of suggested schedules is based at least in part on a greedy partial enumeration algorithm.
Regarding Claim 8, Lo discloses the following:
wherein generating the set of suggested schedules is based at least in part on a greedy partial enumeration algorithm [see at least Paragraph 0014 for reference to the scheduling tool enables the system to evaluate the appropriateness of scheduling options according to user defined objectives and criteria through the processing of Objective Functions; Paragraph 0169 for reference to the system attempts to produce a solution using an algorithm supplied by the scheduling engine DLL, such as a Greedy Algorithm]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the greedy algorithm Lo. Doing so enables the system to determine and apply appropriate scheduling options, as stated by Lo (Paragraph 0014).
Claim 9
While the combination of Knieper, Kim, Vasagiri, Reiss, and Lo disclose the limitations above, regarding Claim 9, Knieper discloses the following:
identifying one or more time slot-location pairs from the set of time slot-location pairs, wherein the estimated amount of earnings associated with each of the one or more time slot-location pairs is less than a threshold estimated amount of earnings [see at least Paragraph 0013 for reference to the system arranging the categories in a pattern so that the sum of the lowest low-end (minimum) income generated is equal to or exceeds the revenue goal for the specified time period; Paragraph 0017 for reference to the minimum income generated total can be calculated, providing an easy way to calculate weekly or other shorter term totals thereafter, in an attempt to reach the ultimate longer-term revenue goal; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
excluding the one or more time slot-location pairs from the set of suggested schedules [see at least Paragraph 0017 for reference to in order to achieve the income or revenue goal, the tasks should be arranged according to completion time and minimum income generated so that the goal may be achieved or exceeded for the specified time period; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
Claim 18
While the combination of Knieper, Kim, Vasagiri, and Reiss disclose the limitations above, they do not disclose wherein generating the set of suggested schedules is based at least in part on a greedy partial enumeration algorithm.
Regarding Claim 18, Lo discloses the following:
wherein generating the set of suggested schedules is based at least in part on a greedy partial enumeration algorithm [see at least Paragraph 0014 for reference to the scheduling tool enables the system to evaluate the appropriateness of scheduling options according to user defined objectives and criteria through the processing of Objective Functions; Paragraph 0169 for reference to the system attempts to produce a solution using an algorithm supplied by the scheduling engine DLL, such as a Greedy Algorithm]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the scheduling method of Knieper to include the greedy algorithm Lo. Doing so enables the system to determine and apply appropriate scheduling options, as stated by Lo (Paragraph 0014).
Claim 19
While the combination of Knieper, Kim, Vasagiri, Reiss, and Lo disclose the limitations above, regarding Claim 19, Knieper discloses the following:
identifying one or more time slot-location pairs from the set of time slot-location pairs, wherein the estimated amount of earnings associated with each of the one or more time slot-location pairs is less than a threshold estimated amount of earnings [see at least Paragraph 0013 for reference to the system arranging the categories in a pattern so that the sum of the lowest low-end (minimum) income generated is equal to or exceeds the revenue goal for the specified time period; Paragraph 0017 for reference to the minimum income generated total can be calculated, providing an easy way to calculate weekly or other shorter term totals thereafter, in an attempt to reach the ultimate longer-term revenue goal; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
excluding the one or more time slot-location pairs from the set of suggested schedules [see at least Paragraph 0017 for reference to in order to achieve the income or revenue goal, the tasks should be arranged according to completion time and minimum income generated so that the goal may be achieved or exceeded for the specified time period; Figure 1 and related text regarding item 190 ‘Arrange the scheduled tasks according to the completion time and [cost] lowest low-end income generated so that the revenue goal is achieved or exceeded for the specified time period’; Figure 3 and related text regarding ‘Example Completion Time and Minimum Income Categories Chart’; Figure 4 and related text regarding the Example Calendar/Schedule Template’ including at the bottom of each day within the example a ‘Total’ category with total estimated earnings of the day based on the determination of Figure 3]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2022/0083951 A1
Brager et al.
SYSTEM AND METHODS FOR GENERATING EMPLOYEE SCHEDULES
US 2007/0294344 A1
Mohan, Prabhuram
Automatic Scheduling System
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3625