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 .
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.
Step 1 analysis for all claims:
In the instant case, claims 1-7, 21 are directed to a process, claims 8-14 are directed to manufacture and claims 15-20 are directed to a system. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 1:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
generating a prompt for input to a machine-learned language model, the prompt specifying at least the list of items and a request to infer one or more key items in the order; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses coming up with a question to ask the model about regarding key items of a list of items.
parsing the response from the model serving system to extract information identifying a subset of items as being the one or more key items of the order; and and As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses using parts of the response from the model to identify a part of the item list as key items.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving, from a client device, an order including a list of items ordered from a user of the client device; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
providing the prompt to a model serving system for execution by the machine-learned language model for execution; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
sending, to another client device, the order of the list of items and one or more indications that the subset of items are key items of the order,; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
the sending further comprising causing display of an interface including the list of items and the one or more indications on the another client device.; Which amount amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving, from a client device, an order including a list of items ordered from a user of the client device; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
providing the prompt to a model serving system for execution by the machine-learned language model for execution; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
sending, to another client device, the order of the list of items and one or more indications that the subset of items are key items of the order,; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
the sending further comprising causing display of an interface including the list of items and the one or more indications on the another client device.; These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
for each recipe, annotating one or more key ingredients based on matching the list of ingredients with the title of the recipe, wherein the prompt includes the annotated recipes.; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses choosing the key ingredients out of a list of ingredients using other recipes and the question being asked the ML model involving the recipes.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
obtaining a set of recipes from a database, each recipe including a title and a list of ingredients for the recipe; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
obtaining a set of recipes from a database, each recipe including a title and a list of ingredients for the recipe; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
aggregating a list of items in the order history; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses making a list of ingredients using the order history
identifying a subset of items in the order history as key items; for each of one or more previous orders of the user, annotating one or more key items in the order; and; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses choosing the key items from the order history and annotating those from the precious orders.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
obtaining order history for a user; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
providing the prompt comprises including the annotated orders in the prompt. which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
obtaining order history for a user; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
providing the prompt comprises including the annotated orders in the prompt.; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
generating another interface on the client device presenting the one or more key items; and; These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
for each key item, receiving a positive indication or a negative indication on whether the key item is an actual key item from a user. which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
generating another interface on the client device presenting the one or more key items; and; These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
for each key item, receiving a positive indication or a negative indication on whether the key item is an actual key item from a user.; This limitation is directed to receiving input at an interface on a computing device, wherein the input comprises a dataset, an analysis for the dataset, and an output medium which amounts to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
generating a training dataset including a set of data instances, wherein a data instance includes inputs comprising the list of items of the order and expected outputs; comprising key items for which positive indication was received from the user; and; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
fine-tuning parameters of the machine-learned model using the training dataset.; which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
generating a training dataset including a set of data instances, wherein a data instance includes inputs comprising the list of items of the order and expected outputs; comprising key items for which positive indication was received from the user; and; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
fine-tuning parameters of the machine-learned model using the training dataset; Fine-tuning which is training is at a high-level of generality with no detail of the training process such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
generating a training dataset including a set of data instances; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
wherein a data instance includes inputs comprising the list of items of the order, wherein the items include edible and non-edible items.; amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
generating a training dataset including a set of data instances; As discussed above, the additional elements amount to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
wherein a data instance includes inputs comprising the list of items of the order, wherein the items include edible and non-edible items.; As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving an interaction with a positive user interface element presented to the user or receiving an indication of no interaction with a negative user interface element presented to the user.; which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving an interaction with a positive user interface element presented to the user or receiving an indication of no interaction with a negative user interface element presented to the user.; This limitation is directed to receiving input at an interface on a computing device, wherein the input comprises a dataset, an analysis for the dataset, and an output medium which amounts to extra-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8:
Claim 8 recites substantially similar limitations other than additional elements for claim 1 and is therefore rejected on the same basis.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A non-transitory computer readable storage medium comprising stored program code instructions, the instructions when executed causes a processing system to:; which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
A non-transitory computer readable storage medium comprising stored program code instructions, the instructions when executed causes a processing system to:; Computer elements are recited at a high-level of generality such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 9:
Claim 9 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 10:
Claim 10 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 11:
Claim 1 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Claim 12:
Claim 16 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Claim 13:
Claim 16 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Claim 14:
Claim 14 recites substantially similar limitations for claim 7 and is therefore rejected on the same basis.
Claim 15:
Claim 15 recites substantially similar limitations other than additional elements for claim 1 and is therefore rejected on the same basis.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A computer system comprising: a processor; and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to: which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
A computer system comprising: a processor; and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to: Computer elements are recited at a high-level of generality such and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 16:
Claim 16 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 17:
Claim 17 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 18:
Claim 18 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Claim 19:
Claim 19 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Claim 20:
Claim 20 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Claim 21:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the another client device is a client device of a user fulfilling the order. amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the another client device is a client device of a user fulfilling the order.; As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1, 2, 8, 9, 15, 16, and 21 are rejected under 35 U.S.C 103 as being unpatentable over Tate et al. (US 20220044299 A1, hereinafter Tate) in view of Baumback et al. (US 20210043108 A1, hereinafter Baumback).
Claim 1:
Tate teaches:
A method comprising:
Tate [0059] Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps.
EN: this just reads on a method for the following teachings
receiving, from a client device, an order including a list of items ordered from a user of the client device;
Tate [0017] The environment 100 includes an online concierge system 102. The online concierge system 102 is configured to receive orders from one or more customers 104 (only one is shown for the sake of simplicity). An order specifies a list of goods (items or products) to be delivered to the customer 104. The order also specifies the location to which the goods are to be delivered, and a time window during which the goods should be delivered. In some embodiments, the order specifies one or more retailers from which the selected items should be purchased. The customer 104 may use a customer mobile application (CMA) 106 to place the order; the CMA 106 is configured to communicate with the online concierge system 102.
EN: order from customer is placed through the CMA (106) and received by the online concierge system (102)
generating a prompt for input to a machine-learned … model,
Tate [0038] The availability module 408 also receives one or more warehouse locations designated by the customer 104 via the customer ordering interface either directly from the CMA 106 or from the query module 400. For each warehouse location, the availability module 408 inputs each item in the set of items to a prediction model 410. The prediction model 410 is a machine learning model trained to predict a probability that an item is available at a warehouse location.
EN: using the query as the input reads on generating input for ML model
providing the prompt to a model serving system for execution by the machine-learned … model for execution;
Tate [0038] The availability module 408 also receives one or more warehouse locations designated by the customer 104 via the customer ordering interface either directly from the CMA 106 or from the query module 400. For each warehouse location, the availability module 408 inputs each item in the set of items to a prediction model 410. The prediction model 410 is a machine learning model trained to predict a probability that an item is available at a warehouse location.
EN: inputting to the ML model reads on providing the prompt to model serving system; availability module 408 reads on model serving system as it interacts with the system
receiving, from the model serving system, a response generated by executing the machine-learned … model on the prompt;
[0038] The availability module 408 receives a probability of availability for each item at each of the one or more warehouse locations from the prediction model 410.
EN: this passages shows that the model serving system receives the answer from the model which reads on a response from the ML model
[0039] The availability module 408 sends product categories with low availability and the items mapped to those product categories to the description module 414.
EN: description module 414 receives this response
parsing the response from the model serving system to extract information identifying a subset of items
Tate [0039] The availability module 408 receives a probability of availability for each item at each of the one or more warehouse locations from the prediction model 410. For each assigned product category received from the category module 404, the availability module 408 determines a total predicted likelihood of availability for the product category based on the predicted likelihood for each item assigned to the product category. …
If the total predicted likelihood is below a threshold value for a product category, the availability module 408 determines that the product category has a low availability. The availability module 408 sends product categories with low availability and the items mapped to those product categories to the description module 414.
EN: products with low availability reads on a subset of items
sending, to another device, the order of the list of items…, the sending further comprising causing display of an interface including the list of items … to the another client device
Tate [0029] FIG. 3B is a block diagram of the picker mobile application (PMA) 112, according to one embodiment. The picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet. The PMA 112 may be accessed through an app running on the mobile client device or through a website accessed in a browser.
EN: the picker’s device reads on another client device
[0030] The PMA 112 also includes a system communication interface 324, which interacts with the online concierge system 102. For example, the system communication interface 324 receives information from the online concierge system 102 about the items of an order, such as when a customer 104 updates an order to include more or fewer items. The system communication interface may receive notifications and messages from the online concierge system 102 indicating information about an order or communications from a customer 104. The system communication interface 324 may send this information to the order interface engine 328, which generates a picker order interface.
EN: the ingredients that were ordered by the customer is displayed to the picker through a the PMA which is accessed using the picker’s device which reads on another client device
Tate does not explicitly teach an LLM, however Baumback teaches:
language model
Baumback [0059] For example, the natural language processing engine 180 may determine that a particular recipe stage in a digital recipe indicates that a steak should be seared on both sides for 30 seconds. Based on the keywords “steak” and/or “seared” extracted from the recipe stage of the digital recipe, the natural language processing engine 180 may match these keywords to a known and detailed time, temperature, key ingredient, and food manipulation instruction (e.g., an instruction describing how the steak should be manipulated), which may be utilized to facilitate the cooking and/or food preparation process.
EN: natural language processing reads on language model
the prompt specifying … a request to infer one or more key items …;
Baumback [0059] For example, the natural language processing engine 180 may determine that a particular recipe stage in a digital recipe indicates that a steak should be seared on both sides for 30 seconds. Based on the keywords “steak” and/or “seared” extracted from the recipe stage of the digital recipe, the natural language processing engine 180 may match these keywords to a known and detailed time, temperature, key ingredient, and food manipulation instruction (e.g., an instruction describing how the steak should be manipulated), which may be utilized to facilitate the cooking and/or food preparation process.
EN: Tate teaches a prompt that is fed into the ML model and this model specifically can be used for identifying a key ingredient which reads on always a request for key item; each keyword can be matched to a key ingredients which means multiple multiples keywords can read on key ingredients
identifying … items as being the one or more key items of the order; and
Baumback [0072] For example, the server 160 may assist in processing loads handled by the various devices in the system 100, such as, but not limited to, obtaining digital recipes including digital content; parsing the recipes for keywords to identify one or more recipe stages of the recipes; identifying cooking ingredients in each recipe stage; identifying which recipe stages correspond to cooking stages; identifying key ingredients in each cooking stage that are critical in controlling the cooking time; determining the cooking temperature in each cooking stage based on rules;
EN: Tate teaches a prompt that is fed into the ML model and Baumback teaches identifying key ingredients which reads on key items; server 160 helps the various devices in system 100 which includes natural language engine 180; this passages shows that one or more key ingredients are abled to be identified
one or more indications that the subset of items are key items
Baumback [0072] For example, the server 160 may assist in processing loads handled by the various devices in the system 100, such as, but not limited to, obtaining digital recipes including digital content; parsing the recipes for keywords to identify one or more recipe stages of the recipes; identifying cooking ingredients in each recipe stage; identifying which recipe stages correspond to cooking stages; identifying key ingredients in each cooking stage that are critical in controlling the cooking time; determining the cooking temperature in each cooking stage based on rules;
EN: identifying they are key ingredients reads on indicating that those items are key items
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of determining items for orders on an online concierge system of Tate with the method of identifying key items by natural language processing of Baumback in order to improve efficiency and accuracy.
Baumback [0065] In certain embodiments, the system 100 may also include a learning engine 185. The learning engine 185 may be software, hardware, or a combination thereof, and may be supported by any suitable machine learning and/or artificial intelligence algorithms The learning engine 185 may be a program, system, and/or device that determines patterns and/or associations with regard to recipes, parsed text and/or content from recipes, recipe stages within recipes, favorite types of foods and/or ingredients of the first and/or second users 101, 110, preferred cooking styles, preferred cooking utensils and/or devices, the types of media content that the first and/or second users 101, 110 prefer with their recipes, food textures preferred by the first and/or second users 101, 110, a level of doneness for food items 130 preferred by the first and/or second users 101, 110, any other learnable information, or any combination thereof. The learning engine 185 may allow for improved efficiency and accuracy of the system 100, while enabling more advanced processing of information traversing the system 100.
Claim 2:
The combination of Tate and Baumback teaches all of the limitations of claim 1 as cited above.
Tate teaches key ingredients, but does not explicitly teach recipes, however Baumback further teaches:
obtaining a set of recipes from a database, each recipe including … a list of ingredients for the recipe;
Baumback [0025] The interactive electronic cookbook 106 may provide users and automated cooking appliances with access to recipes from not only traditional printed formats, but also from digital text files of recipes.
EN: interactive electronic cookbook reads on database
[0026] The interactive electronic cookbook 106 may provide expert guidance with regard to the preparation of ingredients before cooking to assist the first user 101 in achieving optimal results.
EN: this reads on displaying all the ingredients including the key items
The combination of Tate and Baumback does not explicitly teach, but Uchida teaches:
for each recipe, annotating one or more key ingredients based on matching … with the title of the recipe, wherein the prompt includes the annotated recipes.
Uchida [0057] First, in Step (hereinafter abbreviated as S) 11, the keyword obtaining unit 15 obtains the keyword "pasta" from the user terminal 30 via the communication unit 11. Next, the recipe extracting unit 16 extracts, from the recipe information DB 14a, a plurality of recipes (recipe IDs) in which the keyword "pasta" is included in recipe information (S12). Next, the category identifying unit 17 identifies, for each recipe (recipe ID) extracted by the recipe extracting unit 16, which category includes the keyword "pasta" (S13).
[0058] Next, the evaluation value determining unit 18 determines, for each recipe (recipe ID) extracted by the recipe extracting unit 16, an evaluation value based on the result of the identification by the category identifying unit 17 (S14). For example, the evaluation value determining unit 18 determines the evaluation value E1 of the category "title" as "2" when the keyword "pasta" is included in "title", determines the evaluation value E2 of the category "dish name" as "3" when the keyword "pasta" is included in "dish name", and determines the evaluation value E4 of the category "tag" as "1" when the keyword "pasta" is included in "tag". The evaluation value determining unit 18 determines the evaluation value E3 of the category "ingredient" as one of "1" to "5" that is determined based on the place in order of the keyword in the ingredient field, when the keyword "pasta" is included in "ingredient".
EN: evaluation value reads on annotation; this passage reads on a method that starts with inputting keywords and then finding recipes that has those keywords in the recipes which reads on annotating the recipes; if the keyword which reads on ingredient is found in the title it gives it a certain evaluation value
[0049] The evaluation value determining unit 18 thus determines the evaluation value of the category "ingredient" based on the place in order of an ingredient. In this way, a recipe is given a high evaluation value when an ingredient in question has a high importance to the recipe.
EN: the evaluation values how important this ingredient is
each recipe including a title
EN: as taught above there is a title for each recipe if one of the evaluation values is calculated using the title of the recipe
Claim 8:
Claim 8 recites substantially similar limitations other than a non-transitory computer readable storage medium for claim 1 and is therefore rejected on the same basis. Tate further teaches:
A non-transitory computer readable storage medium comprising stored program code instructions, the instructions when executed causes a processing system to
Tate [0058] The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored on a computer readable medium that can be accessed by the computer. Such a computer program may be stored in a non-transitory computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of computer-readable storage medium suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
Claim 9:
Claim 9 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 15:
Claim 15 recites substantially similar limitations other than a computer system for claim 1 and is therefore rejected on the same basis. Tate further teaches:
A computer system comprising:
Tate [0056] Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.
a processor;
Tate [0058] Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to:
Tate [0058] The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored on a computer readable medium that can be accessed by the computer. Such a computer program may be stored in a non-transitory computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of computer-readable storage medium suitable for storing electronic instructions, and each coupled to a computer system bus.
Claim 16:
Claim 16 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Claim 21:
The combination of Tate and Baumback teaches all of the limitations of claim 1 as cited above, and Tate further teaches:
wherein the another client device is a client device of a user fulfilling the order.
Tate [0029] FIG. 3B is a block diagram of the picker mobile application (PMA) 112, according to one embodiment. The picker 108 accesses the PMA 112 via a mobile client device, such as a mobile phone or tablet. The PMA 112 may be accessed through an app running on the mobile client device or through a website accessed in a browser.
EN: picker reads on user filling the order
Claims 3, 10, and 17 are rejected under 35 U.S.C 103 as being unpatentable over Tate in view of Baumback in further view of Uchida et al. (US 20150066909 A1, hereinafter Uchida).
Claim 3:
The combination of Tate and Baumback teaches all of the limitations of claim 1 as cited above, and Tate further teaches:
obtaining order history for a user and aggregating a list of items in the order history;
Tate [0021] The order fulfillment engine 206 also determines replacement options for items in an order. For each item in an order, the order fulfillment engine 206 may retrieve data describing items in previous orders facilitated by the online concierge system 102, previously selected replacement options for that item, and similar items.
EN: the item data in previous orders reads on aggregated list of items
[0021] In some embodiments, the order fulfillment engine 206 only uses data for the customer 104 related to the order to suggest replacement options.
EN: previous shopping history reads on order history for a user;
identifying a subset of items in the order history as key items;
Tate [0021] Based on this data, the order fulfillment engine 206 creates a set of replacement options for each item in the order comprising the items from the data.
EN: this reads on a set of items from the previous order history data which reads on a subset of items in order history
[0021] The order fulfillment engine 206 ranks replacement options in the set to determine which items to display to the customer 104. In some embodiments, the order fulfillment engine 206 may rank the replacement options by the number of previous orders containing the replacement option or user quality ratings gathered by the online concierge system 102.
EN: ranking by importance reads on key items; Although Tate does not explicitly teach identifying key items in a current order, it does teach key items in order history.
for each of one or more previous orders of the user, annotating one or more key items in the order; and
Tate [0021] The order fulfillment engine 206 also determines replacement options for items in an order. For each item in an order, the order fulfillment engine 206 may retrieve data describing items in previous orders facilitated by the online concierge system 102, previously selected replacement options for that item, and similar items.
EN: this passage describes annotating all of items in the current order by finding replacement options from the previous orders; this means the identified key items from earlier get annotated
providing the prompt comprises including the … orders in the prompt.
Tate [0021] The order fulfillment engine 206 ranks replacement options in the set to determine which items to display to the customer 104. In some embodiments, the order fulfillment engine 206 may rank the replacement options by the number of previous orders containing the replacement option or user quality ratings gathered by the online concierge system 102.
EN: the order fulfillment engine is always prompted to do this which means it includes the previous orders
Tate doesn’t explicitly teach anything annotated in the prompt, however Uchida teaches annotated recipes in the prompt:
providing the prompt comprises including the annotated … in the prompt.
Uchida [0063] As described above, the recipe information providing system according to the first embodiment displays recipes based on evaluation values that are determined based on the place in order of a given ingredient (here, an ingredient associated with a keyword) in the ingredient field. The recipe information providing system can therefore prevent recipes that are, for example, ones not desired by the user from being displayed at the top of the search result page. In the case where the keyword is "pasta", for example, a recipe "hamburger steak" in which "pasta" is included in its ingredients and the importance of "pasta" as an ingredient is low is given a low evaluation value and accordingly is not displayed at the top of the search result page. "Pasta" recipes desired by the user are displayed preferentially at the top of the search result page in this manner, and the user can thus view desired recipes.
EN: this reads on annotated recipes being processed so that it can be displayed which means the prompt for this part is to rank the annotated recipes
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of determining key items for orders on an online concierge system by NLP of Tate and Baumback with the method of ranking recipes based on their ingredient importance of Uchida in order to present more desired recipes to the user.
Uchida [0063] As described above, the recipe information providing system according to the first embodiment displays recipes based on evaluation values that are determined based on the place in order of a given ingredient (here, an ingredient associated with a keyword) in the ingredient field. … "Pasta" recipes desired by the user are displayed preferentially at the top of the search result page in this manner, and the user can thus view desired recipes.
Claim 10:
Claim 10 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claim 17:
Claim 17 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Claims 4-7, 11-14, 18-20 are rejected under 35 U.S.C 103 as being unpatentable over Tate in view of Baumback in further view of Reynolds et al. (US 20230419168 A1, hereinafter Reynolds).
Claim 4:
The combination of Tate and Baumback teaches all of the limitations of claim 1 as cited above, and Tate further teaches:
generating another interface on the client device presenting the one or more key items; and
Tate [0030] The PMA 112 also includes a system communication interface 324, which interacts with the online concierge system 102. For example, the system communication interface 324 receives information from the online concierge system 102 about the items of an order, such as when a customer 104 updates an order to include more or fewer items. The system communication interface may receive notifications and messages from the online concierge system 102 indicating information about an order or communications from a customer 104. The system communication interface 324 may send this information to the order interface engine 328, which generates a picker order interface.
EN: this displays the key items because it displays all the items from the order
The combination of Tate and Baumback does not explicitly teach user feedback, however, Reynolds further teaches:
for each … item, receiving a positive indication or a negative indication on whether the … item is an actual … item from a user.
Reynolds [0027] Machine learning model 113 is configured to receive user feedback from HMI environment 101. The user feedback may be used to advance the training state of machine learning model 112. For example, HMI 103 may display user prompt 106 in environment view 104. User prompt 106 may comprise toggles for a user to indicate their approval or disapproval of machine learning output 105. For example, the toggles may allow a user to accept/reject machine learning predictions generated by model 113 using on screen features of environment view 104. Computing device 102 may transfer user feedback received via HMI 103 for delivery to machine learning model 113. Repository 112 may utilize the user feedback to advance the training state of model 113. For example, the machine learning interface application hosted by computing device 102 may adjust the weights of feature vectors based on the user feedback and transfer the weighted feature vectors for delivery to model 113.
EN: approval and disapproval from the user reads on positive and negative indication from user; key item is already taught above
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method of determining key items for orders on an online concierge system by NLP of Tate and Baumback with the method of incorporating user feedback loop into the training of an ML model of Reynolds in order to improve performance of the model over time.
Reynolds [0021] Machine learning models may be deployed on premises in an industrial automation environment or off-site. For example, the machine learning model may be implemented in a data science environment and possess data pipelines to receive industrial data from industrial controllers and transfer machine learning operational data for display on HMIs. Machine learning models undergo training periods to improve their performance over time. In an implementation, an operator may view the training state of a machine learning model on an HMI and responsively input user feedback to advance the training state of the model. This training procedure may be cyclical. Thus, the machine learning assets disclosed herein may form a feedback loop to periodically advance their training based on user feedback.
Claim 5:
The combination of Tate, Baumback, and Reynolds teaches all of the limitations of claim 4 as cited above, and Tate further teaches:
generating a training dataset including a set of data instances, wherein a data instance includes inputs comprising the list of items of the order and expected outputs
Tate [0038] The training module 412 trains the prediction model on data describing items included in previous orders, whether each item in each previous order was picked, a warehouse location associated with the previous orders, and a plurality of characteristics associated with each of the items.
EN: the precious order data reads on generated training data as it is put together with the output; the input is list of items and the prediction is an expected output of the items
Tate does not explicitly teach the training data comprising key items, however Baumback teaches:
comprising key items
Baumback [0059] In certain embodiments, the natural language processing engine 180 may determine associations and similarities between the parsed text and content obtained from a digital recipe with terms, keywords and/or identifiers stored in the system 100, such as by recognizing patterns in the attributes that correspond to the text and content, by determining synonyms for the text and/or content, by recognizing images and/or video (or other content) having similarities to the media content stored in the system 100, by performing any other natural language processing capabilities, or any combination thereof.
EN: the data has digital receipts with keywords stored in the system which reads on key items
The combination of Tate and Baumback does not explicitly teach user feedback incorporated in the training, however, Reynolds further teaches:
data instance includes … outputs comprising … items … for which positive indication was received from the user; and
Reynolds [0021] In an implementation, an operator may view the training state of a machine learning model on an HMI and responsively input user feedback to advance the training state of the model. This training procedure may be cyclical. Thus, the machine learning assets disclosed herein may form a feedback loop to periodically advance their training based on user feedback.
EN: this reads on fine-tuning parameters of the machine-learned model using the training dataset.
Reynolds [0071] In some examples, the machine learning interface application hosted by HMI 311 may weight and/or adjust the weights of feature vectors using additional metrics beside user feedback supplied via user interface 313. For example, HMI 311 may track user actions within HMI 311. The user actions may comprise user inputs that reject/accept suggestions generated by model 324, user actions to affect ones of OEM devices 141-145 (e.g., manual tuning or slowing production speed), and the like. The interface application may weight and/or adjust the weights of the feature vectors to reflect the user actions.
EN: this reads on fine tuning as its adjusting based on user after being trained
Claim 6:
The combination of Tate, Baumback, and Reynolds teaches all of the limitations of claim 4 as cited above, and Tate further teaches:
generating a training dataset including a set of data instances, wherein a data instance includes inputs comprising the list of items of the order, wherein the items include edible and non-edible items.
Tate [0038] The training module 412 trains the prediction model on data describing items included in previous orders, whether each item in each previous order was picked, a warehouse location associated with the previous orders, and a plurality of characteristics associated with each of the items.
EN: the precious order data reads on generated training data; the input is list of items and the prediction is an expected output of the items
[0018] The retailers 110 may be physical retailers, such as grocery stores, discount stores, department stores, etc., or non-public warehouses storing items that can be collected and delivered to customers 104.
EN: the items can be edible and non-edible as items come from grocery stores and other stores
Claim 7: (4)
The combination of Tate, Baumback, and Reynolds teaches all of the limitations of claim 4 as cited above. The combination of Tate and Baumback does not explicitly teach, but Reynolds further teaches:
receiving an interaction with a positive user interface element presented to the user or receiving an indication of no interaction with a negative user interface element presented to the user.
Reynolds [0071] In some examples, the machine learning interface application hosted by HMI 311 may weight and/or adjust the weights of feature vectors using additional metrics beside user feedback supplied via user interface 313. For example, HMI 311 may track user actions within HMI 311.
EN: user actions are tracked which means the action of the user are received
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIAHE NIU whose telephone number is (571)270-0152. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIAHE NIU/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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