DETAILED ACTION
This action is in response to the claims filed 03/14/2024 for Application number 18/605,580. Claims 1-20 are currently pending.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
generating a prompt [for input to a machine-learned language model], the prompt specifying at least the message and a request to infer whether an automated action can be performed for the message can be considered to be an evaluation in the human mind,
parsing the response from the model serving system to extract an automated action to perform based on the message can be considered to be an evaluation in the human mind
comparing the automated action extracted from the response to a set of rule actions to identify a rule action that corresponds to the automated action can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “for input to a machine-learned language model”, “…for execution by the machine-learned language model”, and “and responsive to identifying that a corresponding rule action is present, performing the automated action.”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: receiving, from one or more client devices, a message from a conversation sent from a sending party to a receiving party on a communication interface, wherein the sending party and the receiving party are users of an online system
providing the prompt to a model serving system for execution by the machine-learned language model;
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt;
These limitations are mere data gathering steps and thus are insignificant extra-solution activities. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a model serving system by executing the machine-learned language model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitations of:
receiving, from one or more client devices, a message from a conversation sent from a sending party to a receiving party on a communication interface, wherein the sending party and the receiving party are users of an online system
providing the prompt to a model serving system for execution by the machine-learned language model;
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt;
are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein each order includes a list of one or more items to be obtained at a fulfillment location. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the sending party is a first requesting user, the receiving party is a first fulfillment user, and the message indicates an inquiry or a modification to a first order by the first requesting user that is assigned to the first fulfillment user. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the sending party is a first fulfillment user, the receiving party is a first requesting user, and the message indicates an inquiry to delivery instructions related to a first order by the first requesting user that is assigned to the first fulfillment user. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein generating the prompt further comprises: generating the prompt further specifying another request to predict the automated action to be performed by the online system. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein generating the prompt further comprises: generating the prompt further specifying another request to predict an automated reply that the online system can provide to the sending party. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 6 is further incorporated, and further, the claim recites: wherein generating the prompt further comprises: generating the prompt further specifying previous conversations between the sending party and the receiving party, wherein the response generated by the machine-learned language model is informed by the previous conversations. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 6 is further incorporated, and further, the claim recites: wherein parsing the response from the model comprises: parsing the response to identify the automated action predicted based on the prompt and the automated reply predicted based on the prompt. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: modifying a first order inclusive of a list of items, the first order being fulfilled between the sending party and the receiving party; or modifying delivery instructions of the first order being fulfilled. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the machine-learned language model is trained by:
retrieving past conversations between the requesting users and the fulfillment users of the online system;
identifying one or more actions performed by the online system in response to the past conversations; and
training the machine-learned language model with the past conversations and the identified one or more actions. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 10 is further incorporated, and further, the claim recites: receiving feedback from the sending party or the receiving party in response to the automated action performed. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application. The claim as a whole is directed to an abstract idea.
The claim further recites: fine-tuning the machine-learned language model with the feedback. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 12, the rejection of claim 1 is further incorporated, and further, the claim recites: responsive to identifying that there is no corresponding rule action present, prompting the client device of the receiving party to provide a manual reply. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application. The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Claim 13 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 13 additionally requires analysis for “A non-transitory computer-readable storage medium storing instructions that, when executed by a computer processor, cause the computer processor to perform operations comprising..” however these additional elements amount to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP §2106.05(f).
Regarding Claims 14-19, it recites features similar to claim 3, 4, 5, 6 (16 is a combination of 5 and 6), 7, 10 and 11 and are rejected for at least the same reasons therein.
Regarding Claim 20, it recites features similar to claim 1 and 13 and is rejected for at least the same reasons therein.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Aggarwal et al. ("US 20220270600 A1", hereinafter "Aggarwal").
Regarding claim 1, Aggarwal teaches A method comprising:
receiving, from one or more client devices (See FIG. 1, [¶0021] consumer device 104), a message from a conversation sent from a sending party to a receiving party on a communication interface, wherein the sending party and the receiving party are users of an online system (“A customer may use the customer device 104 to initiate a call to a commerce site, such as a restaurant 132. … In other cases, a human employee may receive the call, the AI engine 110 may monitor the conversation between the customer and the employee, and monitor what the employee enters into the EA-POS device 102.” [¶0022]);
generating a prompt for input to a machine-learned language model, the prompt specifying at least the message and a request to infer whether an automated action can be performed for the message (“The language model 232 associates a specific utterance, e.g., “I want chicken wings” (prompt), with a specific action, e.g., entering a chicken wing order into the EA-POS 102.” [¶0036; See also ¶0024, the system checks if an automated action could be performed]);
providing the prompt to a model serving system for execution by the machine-learned language model (See ¶0036, The encoder 210 may include a pre-trained language model 232 that predicts, based on the most recent utterances 115 and the current order context 120, (1) how the cart 126 is to be modified and (2) what the software agent 116 provides as a response, e.g., dialog response 220.);
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt (“The encoder 210 may include a pre-trained language model 232 that predicts, based on the most recent utterances 115 and the current order context 120, (1) how the cart 126 is to be modified and (2) what the software agent 116 provides as a response, e.g., dialog response 220.” [¶0036]);
parsing the response from the model serving system to extract an automated action to perform based on the message (“To illustrate, if the customer says “Two large pepperoni pizzas” then the system automatically (e.g., without human interaction) adds two large pepperoni pizzas to the cart.” [¶0016]);
comparing the automated action extracted from the response to a set of rule actions to identify a rule action that corresponds to the automated action (“The AI engine may automatically populate and modify a cart associated with an order that each customer is placing. The AI engine may automatically provide suggestions to the human employees on up-selling (e.g., adding items, increasing a size of ordered items, or both).” [¶0030; modifying cart by adding items, removing items, increasing size of ordered items corresponds to a set of rule actions]); and
responsive to identifying that a corresponding rule action is present (“The dish classifier 214 may predict the cart delta vector 216 by passing the encoded representations in the utterance vector 212 through additional neural dialog layers for classification, resulting in a sparse vector that indicates the corresponding element(s) within all possible cart actions, e.g., a comprehensive array of labels of possible combinations.” [¶0048, possible cart actions corresponds to identifying a corresponding rule action is present]), performing the automated action (See ¶0016, “then the system automatically (e.g., without human interaction) adds two large pepperoni pizzas to the cart.” [¶0016]).
Regarding claim 2, Aggarwal teaches The method of claim 1, wherein each order includes a list of one or more items to be obtained at a fulfillment location. (“After the payment data 130 has been received and the payment data processed, the restaurant 132 may initiate order fulfillment 134, such as preparing the items in the order for take-out, delivery, in-restaurant dining, or the like.” [¶0026])
Regarding claim 3, Aggarwal teaches The method of claim 1, wherein the sending party is a first requesting user, the receiving party is a first fulfillment user, and the message indicates an inquiry or a modification to a first order by the first requesting user that is assigned to the first fulfillment user. (“To illustrate, if the customer says “Two large pepperoni pizzas” then the system automatically (e.g., without human interaction) adds two large pepperoni pizzas to the cart. Thus, the employee verbally interacts with the customer, without interacting with the point-of-sale terminal, and with the system interacting with the point-of-sale terminal. The employee observes the system modifying the contents of the cart while the employee is verbally interacting with the customer.” [¶0016])
Regarding claim 4, Aggarwal teaches The method of claim 1, wherein the sending party is a first fulfillment user, the receiving party is a first requesting user, and the message indicates an inquiry to delivery instructions related to a first order by the first requesting user that is assigned to the first fulfillment user. (“The customer may use a payment means, such as a digital wallet 128, to provide payment data 130 to complete the order. In response, the restaurant 132 may initiate order fulfillment 134 that includes preparing the ordered items for take-out, delivery, or in-restaurant consumption.” [¶0023; See further ¶0022, ¶0043 regarding delivery instructions])
Regarding claim 5, Aggarwal teaches The method of claim 1, wherein generating the prompt further comprises:
generating the prompt further specifying another request to predict the automated action to be performed by the online system. (“The utterances 115 and the order context 120 (e.g., contextual language information and current cart information up to a given point time) are encoded (e.g., into the utterance vector 212) to provide the cart delta vector 216 (e.g., a delta relative to the cart 126) as well as the next predicted dialog response 220.…For example, the encoder 210 is able to associate a specific utterance of the utterances 115, such as “I want chicken wings”, with a specific action, e.g., entering a chicken wing order into the cart 126. The encoder 210 predicts what items should be added to the cart 126 (e.g., based on the action associated with the utterance) and which items should be removed from the cart 126, and any associated quantities.” [¶0039])
Regarding claim 6, Aggarwal teaches The method of claim 5, wherein generating the prompt further comprises:
generating the prompt further specifying another request to predict an automated reply that the online system can provide to the sending party. (“For example, in a conventional system, after each utterance, the system may ask for a confirmation “Did you say ‘combo meal’? In the system of FIG. 2, a predictive model predicts the dialog response 220 based on the utterance 115 and the order context 120.” [¶0040])
Regarding claim 7, Aggarwal teaches The method of claim 6, wherein generating the prompt further comprises: generating the prompt further specifying previous conversations between the sending party and the receiving party, wherein the response generated by the machine-learned language model is informed by the previous conversations. (“The order context 120 may include an interaction history 222 between the software agent 116 and the customer, a current cart state 224, and a conversation state 226. The interaction history 222 may include interactions between the customer and one of the software agents 116, including the utterances 115 of the customer and the responses 113 of the software agent 116.” [¶0032])
Regarding claim 8, Aggarwal teaches The method of claim 6, wherein parsing the response from the model comprises: parsing the response to identify the automated action predicted based on the prompt and the automated reply predicted based on the prompt. (“The language model 232 associates a specific utterance, e.g., “I want chicken wings”, with a specific action, e.g., entering a chicken wing order into the EA-POS 102. The language model 232 predicts what items from the menu 140 are to be added to the cart 126 (e.g., based on one or more actions associated with the utterance 115) and which items are to be removed from the cart 126, quantities, modifiers, or other special treatments (e.g., preparation instructions such as “rare”, “medium”, “well done” or the like for cooking meat) associated with the items that are to be added and/or removed.” [¶0036; See further ¶0038; “For example, if a customer states that they would like to order a burger, an appropriate response may be “what toppings would you like on that burger?””])
Regarding claim 9, Aggarwal teaches The method of claim 1, wherein performing the automated action comprises: modifying a first order inclusive of a list of items, the first order being fulfilled between the sending party and the receiving party; or modifying delivery instructions of the first order being fulfilled. (See ¶0016, ¶0030 “The AI engine may automatically populate and modify a cart associated with an order that each customer is placing” note: The claim recites “or” thus the examiner is only required to map to one of the recited elements)
Regarding claim 10, Aggarwal teaches The method of claim 1, wherein the machine-learned language model is trained by:
retrieving past conversations between the requesting users and the fulfillment users of the online system (The training is based on the conversation data 136 that has been gathered over time between customers and employees who enter data in the EA-POS 102. [¶0036]);
identifying one or more actions performed by the online system in response to the past conversations (See ¶0036, “I want chicken wings” with a specific action, e.g. entering a chicken wing order into the EA-POS); and
training the machine-learned language model with the past conversations and the identified one or more actions (See ¶0036, “The training is based on the conversation data 136 that has been gathered over time between customers and employees who enter data in the EA-POS 102. The employee entered data may be used as labels for the conversation data 136 when training the various machine learning models described herein.).
Regarding claim 11, Aggarwal teaches The method of claim 10, further comprising:
receiving feedback from the sending party or the receiving party in response to the automated action performed (“At 804, the machine learning algorithm may be trained using training data 806 (e.g., a portion of the conversation data 136)… After the machine learning has been trained using the training data 806, the machine learning may be tested, at 808, using test data 810 to determine an accuracy of the machine learning.” [¶¶0066-0070; using conversation data would include receiving feedback from the sending party or receiving party in response to the automated action performed]); and
fine-tuning the machine-learned language model with the feedback (¶0071, “If an accuracy of the machine learning does not satisfy a desired accuracy (e.g., 95%, 98%, 99% accurate), at 808, then the machine learning code may be tuned, at 812, to achieve the desired accuracy.”).
Regarding claim 12, Aggarwal teaches The method of claim 1, further comprising: responsive to identifying that there is no corresponding rule action present, prompting the client device of the receiving party to provide a manual reply. (“The conversation may be routed to a human being under particular exception conditions, such as due to an inability of the software agent 116 to complete the conversation 111 or the like.” [¶0024])
Claim 13 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 13 additionally requires A non-transitory computer-readable storage medium storing instructions that, when executed by a computer processor, cause the computer processor to perform operations comprising… (Aggarwal, ¶0077 “may be any type of non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by the processors 902 as a particular machine configured for carrying out the operations and functions described in the implementations herein.”)
Regarding Claims 14-19, it recites features similar to claim 3, 4, 5, 6 (16 is a combination of 5 and 6), 7, 10 and 11 and are rejected in the same manner, the same art, and reasoning applying.
Regarding claim 20, it is substantially similar to claims 1 and 13 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhardwaj et al. (“US 20220247700 A1”) discloses a chatbot that can respond on behalf of a user during a three way chat
Sisodia et al. (“US 20200151391 A1”) discloses a system for generating conversation content for communication with a user
Swartz et al. (“US 20190378081 A1”) discloses a system for facilitating delivery of goods from a retail location to a customer
Rogers et al. (“US 20180247352 A1”) discloses a chatbot order submission system for taking group orders and transmitting the task data over to a network where an entity will perform a task
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL H HOANG/ PRIMARY EXAMINER, Art Unit 2122
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