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 No 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 abstract ideas without significantly more.
Regarding claim 1,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a machine.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of determine, based at least in part on the user information, a persona corresponding to a group of the user; recites an evaluation of user information to determine a persona, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generate, based at least in part on the request, the user information, the environment information, and the persona, a solution generation prompt configured to cause a machine learning model of the plurality of machine learning models to generate a personalized solution plan; recites an evaluation of a request and a persona to determine a prompt, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generate the personalized solution plan based on applying the solution generation prompt as input to the machine learning model; recites an evaluation of a prompt to determine a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of a computer-readable memory storing computer-executable instructions and a plurality of machine learning models; recites the mere application of generic computer components, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
The limitation of and one or more processors configured to execute the computer-executable instructions to at least: recites the mere application of generic computer components, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
The limitation of receive, from a requesting device, a request to generate a solution plan comprising an indication of a user identity associated with a user; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of obtain, based at least in part on the request, user information from a user information store, wherein the user information indicates an attribute or role information associated with the user; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of obtain, based at least in part on the request, environment information from an environmental information store, wherein the environment information comprises information describing an environment for which the solution plan is to be generated; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of and cause the requesting device to present the personalized solution plan to the user recites the mere extra-solution activity of data outputting, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of a computer-readable memory storing computer-executable instructions and a plurality of machine learning models; recites the mere application of generic computer components, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(f).
The limitation of and one or more processors configured to execute the computer-executable instructions to at least: recites the mere application of generic computer components, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(f).
The limitation of receive, from a requesting device, a request to generate a solution plan comprising an indication of a user identity associated with a user; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of obtain, based at least in part on the request, user information from a user information store, wherein the user information indicates an attribute or role information associated with the user; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of obtain, based at least in part on the request, environment information from an environmental information store, wherein the environment information comprises information describing an environment for which the solution plan is to be generated; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of and cause the requesting device to present the personalized solution plan to the user recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 1 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 2,
Claim 2 adds the additional limitations to claim 1:
wherein the one or more processors are further configured by the computer-executable instructions to obtain, based at least in part on the request, contextual information from a contextual information store, wherein the contextual information comprises a previous request received by the system, recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
and wherein the solution plan is further based in part on the contextual information recites an evaluation of contextual information to determine a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 2 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 3,
Claim 3 adds the additional limitations to claim 1:
wherein to generate the persona, the one or more processors are further configured by the computer-executable instructions to: generate a persona generation prompt configured to cause a second machine learning model to generate the persona; recites an evaluation of a prompt, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
and apply the persona generation prompt as input to the second machine learning model recites mere instructions to apply a prompt to a generic machine learning model, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 3 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 4,
Claim 4 adds the additional limitations to claim 1:
wherein the one or more processors are further configured by the computer-executable instructions to determine the personalized solution plan is responsive to the request based on applying the solution plan as input to a second machine learning model configured to generate a determination indicating whether the personalized solution plan is responsive to the request recites a judgement on if a plan is responsive to a request, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 4 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 5,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a process.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of determining, based on the obtained user information, a persona associated with the user; recites an evaluation of user information to determine a persona, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generating, based in part on the request and the persona, a solution request prompt; recites an evaluation of a request and a persona to determine a prompt, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generating the personalized solution plan based at least in part on applying the solution request prompt as input to a machine learning model configured to generate a solution plan; recites an evaluation of a prompt to determine a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of under control of a computing device comprising one or more processors configured to execute specific instructions, recites the mere application of generic computer components, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
The limitation of receiving a request to generate a personalized solution plan comprising an indication of a user identity corresponding to a user; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of obtaining user information associated with the user identity; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of and causing transmission of the personalized solution plan responsive to the request recites the mere extra-solution activity of data outputting, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of under control of a computing device comprising one or more processors configured to execute specific instructions, recites the mere application of generic computer components, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(f).
The limitation of receiving a request to generate a personalized solution plan comprising an indication of a user identity corresponding to a user; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of obtaining user information associated with the user identity; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of and causing transmission of the personalized solution plan responsive to the request recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 5 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 6,
Claim 6 adds the additional limitations to claim 5:
obtaining environment information, recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
wherein the persona is determined based in part on the environment information recites an evaluation of environment information to determine a persona, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 6 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 7,
Claim 7 adds the additional limitations to claim 5:
further comprising obtaining contextual information, recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
wherein the persona is determined based in part on the contextual information recites an evaluation of contextual information to determine a persona, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 7 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 8,
Claim 8 adds the additional limitations to claim 5:
wherein to generate the personalized solution plan the machine learning model further obtains environment information, recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
and wherein the personalized solution plan is based at least in part on the environment information recites an evaluation of environment information to determine a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 8 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 9,
Claim 9 adds the additional limitations to claim 5:
wherein to generate the personalized solution plan the machine learning model further obtains contextual information, recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
and wherein the personalized solution plan is based at least in part on the contextual information recites an evaluation of contextual information to determine a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 9 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 10,
Claim 10 adds the additional limitations to claim 5:
applying the personalized solution plan as input to a second machine learning model configured to generate a determination indicating whether the personalized solution plan is responsive to the request; recites a judgement of a plan’s responsiveness to a request, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
based on the determination indicating the personalized solution plan is not responsive to the request, obtaining additional information; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
and updating the personalized solution plan based at least in part on applying the personalized solution plan and the additional information as input to the machine learning model recites an evaluation of an updated plan based on additional information, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 10 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 11,
Claim 11 adds the additional limitations to claim 5:
modifying the personalized solution plan based on applying the persona and the personalized solution plan as input to a second machine learning model configured to further personalize the personalized solution plan for the user identity recites an evaluation of a modified plan based on an original plan and a persona, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 11 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 12,
Claim 12 adds the additional limitations to claim 11:
wherein the personalized solution plan comprises a plurality of solution instructions, recites further detail on the personalized solution plan, without changing that generating the personalized solution plan …, as recited in claim 5, is an evaluation, which is a mental process, which is an abstract idea.
and wherein further personalizing the solution plan comprises at least one of: replacing a first term of a text of the solution plan with a second term based in part on the persona, adding an additional solution instruction to the plurality of solution instructions of the solution plan, or removing a solution instruction of the plurality of solution instructions of the solution plan recites a judgement of the terminology and/or steps to be used within a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 12 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 13,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a manufacture.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of determine, based on the obtained user information, a persona; recites an evaluation of user information to determine a persona, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generate, based in part on the request and the persona, a solution request prompt; recites an evaluation of a request and a persona to determine a prompt, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generate a personalized solution plan based at least in part on applying the solution request prompt as input to a machine learning model configured to generate a solution plan; recites an evaluation of a prompt to determine a plan, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of receive a request comprising an indication of a user identity; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of obtain user information associated with the user identity; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of and provide the personalized solution plan in response to the request recites the mere extra-solution activity of data outputting, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of receive a request comprising an indication of a user identity; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of obtain user information associated with the user identity; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of and provide the personalized solution plan in response to the request recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 13 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 14,
Claim 14 adds the additional limitations to claim 13:
obtain environmental information for an environment associated with the request; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
and obtain contextual information based in part on the request; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
wherein the solution request prompt is generated based in part on the environmental information and the contextual information recites an evaluation of environment information and contextual information to determine a prompt, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 14 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 15,
Claim 15 adds the additional limitations to claim 14:
wherein the environmental information comprises at least one of: an equipment identifier, a device identifier, or a user location recites further detail on the environmental information, without changing that obtain environmental information …, as recited in claim 14, is mere data gathering and receiving data over a network, and does not integrate or constitute significantly more than any recited judicial exceptions.
Therefore, claim 15 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 16,
Claim 16 adds the additional limitations to claim 14:
wherein the contextual information comprises at least one of: a previous request, a previous personalized solution plan, or a previous machine learning model used to generate the previous personalized solution plan recites further detail on the contextual information, without changing that and obtain contextual information …, as recited in claim 14, is mere data gathering and receiving data over a network, and does not integrate or constitute significantly more than any recited judicial exceptions.
Therefore, claim 16 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 17,
Claim 17 adds the additional limitations to claim 13:
wherein the personalized solution plan comprises a plurality of solution instructions recites further detail on the personalized solution plan, without changing that generate a personalized solution plan …, as recited in claim 13, is an evaluation, which is a mental process, which is an abstract idea.
Therefore, claim 17 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 18,
Claim 18 adds the additional limitations to claim 17:
wherein each solution instruction of the plurality of solution instructions comprise at least one of: a text instruction, a video instruction, an image instruction, or a multimodal instruction recites further detail on the personalized solution plan, without changing that generate a personalized solution plan …, as recited in claim 13, is an evaluation, which is a mental process, which is an abstract idea.
Therefore, claim 18 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 19,
Claim 19 adds the additional limitations to claim 13:
wherein the user information comprises at least one of: a job position, a length of employment, an employer, or a technical background recites further detail on the user information, without changing that obtain user information …, as recited in claim 13, is mere data gathering and receiving data over a network, and does not integrate or constitute significantly more than any recited judicial exceptions.
Therefore, claim 19 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 20,
Claim 20 adds the additional limitations to claim 13:
wherein the machine learning model is retrieved from a machine learning model store based in part on the solution request prompt recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Prior Art
The following references are used for prior art claim rejections:
Williams et al. (U.S. Patent Application Publication No. 2024/0330654), hereinafter Williams
Durvasula et al. (U.S. Patent Application Publication No. 2025/0139378), hereinafter Durvasula
Luus et al. (U.S. Patent Application Publication No. 2024/0428275), hereinafter Luus
Low et al. (U.S. Patent Application Publication No. 2025/0165678), hereinafter Low
Vasudevan et al. “Towards Better Confidence Estimation for Neural Models”, hereinafter Vasudevan
Mahapatra et al. (U.S. Patent Application Publication No. 2022/0067604), hereinafter Mahapatra
Sejpal et al. (U.S. Patent Application Publication No. 2025/0029173), hereinafter Sejpal
Jadhav et al. (U.S. Patent Application Publication No. 2019/0042668), hereinafter Jadhav
Gnanasambandam et al. (U.S. Patent Application Publication No. 2023/0052573), hereinafter Gnanasambandam
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 6, 8, 13, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula.
Regarding claim 1,
Williams teaches A system comprising:
a computer-readable memory storing computer-executable instructions and a plurality of machine learning models; ((Williams [0033]) “The memory 122 may store a plurality of computing modules 130, implemented as respective sets of computer-executable instructions (e.g., one or more source code libraries, trained ML models such as neural networks, convolutional neural networks, etc.) as described herein”)
and one or more processors configured to execute the computer-executable instructions to at least: ((Williams [0169]) “The various operations of exemplary methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations”)
receive, from a requesting device, a request to generate a solution plan comprising an indication of a user identity associated with a user; ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot))
obtain, based at least in part on the request, user information from a user information store, ((Williams [0098]) “At least some of the claim information may be available to the insurance carrier, e.g., based upon the user profile associated with the policyholder identified upon logging into the app. In one aspect, based upon Jack's user profile, the server 105 may obtain Jack's policyholder data”)
wherein the user information indicates an attribute or role information associated with the user; ((Williams [0098]) “In one aspect, based upon Jack's user profile, the server 105 may obtain Jack's policyholder data such as name, address, date of birth, insurance policy/policies information (e.g., types of policies, account numbers, coverage information, items covered, etc.), as well as other suitable information”)
obtain, based at least in part on the request, environment information from an environmental information store, ((Williams [0098]) “In another aspect, based on location information provided by user device 102, the server 105 may obtain Jack's current location”, location information is environment information)
wherein the environment information comprises information describing an environment ((Williams [0100]) “additional claim information from the policy holder which may be pertinent to generating insurance claim filing instructions and may include, but is not limited to, property effected by the loss, location of the loss, date and time of the loss, description of the loss and/or events surrounding the loss, as well as any other suitable information”) for which the solution plan is to be generated; ((Williams [0102]) “ML model 310 may generate insurance claim filing instructions, such as seeking medical assistance for any injuries, requesting a tow truck, contacting a collision center, submitting a first notice of loss with the insurance provider, etc.”)
generate the personalized solution plan based on applying the solution generation prompt as input to the machine learning model; ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot) to cause an ML model to divide the objective into one or more discrete steps and generate personalized instructions for performing the one or more discrete steps”)
and cause the requesting device to present the personalized solution plan to the user ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…receive the personalized instructions for performing the one or more discrete steps from the ML chatbot (or voice bot), and communicate the personalized instructions for performing the one or more discrete steps to the user”)
Durvasula teaches the following further limitations that Williams does not teach:
determine, based at least in part on the user information, ((Durvasula [0078]) “the persona classification module 311 may classify the data based on one or more user characteristics associated with the data”) a persona corresponding to a group of the user; ((Durvasula [0052]) “The transformed data may also be curated…by performing one or more operations to…abstract the data (for instance, to groups of users (e.g., by generating and enriching representative personas))”)
generate, based at least in part on the request, ((Durvasula [0022]) “In response to identifying the at least one event, the event-driven personalized recommendation system can generate, using at least one ML model, a personalized recommendation to achieve a goal based on the at least one event. The personalized recommendation can include at least one action that can be taken to achieve the goal”) the user information, ((Durvasula [0043]) “Recommendation system 120 can generate an event-driven personalized recommendation for a user based on user data for the user obtained from data sources 110”) the environment information, ((Durvasula [0078]) “the persona classification module 311 may use…location data…to generate and/or classify data into different personas”, location data is environment information) and the persona, ((Durvasula [0104]) “In further implementations, generating the personalized recommendation includes comparing the user to other users within a same persona category (e.g., using the one or more second machine learning models)”) a solution generation prompt ((Durvasula [0004]) “(ii) classify, using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data”, classified input data used to generate a solution corresponds to a solution generation prompt) configured to cause a machine learning model of the plurality of machine learning models to generate a personalized solution plan; ((Durvasula [0004]) “(iv) generate, based on the at least one event and the classified input data, a personalized recommendation for the user using one or more second machine learning models, wherein the personalized recommendation comprises a set of actions predicted to achieve the goal”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams and Durvasula by taking the system for generating personalized solution plans, taught by Williams, and including determination of a persona and a solution request prompt using the persona, taught by Durvasula, as Durvasula teaches (Durvasula [0104]) “the system may determine one or more actions based on the other users within the same persona category (e.g., by recommending similar actions, by determining actions contrary to negative actions taken by other users, to predict potential positive actions, etc.)”, that is, that associating a user with a persona is helpful for using data from other users with the same or similar personas to improve the quality of the generated solution. Such a combination would be obvious.
Regarding claim 5,
Williams teaches A computer-implemented method comprising:
under control of a computing device comprising one or more processors configured to execute specific instructions, ((Williams [0169]) “The various operations of exemplary methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations”)
receiving a request to generate a personalized solution plan comprising an indication of a user identity corresponding to a user; ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot))
obtaining user information associated with the user identity; ((Williams [0098]) “At least some of the claim information may be available to the insurance carrier, e.g., based upon the user profile associated with the policyholder identified upon logging into the app. In one aspect, based upon Jack's user profile, the server 105 may obtain Jack's policyholder data”)
generating the personalized solution plan based at least in part on applying the solution request prompt as input to a machine learning model configured to generate a solution plan; ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot) to cause an ML model to divide the objective into one or more discrete steps and generate personalized instructions for performing the one or more discrete steps”)
and causing transmission of the personalized solution plan responsive to the request ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…receive the personalized instructions for performing the one or more discrete steps from the ML chatbot (or voice bot), and communicate the personalized instructions for performing the one or more discrete steps to the user”)
Durvasula teaches the following further limitations that Williams does not teach:
determining, based on the obtained user information, ((Durvasula [0078]) “the persona classification module 311 may classify the data based on one or more user characteristics associated with the data”) a persona associated with the user; ((Durvasula [0003]) “(ii) classifying, by the one or more processors and using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data, wherein the one or more personas include at least one user persona representative of the user”)
generating, based in part on the request ((Durvasula [0022]) “In response to identifying the at least one event, the event-driven personalized recommendation system can generate, using at least one ML model, a personalized recommendation to achieve a goal based on the at least one event. The personalized recommendation can include at least one action that can be taken to achieve the goal”) and the persona, ((Durvasula [0104]) “In further implementations, generating the personalized recommendation includes comparing the user to other users within a same persona category (e.g., using the one or more second machine learning models)”) a solution request prompt; ((Durvasula [0004]) “(ii) classify, using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data…and (iv) generate, based on…the classified input data, a personalized recommendation…wherein the personalized recommendation comprises a set of actions predicted to achieve the goal”, classified input data used to generate a solution corresponds to a solution request prompt)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams and Durvasula by taking the method for generating personalized solution plans, taught by Williams, and including determination of a persona and a solution request prompt using the persona, taught by Durvasula, as Durvasula teaches (Durvasula [0104]) “the system may determine one or more actions based on the other users within the same persona category (e.g., by recommending similar actions, by determining actions contrary to negative actions taken by other users, to predict potential positive actions, etc.)”, that is, that associating a user with a persona is helpful for using data from other users with the same or similar personas to improve the quality of the generated solution. Such a combination would be obvious.
Regarding claim 6,
Williams and Durvasula jointly teach The computer-implemented method of claim 5
Durvasula further teaches:
further comprising obtaining environment information, wherein the persona is determined based in part on the environment information ((Durvasula [0078]) “the persona classification module 311 may use…location data…to generate and/or classify data into different personas”, location data is environment information)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Williams and Durvasula for the parent claim of claim 6, claim 5. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 8,
Williams and Durvasula jointly teach The computer-implemented method of claim 5,
Williams further teaches:
wherein to generate the personalized solution plan the machine learning model further obtains environment information, ((Williams [0098]) “In another aspect, based on location information provided by user device 102, the server 105 may obtain Jack's current location”, location information is environment information)
and wherein the personalized solution plan is based at least in part on the environment information ((Williams [0100]) “additional claim information from the policy holder which may be pertinent to generating insurance claim filing instructions and may include, but is not limited to, property effected by the loss, location of the loss, date and time of the loss, description of the loss and/or events surrounding the loss, as well as any other suitable information”, (Williams [0102]) “ML model 310 may generate insurance claim filing instructions, such as seeking medical assistance for any injuries, requesting a tow truck, contacting a collision center, submitting a first notice of loss with the insurance provider, etc.”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Williams and Durvasula for the parent claim of claim 8, claim 5. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 13,
Williams teaches A non-transitory machine-readable storage medium encoded with instructions executable by a processor of a computing device, wherein the instructions, when executed by the processor, cause the computing device to at least: ((Williams [0009]) “In another aspect, a non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to”)
receive a request comprising an indication of a user identity; ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot))
obtain user information associated with the user identity; ((Williams [0098]) “At least some of the claim information may be available to the insurance carrier, e.g., based upon the user profile associated with the policyholder identified upon logging into the app. In one aspect, based upon Jack's user profile, the server 105 may obtain Jack's policyholder data”)
generate a personalized solution plan based at least in part on applying the solution request prompt as input to a machine learning model configured to generate a solution plan; ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot) to cause an ML model to divide the objective into one or more discrete steps and generate personalized instructions for performing the one or more discrete steps”)
and provide the personalized solution plan in response to the request ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…receive the personalized instructions for performing the one or more discrete steps from the ML chatbot (or voice bot), and communicate the personalized instructions for performing the one or more discrete steps to the user”)
Durvasula teaches the following further limitations that Williams does not teach:
determine, based on the obtained user information, ((Durvasula [0078]) “the persona classification module 311 may classify the data based on one or more user characteristics associated with the data”) a persona; ((Durvasula [0003]) “(ii) classifying, by the one or more processors and using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data, wherein the one or more personas include at least one user persona representative of the user”)
generate, based in part on the request ((Durvasula [0022]) “In response to identifying the at least one event, the event-driven personalized recommendation system can generate, using at least one ML model, a personalized recommendation to achieve a goal based on the at least one event. The personalized recommendation can include at least one action that can be taken to achieve the goal”) and the persona, ((Durvasula [0104]) “In further implementations, generating the personalized recommendation includes comparing the user to other users within a same persona category (e.g., using the one or more second machine learning models)”) a solution request prompt; ((Durvasula [0004]) “(ii) classify, using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data…and (iv) generate, based on…the classified input data, a personalized recommendation…wherein the personalized recommendation comprises a set of actions predicted to achieve the goal”, classified input data used to generate a solution corresponds to a solution request prompt)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams and Durvasula by taking the medium with instructions for generating personalized solution plans, taught by Williams, and including determination of a persona and a solution request prompt using the persona, taught by Durvasula, as Durvasula teaches (Durvasula [0104]) “the system may determine one or more actions based on the other users within the same persona category (e.g., by recommending similar actions, by determining actions contrary to negative actions taken by other users, to predict potential positive actions, etc.)”, that is, that associating a user with a persona is helpful for using data from other users with the same or similar personas to improve the quality of the generated solution. Such a combination would be obvious.
Regarding claim 17,
Williams and Durvasula jointly teach The non-transitory machine-readable storage medium of claim 13,
Williams further teaches:
wherein the personalized solution plan comprises a plurality of solution instructions ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot) to cause an ML model to divide the objective into one or more discrete steps and generate personalized instructions for performing the one or more discrete steps”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Williams and Durvasula for the parent claim of claim 17, claim 13. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 18,
Williams and Durvasula jointly teach The non-transitory machine-readable storage medium of claim 17,
Williams further teaches:
wherein each solution instruction of the plurality of solution instructions comprise at least one of: a text instruction, a video instruction, an image instruction, or a multimodal instruction ((Williams [0087]) “In one aspect, an organization may use the AI and/or ML chatbot, such as the trained chatbot 150, to generate one or more customized components of the customized presentation to walk a user through the generated personalized instructions. The trained ML chatbot may generate output such as images, video, slides (e.g., a PowerPoint slide), virtual reality, augmented reality, mixed reality, multimedia, blockchain entries, metaverse content, or any other suitable components which may be used in the customized presentation”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Williams and Durvasula for the parent claim of claim 18, claim 17. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 20,
Williams and Durvasula jointly teach The non-transitory machine-readable storage medium of claim 13,
Durvasula further teaches:
wherein the machine learning model is retrieved from a machine learning model store based in part on the solution request prompt ((Durvasula [0125]) “deploying the trained ML model can include storing the trained ML model, which can be accessible by an analytics system of a recommendation system to generate personalized recommendations”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Williams and Durvasula for the parent claim of claim 20, claim 13. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Claims 2, 9, 14-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Luus.
Regarding claim 2,
Williams and Durvasula jointly teach The system of claim 1,
Luus teaches the following further limitations more explicitly than Williams or Durvasula teaches:
wherein the one or more processors are further configured by the computer-executable instructions to obtain, based at least in part on the request, contextual information from a contextual information store, ((Luus [0064]-[0065]) “Application service 310 receives the user input and requests a targeted portion from AI engine 320. The targeted portion is to be included in a prompt to be sent to foundation model service 330. Upon receiving the request for the targeted portion, AI engine 320 requests and receives context data from context datastore 315 via application service 310”)
wherein the contextual information comprises a previous request received by the system, ((Luus [0066]-[0067]) “Application service 310 submits the prompt to foundation model service 330. foundation model service 330 generates a reply to the prompt and returns the reply to application service 310…Application service 310 also sends the reply to context datastore 315 for use in subsequent prompts as additional context”, (Luus [0018]) “The application service generates a prompt to elicit a reply from the foundation model service (e.g., an LLM service) including recommendations for accomplishing the job by means of one or more software applications”) and wherein the solution plan is further based in part on the contextual information ((Luus [0023]) “By transmitting background or contextual information to the AI engine of the application, the receiving AI engine can provide further guidance to the user at the appropriate level of instruction. For example, if the user wishes to create a household budget and the user has relatively limited experience using spreadsheets, the AI engine of the application service may indicate to an AI engine of a spreadsheet application to display a budget template or to present instructions for creating a simple budget in relatively few steps using relatively simple spreadsheet functions.”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Luus by taking the system of claim 1 for generating personalized solution plans, taught by Williams and Durvasula, and including using additional contextual information, including previous requests, to generate the personalized solution plans, taught by Luus, as Luus teaches: (Luus [0023]) “By transmitting background or contextual information to the AI engine of the application, the receiving AI engine can provide further guidance to the user at the appropriate level of instruction”. Such a combination would be obvious.
Regarding claim 9,
Williams and Durvasula jointly teach The computer-implemented method of claim 5,
Luus teaches the following further limitations more explicitly than Williams or Durvasula teaches:
wherein to generate the personalized solution plan the machine learning model further obtains contextual information, ((Luus [0064]-[0065]) “Application service 310 receives the user input and requests a targeted portion from AI engine 320. The targeted portion is to be included in a prompt to be sent to foundation model service 330. Upon receiving the request for the targeted portion, AI engine 320 requests and receives context data from context datastore 315 via application service 310”)
and wherein the personalized solution plan is based at least in part on the contextual information ((Luus [0023]) “By transmitting background or contextual information to the AI engine of the application, the receiving AI engine can provide further guidance to the user at the appropriate level of instruction. For example, if the user wishes to create a household budget and the user has relatively limited experience using spreadsheets, the AI engine of the application service may indicate to an AI engine of a spreadsheet application to display a budget template or to present instructions for creating a simple budget in relatively few steps using relatively simple spreadsheet functions.”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Luus by taking the method of claim 5 for generating personalized solution plans, taught by Williams and Durvasula, and including using additional contextual information to generate the personalized solution plans, taught by Luus, as Luus teaches: (Luus [0023]) “By transmitting background or contextual information to the AI engine of the application, the receiving AI engine can provide further guidance to the user at the appropriate level of instruction”. Such a combination would be obvious.
Regarding claim 14,
Williams and Durvasula jointly teach The non-transitory machine-readable storage medium of claim 13, wherein the instructions, when executed by the processor, further cause the computing device to:
Williams further teaches:
obtain environmental information for an environment associated with the request; ((Williams [0098]) “In another aspect, based on location information provided by user device 102, the server 105 may obtain Jack's current location”, location information is environment information)
wherein the solution request prompt is generated based in part on the environmental information [and the contextual information] ((Williams [0100]) “additional claim information from the policy holder which may be pertinent to generating insurance claim filing instructions and may include, but is not limited to, property effected by the loss, location of the loss, date and time of the loss, description of the loss and/or events surrounding the loss, as well as any other suitable information”, (Williams [0102]) “ML model 310 may generate insurance claim filing instructions, such as seeking medical assistance for any injuries, requesting a tow truck, contacting a collision center, submitting a first notice of loss with the insurance provider, etc.”, Williams does not teach using contextual information to generate a prompt)
Luus teaches the following further limitations more explicitly than Williams or Durvasula teaches:
and obtain contextual information based in part on the request; ((Luus [0064]-[0065]) “Application service 310 receives the user input and requests a targeted portion from AI engine 320. The targeted portion is to be included in a prompt to be sent to foundation model service 330. Upon receiving the request for the targeted portion, AI engine 320 requests and receives context data from context datastore 315 via application service 310”)
wherein the solution request prompt is generated based in part on…and the contextual information ((Luus [0065]) “AI engine 320 receives context data from context datastore 315 and configures a targeted portion for the prompt”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Luus by taking the medium of claim 13 for generating personalized solution plans, including environmental information for generating a solution request prompt, taught by Williams and Durvasula, and including using additional contextual information to generate the prompt for generating the personalized solution plans, taught by Luus, as Luus teaches: (Luus [0023]) “By transmitting background or contextual information to the AI engine of the application, the receiving AI engine can provide further guidance to the user at the appropriate level of instruction”. Such a combination would be obvious.
Regarding claim 15,
Williams, Durvasula, and Luus jointly teach The non-transitory machine-readable storage medium of claim 14,
Williams further teaches:
wherein the environmental information comprises at least one of: an equipment identifier, a device identifier, or a user location ((Williams [0098]) “In another aspect, based on location information provided by user device 102, the server 105 may obtain Jack's current location”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Williams, Durvasula, and Luus for the parent claim of claim 15, claim 14. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 16,
Williams, Durvasula, and Luus jointly teach The non-transitory machine-readable storage medium of claim 14,
Luus further teaches:
wherein the contextual information comprises at least one of: a previous request, a previous personalized solution plan, or a previous machine learning model used to generate the previous personalized solution plan ((Luus [0066]-[0067]) “Application service 310 submits the prompt to foundation model service 330. foundation model service 330 generates a reply to the prompt and returns the reply to application service 310…Application service 310 also sends the reply to context datastore 315 for use in subsequent prompts as additional context”, (Luus [0018]) “The application service generates a prompt to elicit a reply from the foundation model service (e.g., an LLM service) including recommendations for accomplishing the job by means of one or more software applications”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Williams, Durvasula, and Luus for the parent claim of claim 16, claim 14. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 19,
Williams and Durvasula jointly teach The non-transitory machine-readable storage medium of claim 13,
Luus teaches the following further limitations more explicitly than Williams or Durvasula teaches:
wherein the user information comprises at least one of: a job position, a length of employment, an employer, or a technical background ((Luus [0005]) “In an implementation, the information relating to the user includes a skill level of the user with respect to the one or more applications”, a skill level corresponds to a technical background)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Luus by taking the medium of claim 13 for generating personalized solution plans, taught by Williams and Durvasula, and including the user information for generating the plan including the user’s technical background, taught by Luus, as Luus teaches: (Luus [0023]) “By transmitting background or contextual information to the AI engine of the application, the receiving AI engine can provide further guidance to the user at the appropriate level of instruction”. Such a combination would be obvious.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Low.
Regarding claim 3,
Williams and Durvasula jointly teach The system of claim 1,
Low teaches the following further limitations that Williams does not teach, and more explicitly than Durvasula teaches:
wherein to generate the persona, the one or more processors are further configured by the computer-executable instructions to: generate a persona generation prompt ((Low [0087]) “Prompt generator 702 may generate inputs that can be provided to model(s) 190”, (Low [0095]) “prompts may include a description of a persona, a context, and a question relating to understanding of the context”) configured to cause a second machine learning model to generate the persona; ((Low [0121]) “Building a prompt chain is an iterative process of prompting a model to generate responses to the prompt chain. The final, resulting prompt chain can include sufficient context or memory about a particular user or persona using generated responses from the model, so that the model may respond in a vectorial space that accurately represents the particular user or persona”, iterative prompting of a model to more accurately represent a persona corresponds to causing the model to generate the persona)
and apply the persona generation prompt as input to the second machine learning model ((Low [0087]) “Prompt generator 702 may generate inputs that can be provided to model(s) 190”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Low by taking the system of claim 1 for generating personalized solution plans, including using a persona representative of a ground of users to generate the plan, taught by Williams and Durvasula, and including generating a prompt to generate the persona that is given as input to another machine learning model, taught by Low, as Low teaches: (Low [0121]) “Building a prompt chain is an iterative process of prompting a model to generate responses to the prompt chain. The final, resulting prompt chain can include sufficient context or memory about a particular user or persona using generated responses from the model, so that the model may respond in a vectorial space that accurately represents the particular user or persona”, that is, that designing prompts as inputs to a model to generate a persona allow its vectorial space to more accurately represent the persona. Such a combination would be obvious.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Vasudevan.
Regarding claim 4,
Williams and Durvasula jointly teach The system of claim 1,
Williams further teaches:
wherein the one or more processors are further configured by the computer-executable instructions to determine the personalized solution plan is responsive to the request based on applying the solution plan [as input to a second machine learning model configured] to generate a determination indicating whether the personalized solution plan is responsive to the request ((Williams [0110]) “the chatbot 150 may determine a confidence level at one or more instances during the session. The confidence level and/or score, which may be a number between 0 and 1, may represent the likelihood that the output of an ML chatbot/model is correct and will satisfy a user's request”, Williams does not teach using a second machine learning model to generate the determination that the output is responsive)
Vasudevan teaches the following further limitation that Williams does not explicitly teach, and that Durvasula does not teach:
wherein the one or more processors are further configured by the computer-executable instructions to determine [the personalized solution plan] is responsive to the request based on applying the solution plan as input to a second machine learning model configured to generate a determination indicating whether [the personalized solution plan] is responsive to the request ((Vasudevan Pg. 1) “In most intelligent personal digital assistant systems like Alexa, Google Assistant and Siri, there are multiple Natural Language Understanding (NLU) domains, which can provide a response to the user query [2]. In order to evaluate which of these responses is best suited, the system needs to have a measure of how confident each of these answer providers is about their response…Our main contributions in this paper are…an algorithm to combine these features with the posterior probability baseline using a regression model…We show that the proposed features and confidence prediction model produce a more calibrated confidence score”, Williams but not Vasudevan teaches a personalized solution plan)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Vasudevan by taking the system of claim 1 for generating personalized solution plans, including determining if the personalized solution plan is responsive to the request, taught by Williams and Durvasula, and including using a second machine learning model to make this determination, taught by Vasudevan, as Vasudevan teaches: (Vasudevan Pg. 1) “We show that the proposed features and confidence prediction model produce a more calibrated confidence score”, that is, that using a separate model creates a more reliable estimate of the correctness of a different model’s output, than that model’s own assessment. Such a combination would be obvious.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Mahapatra.
Regarding claim 7,
Williams and Durvasula jointly teach The computer-implemented method of claim 5
Mahapatra teaches the following further limitation that Williams does not teach and more explicitly than Durvasula teaches:
further comprising obtaining contextual information, ((Mahapatra [0003]) “process the application data, the database data, and the user data, with one or more context generator models, to determine context data matching the users and events associated with the applications”) wherein the persona is determined based in part on the contextual information ((Mahapatra [0012]) “The application aggregation system may process the context data, the task data, and the role data, with a persona creator model, to generate persona data that identifies personas, and assignment data that assigns each of the users to one of the personas”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Mahapatra by taking the method of claim 5 for generating personalized solution plans, including a persona used to generate the plans, taught by Williams and Durvasula, and including using additional contextual information to generate the persona, taught by Mahapatra, as it is well known in the art that increasing the amount and diversity of data used by a machine learning model generally results in the model producing more accurate results. Such a combination would be obvious.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Vasudevan, further in view of Sejpal.
Regarding claim 10,
Williams and Durvasula jointly teach The computer-implemented method of claim 5 further comprising:
Williams further teaches:
applying the personalized solution plan [as input to a second machine learning model configured] to generate a determination indicating whether the personalized solution plan is responsive to the request; ((Williams [0110]) “the chatbot 150 may determine a confidence level at one or more instances during the session. The confidence level and/or score, which may be a number between 0 and 1, may represent the likelihood that the output of an ML chatbot/model is correct and will satisfy a user's request”, Williams does not teach using a second machine learning model to generate the determination that the output is responsive)
Vasudevan teaches the following further limitation that Williams does not explicitly teach, and that Durvasula does not teach:
applying [the personalized solution plan] as input to a second machine learning model configured to generate a determination indicating whether [the personalized solution plan] is responsive to the request; ((Vasudevan Pg. 1) “In most intelligent personal digital assistant systems like Alexa, Google Assistant and Siri, there are multiple Natural Language Understanding (NLU) domains, which can provide a response to the user query [2]. In order to evaluate which of these responses is best suited, the system needs to have a measure of how confident each of these answer providers is about their response…Our main contributions in this paper are…an algorithm to combine these features with the posterior probability baseline using a regression model…We show that the proposed features and confidence prediction model produce a more calibrated confidence score”, Williams but not Vasudevan teaches a personalized solution plan)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Vasudevan by taking the method of claim 5 for generating personalized solution plans, including determining if the personalized solution plan is responsive to the request, taught by Williams and Durvasula, and including using a second machine learning model to make this determination, taught by Vasudevan, as Vasudevan teaches: (Vasudevan Pg. 1) “We show that the proposed features and confidence prediction model produce a more calibrated confidence score”, that is, that using a separate model creates a more reliable estimate of the correctness of a different model’s output, than that model’s own assessment. Such a combination would be obvious.
Sejpal teaches the following further limitations that Williams does not explicitly teach, and that Durvasula and Vasudevan do not teach:
based on the determination indicating the personalized solution plan is not responsive to the request, obtaining additional information; ((Sejpal [0037]) “In some embodiments, the user may provide input requesting one or more modifications to a recipe or to the personalized meal plan”)
and updating the personalized solution plan based at least in part on applying the personalized solution plan and the additional information as input to the machine learning model ((Sejpal [0037]) “In response, the online system 140 may generate a subsequent prompt including the recipe and/or the personalized meal plan to be modified and the one or more modifications provided by the user. The online system 140 may provide the subsequent prompt for input to the model serving system 150. The online system 140 receives a subsequent response to the subsequent prompt from the model serving system 150 based on execution of the machine-learning model using the subsequent prompt. The online system 140 may determine the modified personalized meal plan and/or the modified recipe based on the subsequent response”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, Vasudevan, and Sejpal by taking the method of claim 5 for generating personalized solution plans, including determining if the personalized solution plan is responsive to the request using a second machine learning model, taught by Williams, Durvasula, and Vasudevan, and subsequently obtaining additional information in response, and updating the personalized solution plan using the additional information, taught by Sejpal, as doing so increases user satisfaction, by preventing a faulty response by the model and modifying the response to improve it. Such a combination would be obvious.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Jadhav.
Regarding claim 11,
Williams and Durvasula jointly teach The computer-implemented method of claim 5
Jadhav teaches the following further limitation that neither Williams nor Durvasula teaches:
further comprising modifying the personalized [solution plan] based on applying the persona and the personalized [solution] plan ((Jadhav [0008]) “render the inclusive system design on the fly corresponding to the identified dynamic persona as a run-time interface to the user using one of the defined static persona from the design pool”, rendering on the fly corresponds to modifying) as input to a second machine learning model configured to further personalize the personalized [solution] plan for the user identity ((Jadhav [0049]) “In accordance with an embodiment, the automated dynamic interface generation logic 304B is configured to receive one or more cognitive inputs from a designer pertaining to a best fit design solution and evolve through machine learning, thus capturing the designer's cognitive abilities and further configured to receive a dynamic persona corresponding to the captured behavioral pattern of the user for making an automated decision on a run-time interface”, Williams but not Jadhav teaches a solution plan)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, and Jadhav by taking the method of claim 5 for generating personalized solution plans, including a persona used to generate the plans, taught by Williams and Durvasula, and including using a persona and a plan as input to a machine learning model to further personalize a plan for a user, taught by Jadhav, as doing so increases user satisfaction by increasing the suitability of the plan for the particular user. Such a combination would be obvious.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Durvasula, further in view of Jadhav, further in view of Gnanasambandam.
Regarding claim 12,
Williams, Durvasula, and Jadhav jointly teach The computer-implemented method of claim 11,
Williams further teaches:
wherein the personalized solution plan comprises a plurality of solution instructions, ((Williams Abstract) “A computer system for personalized planning may include one or more processors configured to:…send an identification of the user and a prompt for the personalized assistance with the objective to an ML chatbot (or voice bot) to cause an ML model to divide the objective into one or more discrete steps and generate personalized instructions for performing the one or more discrete steps”)
Gnanasambandam teaches the following further limitation that neither Williams, nor Durvasula, nor Jadhav teaches:
and wherein further personalizing the solution plan comprises at least one of: replacing a first term of a text of the solution plan with a second term based in part on the persona, adding an additional solution instruction to the plurality of solution instructions of the solution plan, or removing a solution instruction of the plurality of solution instructions of the solution plan ((Gnanasambandam [0669]) “In some embodiments, modifying the care plan may include generating a second action instruction based on the patient data, and the processing device may cause the modified care plan including the action instruction and the second action instruction to be presented on the computing device of the medical personnel”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Williams, Durvasula, Jadhav, and Gnanasambandam by taking the method of claim 11 for generating personalized solution plans, including a plurality of solution instructions, taught by Williams, Durvasula, and Jadhav, and including modifying the solution plan by adding an additional instruction to the plan, taught by Gnanasambandam, as doing so could help a user in need of additional instruction by providing more granular and detailed steps for their plan that a more experienced user may not need explicitly stated to them. Such a combination would be obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arora et al. (U.S. Patent Application Publication No. 2019/0318219) teaches a system that provides customized responses to a user based on a developed user persona.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTOR A NAULT whose telephone number is (703) 756-5745. The examiner can normally be reached M - F, 12 - 8.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang can be reached at (571) 270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/V.A.N./Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
Prosecution Insights