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 .
This action is in response to the application and claims filed 10/26/2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
Claim Objections
Claim 16 is objected to because of the following informalities: the recitation of “matching learning model” in line 1 should read “machine learning model.” Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, 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. The analysis below of the claims’ subject matter eligibility follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Regarding independent claim 1, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is directed to a method, corresponding to a process - one of the statutory categories.
Step 2A Prong 1: The claim recites
“inferring a workflow template that maps to the problem statement by at least one machine learning model”
and populating the workflow template with the value.”
The “inferring a workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to infer a template that maps/corresponds to the received/observed problem statement, as described in specification “One or more machine learning models 150 can be executed to infer, predict, or otherwise determine a workflow template matching the problem statement. ” (see e.g., paragraph 37 of the specification). The above limitations in the context of this claim encompass inferring/predicting a workflow template that maps/corresponds to the received/observed problem statement (corresponding to mental processes which can be done mentally or by pen and paper).
The “populating the workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine populating with which value (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass “populating the workflow template with the value” (corresponding to mental processes which can be done mentally or by pen and paper).
Regarding the “machine learning model”, no details of the model or its training is recited, and the model is recited at a high level of generality and the model can be constructed by hand with pen and paper. Thus, the claimed “machine learning model”, under the BRI, in light of the specification, could be any model useable to infer a workflow template, which could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “problem statement”) with pen and paper. That is, the “machine learning model” limitation gives the indication that the model can be constructed by hand with pen and paper. Given a sufficiently small set of data items in the “problem statement”, nothing in the claim prohibits the “inferring a workflow template that maps to the problem statement” from being performed mentally or with pen and paper.
The machine learning model is recited at a high level of generality and therefore is being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
Step 2A Prong 2: The claim recites the additional elements:
“A method of generating a workflow, comprising: receiving a problem statement specified by a user in a natural language;”
“inferring … by at least one machine learning model;”
“receiving a parameter of the workflow template from a template repository;”
“generating a value of the parameter of the workflow template by the at least one machine learning model based on the problem statement;”
The “receiving a problem statement” limitation describes data gathering with “the problem statement” as the data. The “receiving a parameter of the workflow” limitation describes data gathering with “the parameter” as the data. These receiving limitations can be characterized as insignificant extra-solution activity (i.e., data gathering). See MPEP 2106.05(g).
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “inferring … by at least one machine learning model” and “generating a value of the parameter by the at least one machine learning model” limitations which amount to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., generic computer components – “machine learning model” performing generic computer functions), which does not integrate a judicial exception into a practical application. 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 is directed to an abstract idea.
Step 2B: The “receiving a problem statement” and “receiving a parameter of the workflow” limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “receiving a problem statement specified by a user in a natural language” and “receiving a parameter of the workflow” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
The “inferring … by at least one machine learning model” and “generating a value of the parameter by the at least one machine learning model” limitations 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, these additional elements represent mere instructions to apply an exception (i.e., the additional elements recite inferring a workflow template that maps to the problem statement by at least one machine learning model and generating a value of the parameter of the workflow template by the at least one machine learning model based on the problem statement). Mere instructions to infer a workflow template and generate parameters with a machine learning model do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/machine learning model on a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
This claim is not patent eligible.
Regarding claim 2, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 2 is directed to a method, corresponding to a process - one of the statutory categories. Claim 2 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites:
“comparing the score to a threshold;”
“and determining that the score satisfies the threshold.”
The “comparing the score” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the comparison between the score and the threshold (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass comparing an observed score to a threshold to determine if the score satisfies the threshold. (corresponding to mental processes which can be done mentally or by pen and paper).
The “determining that the score satisfies the threshold” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine if the score satisfies the threshold (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass receiving and comparing the score to the threshold and then assessing the score to see if it satisfies the threshold (i.e., evaluation/judgement/opinion based on an observed score and threshold value) (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
“receiving a score from the at least one machine learning model representing a
confidence level of accuracy of the value;”
The receiving a score limitation describes data gathering with the score as the data. The receiving can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “receiving a score from the at least one machine learning model” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., the machine learning model is generically recited without any details of its training) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations of “receiving a score from the at least one machine learning model” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d). A mere act to apply an exception using a generic act of receiving and transmitting cannot provide an inventive concept.
The receiving a score limitation does 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 element represents mere instructions to apply an exception (i.e., the additional element recites receiving data from a machine learning model for applying the abstract ideas). Mere instructions to do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/machine learning model on a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The claim is not patent eligible.
Regarding claim 3, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 3 is directed to a method, corresponding to a process, one of the statutory categories. Claim 3 is directed to a method as depending from claim 2 and 1, thus the analysis for patent eligibilities of claim 2 and of base claim 1 are is incorporated herein.
Regarding claim 3, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: See the analysis of claims 2 and 1 above. Step 2A Prong 2: The claim recites the additional elements:
“further comprising triggering generation of the workflow with a workflow system based on the workflow template.”
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “triggering generation of the workflow” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – “a workflow system” performing generic computer functions) , which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
Step 2B: The “triggering generation of the workflow” limitation does 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 element represents mere instructions to apply an exception (i.e., the additional element recites triggering a workflow for applying the abstract ideas). Mere instructions to trigger the action steps of a workflow do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/workflow on a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
This claim is not patent eligible.
Regarding claim 4, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 4 is directed to an apparatus, corresponding to a method, one of the statutory categories. Claim 4 is directed to an apparatus as depending from claims 2 and 1, thus the analysis for patent eligibility of claims 2 and 1 is incorporated herein.
Step 2A Prong 1: See the analysis of claims 2 and 1 above.
Step 2A Prong 2: The claim recites the additional elements:
“further comprising interacting with the user in the natural language to request and receive data associated with the workflow template by the at least one machine learning model.”
The “interacting with the user in the natural language” limitation describes data gathering with “data associated with the workflow template” as the data. Receiving such data can be characterized as insignificant extra-solution activity (amounts to necessary data gathering). See MPEP 2106.05(g).
Furthermore, the judicial exceptions are not integrated into a practical application. In particular, the claim recites the “interacting with the user in the natural language” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., the machine learning model is generically recited without any details of its training) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “receive data associated with the workflow template” limitation can be characterized as insignificant extra solution activity that is well understood routine and conventional. See MPEP 2106.05(g) and MPEP 2106.05(d)(II) provides that receiving or transmitting data over a network has been understood by the courts to be well-understood, routine and conventional.
According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations of “request and receive data associated with the workflow template” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
The “data associated with the workflow template” limitation does 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 element represents mere instructions to apply an exception (i.e., the additional element recites “by the at least one machine learning model” for applying the abstract ideas). Mere instructions to use the workflow template via machine learning model for receiving data purposes do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/machine learning model on a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The claim is not patent eligible.
Regarding claim 5, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 5 is directed to a method, corresponding to a process, one of the statutory categories. Claim 5 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites:
“determining context data from the problem statement;”
“mapping the context data to a first set of one or more parameters;”
“determining a second set of one or more parameters associated with the workflow
template;“
“and determining one or more missing parameters based on a comparison of the first set of one or more parameters and the second set of one or more parameters.”
The “determining context data” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine “context data (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass determining context data from the problem statement (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “mapping the context data” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the mapping of context data (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass context data to a first set of one or more parameters (corresponding to mental processes which can be done mentally or by pen and paper).
The “determining a second set of one or more parameters” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine a second set of one or more parameters (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass parameters associated with the workflow template (corresponding to mental processes which can be done mentally or by pen and paper).
The “determining one or more missing parameters” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine “one or more missing parameters” (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass missing parameters based on a comparison of a first and second set of one or more parameters (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 6, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 6 is directed to a method, corresponding to a process, one of the statutory categories. Claim 6 is directed to a method as depending from claim 5, thus the analysis for patent eligibilities of claim 5 and of base claim 1 are incorporated herein.
Step 2A Prong 1: The claim recites:
“wherein determining the context data comprises determining at least one of a condition, trigger, or action.”
The “determining the context data” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine what the context data comprises (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass context data which determines at least one of a condition, trigger or action (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 7, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 7 is directed to a method, corresponding to a process, one of the statutory categories. Claim 7 is directed to a method as depending from claims 5 and 1, thus the analysis for patent eligibility of claims 5 and 1 is incorporated herein.
Step 2A Prong 1: The claim recites:
“wherein interacting with the user further comprises translating the one or more missing parameters into one or more natural language requests for data with the at least one machine learning model.”
The “interacting with the user” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the translation of one or more missing parameters (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass translating missing parameters into one or more natural language requests for data with at least one Machine learning model (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 8, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 8 is directed to an apparatus, corresponding to a method, one of the statutory categories. Claim 8 is directed to an apparatus as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: See the analysis of claim 1 above.
Step 2A Prong 2: The claim recites the additional elements:
“wherein one of the at least one machine learning model is a generative pre-trained transformer.
The “machine learning model is a generative pre-trained transformer” limitation can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h).
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “machine learning model” limitation does 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 element represents a field of use and technological environment. Mere instructions to associate a machine learning model with a GPT – generative pre-trained transformer does not amount to significantly more than the judicial exception. As noted above, merely linking the use of a judicial exception to a particular technological environment or field of use cannot provide significantly more than the judicial exception.
The claim is not patent eligible.
Regarding claim 9, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 9 is directed to an apparatus, corresponding to a method, one of the statutory categories. Claim 9 is directed to an apparatus as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: See the analysis of claim 1 above.
Step 2A Prong 2: The claim recites the additional elements:
“further comprising receiving the problem statement specified by the user through a conversational virtual agent linked with a software application including a workflow system.”
The “receiving the problem statement” limitation describes data gathering with the problem statement as the data. Obtaining such problem statements through a conversational virtual agent can be characterized as insignificant extra-solution activity (amounts to necessary data gathering). See MPEP 2106.05(g).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “receiving the problem statement” does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving data/the problem statement are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “receiving the problem statement specified by the user through a conversational virtual agent” are well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
This claim is not patent eligible.
Regarding claim 10, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 10 is directed to an apparatus, corresponding to a method, one of the statutory categories. Claim 10 is directed to an apparatus as depending from claims 9 and 1, thus the analysis for patent eligibility of claims 9 and 1 is incorporated herein.
Step 2A Prong 1: See the analysis of claims 9 and 1 above.
Step 2A Prong 2: The claim recites the additional elements:
“wherein the software application is a financial management software application.”
The “software application” limitation can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h).
Accordingly, these/this additional element(s) do/does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “software application” limitation does 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 element represents a field of use and technological environment. Mere instructions to narrow software application to financial management software application does not amount to significantly more than the judicial exception. As noted above, merely linking the use of a judicial exception to a particular technological environment or field of use cannot provide significantly more than the judicial exception.
This claim is not patent eligible.
Regarding independent claim 11, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 11 is directed to a system, corresponding to a machine, one of the statutory categories.
Step 2A Prong 1: The claim recites
“infer a workflow template that maps to the problem statement by at least
one machine learning model;”
“and populate the workflow template with the value.”
The “infer a workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to infer a template that maps/corresponds to the received/observed problem statement, as described in specification “One or more machine learning models 150 can be executed to infer, predict, or otherwise determine a workflow template matching the problem statement. ” (see e.g., paragraph 37 of the specification). The above limitations in the context of this claim encompass inferring/predicting a workflow template that maps/corresponds to the received/observed problem statement (corresponding to mental processes which can be done mentally or by pen and paper).
The “populate the workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine which value to populate (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass populating the workflow template with the value (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
“A system, comprising: at least one processor; and at least one memory coupled to the at least one processor that stores instructions, that when executed by the at least one processor, cause the system to: receive a problem statement specified by a user in a natural language;”
“receive a parameter of the workflow template from a template repository;”
“infer … by at least one machine learning model;”
“generate a value of the parameter of the workflow template by the at least
one machine learning model based on the problem statement;”
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “at least one processor” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – “a processor” performing generic computer function) , which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “at least one memory coupled to the at least one processor” and “infer a workflow template that maps to the problem statement by at least one machine learning model” limitations which amount to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., instructions executed by a generic processor) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
The “receive a parameter of the workflow” limitation describes data gathering with “the parameter” as the data. These receiving limitations can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “infer … by at least one machine learning model” and “generate a value of the parameter” limitations which amount to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool/machine learning model on a computer to perform an abstract idea (i.e., Machine learning model is generically recited without any details of its training), which does not integrate a judicial exception into a practical application. 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 is directed to an abstract idea.
Step 2B: The “at least one processor“ and “generate a value of the parameter” limitations 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, these additional elements represent mere instructions to apply an exception (i.e., these additional elements recite at least one processor and using a machine learning model for applying the abstract ideas). Mere instructions to use a processor and machine learning model do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The “at least one memory coupled to the at least one processor that stores instructions” and “receive a parameter of the workflow” limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “memory … that stores instructions” and “receive a parameter” are well-understood, routine, conventional activities of storing information in memory and receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
The claim is not patent eligible.
Regarding claim 12, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 12 is directed to a system, corresponding to a machine, one of the statutory categories. Claim 12 is directed to a system as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prong 1: The claim recites
“compare the score to a threshold; ”
“And determine that the score satisfies the threshold.”
The “comparing the score” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the difference between the score and the threshold (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass if the score satisfies the threshold (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “determining that the score satisfies the threshold” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine if the score satisfies the threshold (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass receiving and comparing the score to the threshold and then assessing the score to see if it satisfies the threshold (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
“wherein the instructions further cause the system to: receive a score from the at least one machine learning model representing a confidence level of accuracy of the value generated”
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “further cause the system to” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., instructions which further causes the system to do something) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the “receive a score” limitation describes data gathering with the score as the data. Receiving such a score can be characterized as insignificant extra-solution activity (amounts to necessary data gathering). See MPEP 2106.05(g).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations of “receiving a score from the at least one machine learning model” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d). A mere act to apply an exception using a generic act of receiving and transmitting cannot provide an inventive concept.
The “wherein the instructions further cause the system to: receive a score” limitation does 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 element represents mere instructions “wherein the instructions further cause the system to: receive a score from the at least one machine learning model representing a confidence level of accuracy of the value generated” to apply an exception (i.e., the additional element recites receiving data from a machine learning model for applying the abstract ideas). Mere instructions do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/machine learning model on a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The claim is not patent eligible.
Regarding claim 13, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 13 is directed to a system, corresponding to a machine, one of the statutory categories. Claim 13 is directed to a system as depending from claim 12, thus the analysis for patent eligibilities of claim 12 and of base claim 11 are incorporated herein.
Regarding claim 13, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: See the analysis of claims 12 and 11 above. Step 2A Prong 2: The claim recites the additional elements:
“wherein the instructions further cause the system to trigger generation of the workflow with a workflow system based on the workflow template.”
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “trigger generation of the workflow” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – “system, comprising: at least one processor; and at least one memory coupled to the at least one processor that stores instructions” performing generic computer function) , which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “trigger generation of the workflow” limitation does 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 element represents mere instructions to apply an exception (i.e., the additional element recites triggering a workflow for applying the abstract ideas). Mere instructions to trigger the action steps of a workflow do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/workflow on a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
This claim is not patent eligible.
Regarding claim 14, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 14 is directed to a system, corresponding to a machine, one of the statutory categories. Claim 14 is directed to an apparatus as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prong 1: The claim recites:
determine context data from the problem statement;
map the context data to a first set of one or more parameters;
identify a second set of one or more parameters associated with the workflow template;
and determine one or more missing parameters based on a comparison of the first set of one or more parameters and the second set of one or more parameters.
The “determine context data” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine “context data (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass determining context data from the problem statement (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “map the context data” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the mapping of context data (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass context data to a first set of one or more parameters (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “identify a second set of one or more parameters” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine a second set of one or more parameters (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass parameters associated with the workflow template (corresponding to mental processes which can be done mentally or by pen and paper).
The “determine one or more missing parameters” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine “one or more missing parameters” (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass missing parameters based on a comparison of a first and second set of one or more parameters (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 15, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 15 is directed to a system, corresponding to a machine, one of the statutory categories. Claim 15 is directed to a system as depending from claim 14, thus the analysis for patent eligibilities of claim 14 and of base claim 11 are incorporated herein.
Step 2A Prong 1: The claim recites:
“wherein interacting with the user further comprises translating the one or more missing parameters into one or more natural language requests for data with the at least one machine learning model.”
The “interacting with the user” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the translation of one or more missing parameters (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass translating missing parameters into one or more natural language requests for data with at least one Machine learning model (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 16, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 16 is directed to an apparatus, corresponding to a machine, one of the statutory categories. Claim 16 is directed to an apparatus as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prong 1: See the analysis of claim 11 above.
Step 2A Prong 2: The claim recites the additional elements:
“wherein one of the at least one matching1 learning model is
a generative pre-trained transformer.
The “machine learning model is a generative pre-trained transformer” limitation can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “machine learning model is a generative pre-trained transformer” limitation does 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 element represents a field of use and technological environment. Mere instructions to associate a machine learning model with a GPT – generative pre-trained transformer does not amount to significantly more than the judicial exception. As noted above, merely linking the use of a judicial exception to a particular technological environment or field of use cannot provide significantly more than the judicial exception.
The claim is not patent eligible.
Regarding claim 17, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 17 is directed to a system, corresponding to a machine, one of the statutory categories. Claim 17 is directed to a system as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prong 1: Claim 17 does not recite any abstract ideas but is dependent on claims 11. See the analysis of claims 11 above.
Step 2A Prong 2: The claim recites
“wherein the instructions further cause the system to receive the problem statement specified by the user through a conversational virtual agent linked with a software application including a workflow system.”
The “receive the problem statement” limitation describes data gathering with the problem statement as the data. Obtaining such problem statements through a conversational virtual agent can be characterized as insignificant extra-solution activity (amounts to necessary data gathering). See MPEP 2106.05(g).
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “receive the problem statement” does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving data/the problem statement are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “receive the problem statement specified by the user through a conversational virtual agent” are well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
This claim is not patent eligible.
Regarding claim 18, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 18 is directed to an apparatus, corresponding to a machine, one of the statutory categories. Claim 18 is directed to an apparatus as depending from claims 17 and 11, thus the analysis for patent eligibility of claims 17 and 11 is incorporated herein.
Step 2A Prong 1: Claim 18 does not recite any abstract ideas but is dependent on claims 17 and 11. See the analysis of claims 17 and 11 above.
Step 2A Prong 2: The claim recites the additional elements:
“wherein the software application is a financial management software application.”
The “software application” limitation can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h).
Accordingly, these/this additional element(s) do/does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:The “software application” limitation does 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 element represents a field of use and technological environment. Mere instructions to narrow software application to financial management software application does not amount to significantly more than the judicial exception. As noted above, merely linking the use of a judicial exception to a particular technological environment or field of use cannot provide significantly more than the judicial exception.
This claim is not patent eligible.
Regarding independent claim 19, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 19 is directed to a method, corresponding to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites
“inferring a workflow template that maps to the problem statement by at least one machine learning model;”
“populating the workflow template with the data by the at least one machine learning model;”
The “inferring a workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine “One or more machine learning models 150 can be executed to infer, predict, or otherwise determine a workflow template matching the problem statement. ” (see e.g., paragraph 37 of specification). The above limitations in the context of this claim encompass “inferring/predicting a workflow template” (see e.g., paragraph 37 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “populating the workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine “the data to populate the workflow template” (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass using machine learning model to populate workflow template with data to trigger generating a workflow (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
“A method of workflow generation, comprising: receiving a problem statement specified by a user in a natural language through a conversational virtual agent linked with a software application including a workflow system;
“interacting with the user in the natural language through the conversational virtual agent to request and receive data associated with the workflow template by the at least one machine learning model;”
and triggering generation of a workflow by the workflow system with the workflow template.
The “receiving a problem statement” limitation describes data gathering with “the problem statement” as the data. Receiving such problem statement can be characterized as insignificant extra-solution activity (amounts to necessary data gathering). See MPEP 2106.05(g).
The “interacting with the user in the natural language” limitation describes data gathering with “data associated with the workflow template” as the data. Receiving such data can be characterized as insignificant extra-solution activity (amounts to necessary data gathering). See MPEP 2106.05(g).
Furthermore, the judicial exceptions are not integrated into a practical application. In particular, the claim recites the “interacting with the user in the natural language” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., Machine learning model is generically recited without any details of its training) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “interacting with the user in the natural language ” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – “machine learning model” performing generic computer functions), which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, these/this additional element(s) do/does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “receiving a problem statement” and “interacting with the user in the natural language” does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “receiving a problem statement specified by a user in a natural language through a conversational virtual agent” and “interacting with the user in the natural language through the conversational virtual agent” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
The “interacting with the user in the natural language” does 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 element represents mere instructions to apply an exception. Mere instructions to receive data associated with the workflow template by the at least one machine learning model do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer/machine learning model via a computer cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 20, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 20 is directed to a method, corresponding to a process, one of the statutory categories. Claim 20 is directed to a method as depending from claim 19, thus the analysis for patent eligibility of claim 19 is incorporated herein.
Step 2A Prong 1: The claim recites:
“determining one or more missing parameters of the workflow template;”
“translating the one or more missing parameters into one or more natural language requests for the data with the at least one machine learning model;”
“and updating the workflow template to include parameter values received in response to the one or more natural language requests.”
The “determining one or more missing parameters” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine one or more missing parameters of the workflow template (see e.g., paragraph 46 of specification). The above limitations in the context of this claim encompass “ Method 500 starts at block 510 with determining a first set of parameters or attributes associated with a workflow template.” (see e.g., paragraph 46 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “translating the one or more missing parameters” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine the translation of one or more missing parameters (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass translating missing parameters into one or more natural language requests for data with at least one Machine learning model (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
The “updating the workflow template” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to determine populating with which value/updating with which values (see e.g., paragraph 76 of specification). The above limitations in the context of this claim encompass “populating the workflow template with the value/ updating the workflow template with which values” (see e.g., paragraph 76 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2:
“sending the one or more natural language requests to the user with the conversational virtual agent as part of interacting with the user;”
The “natural language requests” limitation is an insignificant extra-solution activity that is not integrated into the claim as a whole and does not add a meaningful limitation to the above-noted mental process specified in this claim. That is, “sending the one or more natural language requests” amounts to necessary data outputting (See MPEP § 2106.05(g)).
Step 2B: The claim recites
The “sending the one or more natural language requests” limitation can be characterized as insignificant extra solution activity that is well understood routine and conventional. See MPEP 2106.05(d)(II) example (ii) provides that receiving or transmitting data over a network has been understood by the courts to be well-understood, routine and conventional.
According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations of “sending the one or more natural language requests ” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of performing a mental process, data gathering (i.e., insignificant extra solution activities) and sending/transmitting information over a network (WURC). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-7, 9, 11, 14-15, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Batman, Sinan (U.S. Publication No. 20220230714, hereinafter “Batman”).
Regarding independent claim 1, Batman discloses the invention as claimed including A method of generating a workflow, comprising: receiving a problem statement specified by a user in a natural language; (see, e.g., paragraphs 12 and 71, “The workflow system may generate answers to the inquiries based on the patient data and may provide the answers to the clinical device. “and “may input an inquiry (e.g., verbal or written) into the second clinical device 140.” [i.e., workflow generates inquiry/problem statement as verbal or written language/natural language]);
inferring a workflow template that maps to the problem statement by at least one machine learning model; (see, e.g., paragraphs 108 and 142, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “modifying the template by training a machine learning model based on the inquiry and the response” [i.e., inferring a workflow template/modifying the template which the workflow system generates based on a problem statement/inquiry via a machine learning model]);
receiving a parameter of the workflow template from a template repository; (see, e.g., paragraphs 108 and 139, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “retrieving, from the template database, a template associated with the second care provider; identifying patient data including: message data that includes at least one text, audio, or video message about the patient that is transmitted to or from a first computing device associated with the first care provider, electronic medical record (EMR) data that includes at least a portion of an EMR of the patient, and sensor data that includes a physiological parameter of the patient detected by a hospital bed on which the patient is supported; generating a handoff report based on the template and the patient data” [i.e., identifying/receiving data of a template generated by a workflow system that includes parameters from template database/template repository]);
generating a value of the parameter of the workflow template by the at least one machine learning model based on the problem statement; (see, e.g., paragraphs 108 and 141, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “second patients, second sensor data that includes physiological parameters of the second patients detected by second hospital beds, outcome data indicating conditions of the second patients, and task data indicating tasks performed on the second patients by the third care providers; and training the machine learning model … generating a response to the inquiry based on the patient data; transmitting, to the second computing device, the response; modifying the template by training a machine learning model based on the inquiry and the response” [i.e., values/data/sensor data of parameters/physiological parameters of the template generated by a workflow system trained by machine learning models based on the inquiry/problem statement]); and
populating the workflow template with the value. (see, e.g., paragraphs 108 and 73, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “For example, the workflow system 108 may add the additional EMR data to the handoff report… According to some examples, the computing model may generate a template associated with the second care provider 138 that indicates relevant components of the handoff report for the second care provider 138. According to some examples, the template is stored in a database and can be accessed in advance of generating a new handoff report for the second care provider 138.” [i.e., Adding/populating data/value into the handoff report which is associated with the template generated by a workflow system (handoff report in the form of or using the template) which has relevant components of the handoff report]).
Regarding claim 5, as discussed above, Batman discloses the method of claim 1.
Batman further discloses determining context data from the problem statement; (see, e.g., paragraph 12, “workflow system may generate answers to the inquiries” [e.g., answers/context data are generated/determined from inquires/problem statement]);
mapping the context data to a first set of one or more parameters; (see, e.g., paragraph 124, “In various examples, the second predictive model 706 includes one or more parameters that are optimized based on training data. The training data may include the inquiry 702 and the response 704. Thus, the second predictive model 706 adapts the template based on the inquiry 702 and the response 704. For example, the second predictive model 706 may learn, based on the inquiry 702, that the particular care provider would prefer to identify the heart rates of patients in the second handoff report, and may therefore adapt the template to include a component corresponding to the heart rates.” [e.g., The template which includes the parameters are optimized/mapped based on the training data]);
determining a second set of one or more parameters associated with the workflow template (see, e.g., paragraphs 108 and 124, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “the second predictive model 706 includes one or more parameters that are optimized based on training data. The training data may include the inquiry 702 and the response 704. Thus, the second predictive model 706 adapts the template based on the inquiry 702 and the response 704.” [e.g., second set of one or more parameters because it is the parameters of the second predictive model which is associated with a template generated by a workflow system]);
determining one or more missing parameters based on a comparison of the first set of one or more parameters and the second set of one or more parameters (see, e.g., paragraphs 5, 11, 110, and 124, “However, in practice, key patient information can be lost as the patients are transferred between care providers.” – paragraph 5, “the workflow system is configured to monitor patient data associated with patients being cared for by a first care provider and to generate, based on the patient data, a handoff report for a second care provider taking over care of the patients. In various cases, the workflow system determines what data, among a voluminous amount of possible patient data, is relevant to the second care provider. The workflow system may generate the handoff report based on a default template associated with the clinical environment or a personalized template associated with the second care provider” – paragraph 11, “In various examples, the first predictive model 504 includes one or more parameters that are optimized based on training data.” – paragraph 110, “In various examples, the second predictive model 706 includes one or more parameters that are optimized based on training data.” – paragraph 124 [e.g., Key patient information/parameters can be lost between care providers (i.e., the transfer of patient and hand off report from first to second care providers is based off template which includes parameters and data) During the transfer, a comparison is made of the data from first set to second set of parameters to determine the missing/lost parameter]).
Regarding claim 6, as discussed above, Batman discloses the method of claim 5.
Batman further discloses wherein determining the context data comprises determining at least one of a condition, trigger, or action (see, e.g., Abstract, “The example method further includes determining, based on the patient data, a condition of the patient and a task associated with the patient. A report including the condition of the patient and the task is generated and transmitted to a computing device associated with the care provider.” [e.g., a condition is determined from the data which then generates/triggers the task/action]).
Regarding claim 7, as discussed above, Batman discloses the method of claim 5.
Batman further discloses wherein interacting with the user further comprises translating the one or more missing parameters into one or more natural language requests for data with the at least one machine learning model (see, e.g., paragraph 71, “ In some implementations, the handoff report may act as a “personal digital assistant” to help bring the second care provider 138 up-to-speed on the patients. In examples in which the second care provider 138 would like additional patient data about patient 104, the second care provider 138 may input an inquiry (e.g., verbal or written) into the second clinical device 140... The workflow system 108 may include a computing model configured to identify words, such as “chance,” “patient 104,” and “sepsis,” and may also identify the meaning of the inquiry. In response, the workflow system 108 may evaluate previously obtained patient data of the patient 104 relevant to the sepsis risk of the patient.” [e.g., The second care provider may want additional patient data about the patient (missing data). The inquiry, either verbal or written (natural language) is put into the workflow system which contains a computing model configured to identify words and meanings of phrases.]).
Regarding claim 9, as discussed above, Batman discloses the method of claim 1.
Batman further discloses further comprising receiving the problem statement specified by the user through a conversational virtual agent linked with a software application including a workflow system (see, e.g., paragraphs 71 and 26, “In various implementations, the workflow system 108 may work like a virtual assistant in answering questions of the second care provider 138 after the first care provider 102 has left the premises. The workflow system 108 may, when prompted by the second care provider 138, share various details about the condition of the patient 104 in a manner similar to a conversation.” “The workflow system 108 may be implemented in hardware, software, or a combination thereof. For example, the workflow system 108 may include software being executed on one or more servers located in the clinical environment or remote from the clinical environment.” [e.g., problem statement/ details about the condition of the patient, is conveyed to the virtual agent/virtual assistant which is linked to the workflow system]).
Regarding independent claim 11, Batman discloses the invention as claimed including A system, comprising: at least one processor; (see, e.g., paragraph 135, “The memory 902 may include various instructions (e.g., instructions in the workflow system 108 and/or template database 904), which can be executed by at least one processor 906 to perform operations.” [i.e., System can be executed by at least one processor]);
and at least one memory coupled to the at least one processor that stores instructions, that when executed by the at least one processor, cause the system to: receive a problem statement specified by a user in a natural language; (see, e.g., paragraphs 135 and 139, “The memory 902 may include various instructions (e.g., instructions in the workflow system 108 and/or template database 904), which can be executed by at least one processor 906 to perform operations.” “A workflow system, including: at least one processor; and memory storing a template database and instructions that, when executed by the at least one processor, cause the workflow system to perform operations including: determining that care of a patient is being transferred from a first care provider to a second care provider at a particular time; retrieving, from the template database, a template associated with the second care provider; identifying patient data including: message data that includes at least one text, audio, or video message about the patient that is transmitted to or from a first computing device associated with the first care provider, electronic medical record (EMR) data that includes at least a portion of an EMR of the patient, and sensor data that includes a physiological parameter of the patient detected by a hospital bed on which the patient is supported” [i.e., memory includes/stores instructions which can be executed by at least one processor and causes system to receive text, audio and video message which means the message will be in natural language]);
infer a workflow template that maps to the problem statement by at least one machine learning model (see, e.g., paragraphs 108 and 142, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “modifying the template by training a machine learning model based on the inquiry and the response” [i.e., (inferring a workflow template/modifying the template generated by a workflow system) based on a problem statement/inquiry via a machine learning model]);
receive a parameter of the workflow template from a template repository (see, e.g., paragraphs 108 and 139, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “retrieving, from the template database, a template associated with the second care provider; identifying patient data including: message data that includes at least one text, audio, or video message about the patient that is transmitted to or from a first computing device associated with the first care provider, electronic medical record (EMR) data that includes at least a portion of an EMR of the patient, and sensor data that includes a physiological parameter of the patient detected by a hospital bed on which the patient is supported; generating a handoff report based on the template and the patient data” [i.e., identifying/receiving data of a template generated by a workflow system that includes parameters from (template database/template repository]);
generate a value of the parameter of the workflow template by the at least one machine learning model based on the problem statement (see, e.g., paragraph 108 and 141, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “second patients, second sensor data that includes physiological parameters of the second patients detected by second hospital beds, outcome data indicating conditions of the second patients, and task data indicating tasks performed on the second patients by the third care providers; and training the machine learning model… generating a response to the inquiry based on the patient data; transmitting, to the second computing device, the response; modifying the template by training a machine learning model based on the inquiry and the response;” [i.e., (values/data/sensor data) of (parameters/physiological parameters) of the template generated by a workflow system trained by machine learning models based on the inquiry/problem statement]);
and populate the workflow template with the value (see, e.g., paragraph 108 and 73, “the workflow system 108 may use preconfigured templates to generate the handoff report 502” and “For example, the workflow system 108 may add the additional EMR data to the handoff report… According to some examples, the computing model may generate a template associated with the second care provider 138 that indicates relevant components of the handoff report for the second care provider 138. According to some examples, the template is stored in a database and can be accessed in advance of generating a new handoff report for the second care provider 138.” [i.e., adding/populating data/value into the handoff report which is associated with the template generated by a workflow system (handoff report in the form of or using the template) which has relevant components of the handoff report]).
Regarding claim 14, as discussed above, Batman discloses the system of claim 11.
Batman further discloses wherein the instructions further cause the system to: determine context data from the problem statement; (see, e.g., paragraph 12, “workflow system may generate answers to the inquiries” [e.g., answers/context data are generated/determined from inquires/problem statement]);
map the context data to a first set of one or more parameters; (see, e.g., paragraph 124, “In various examples, the second predictive model 706 includes one or more parameters that are optimized based on training data. The training data may include the inquiry 702 and the response 704. Thus, the second predictive model 706 adapts the template based on the inquiry 702 and the response 704. For example, the second predictive model 706 may learn, based on the inquiry 702, that the particular care provider would prefer to identify the heart rates of patients in the second handoff report, and may therefore adapt the template to include a component corresponding to the heart rates.” [e.g. The template which includes the parameters are optimized/mapped based on the training data]);
identify a second set of one or more parameters associated with the workflow template (see, e.g., paragraph 124, “the second predictive model 706 includes one or more parameters that are optimized based on training data. The training data may include the inquiry 702 and the response 704. Thus, the second predictive model 706 adapts the template based on the inquiry 702 and the response 704.” [e.g., second set of one or more parameters because it is the parameters of the second predictive model which is associated with a template]);
determine one or more missing parameters based on a comparison of the first set of one or more parameters and the second set of one or more parameters. (see, e.g., paragraphs 5, 11, 110, and 124, “However, in practice, key patient information can be lost as the patients are transferred between care providers.” – paragraph 5, “the workflow system is configured to monitor patient data associated with patients being cared for by a first care provider and to generate, based on the patient data, a handoff report for a second care provider taking over care of the patients. In various cases, the workflow system determines what data, among a voluminous amount of possible patient data, is relevant to the second care provider. The workflow system may generate the handoff report based on a default template associated with the clinical environment or a personalized template associated with the second care provider” – paragraph 11, “In various examples, the first predictive model 504 includes one or more parameters that are optimized based on training data.” – paragraph 110, “In various examples, the second predictive model 706 includes one or more parameters that are optimized based on training data.” – paragraph 124 [e.g., Key patient information/parameters can be lost between care providers (i.e., the transfer of patient and hand off report from first to second care providers is based off template which includes parameters and data) During the transfer, a comparison is made of the data from first set to second set of parameters to determine the missing/lost parameter]).
Regarding claim 15, as discussed above, Batman discloses the methods of claim 14.
Batman further discloses wherein interacting with the user further comprises translating the one or more missing parameters into one or more natural language requests for data with the at least one machine learning model. (see, e.g., paragraph 71, “In some implementations, the handoff report may act as a “personal digital assistant” to help bring the second care provider 138 up-to-speed on the patients. In examples in which the second care provider 138 would like additional patient data about patient 104, the second care provider 138 may input an inquiry (e.g., verbal or written) into the second clinical device 140... The workflow system 108 may include a computing model configured to identify words, such as “chance,” “patient 104,” and “sepsis,” and may also identify the meaning of the inquiry. In response, the workflow system 108 may evaluate previously obtained patient data of the patient 104 relevant to the sepsis risk of the patient.” [e.g., The second care provider may want additional patient data about the patient (missing data). The inquiry, either verbal or written (natural language) is put into the workflow system which contains a computing model configured to identify words and meanings of phrases.]);
Regarding claim 17, as discussed above, Batman discloses the system of claim 11.
Batman further discloses wherein the instructions further cause the system to receive the problem statement specified by the user through a conversational virtual agent linked with a software application including a workflow system. (see, e.g., paragraphs 71 and 26, “In various implementations, the workflow system 108 may work like a virtual assistant in answering questions of the second care provider 138 after the first care provider 102 has left the premises. The workflow system 108 may, when prompted by the second care provider 138, share various details about the condition of the patient 104 in a manner similar to a conversation. “The workflow system 108 may be implemented in hardware, software, or a combination thereof. For example, the workflow system 108 may include software being executed on one or more servers located in the clinical environment or remote from the clinical environment.” [e.g., problem statement/ details about the condition of the patient, is conveyed to the virtual agent/virtual assistant which is linked to the workflow system]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-4, 8, 12-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Batman as applied to claims 1 and 11 above and further in view of Ripa, Bogdan et al. (U.S. Publication No. 20230107233, hereinafter “Ripa”).
Regarding claim 2, as discussed above, Batman discloses the method of claim 1.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose “receiving a score from the at least one machine learning model representing a confidence level of accuracy of the value;
comparing the score to a threshold;
and determining that the score satisfies the threshold.”
In the same field, analogous art Ripa teaches receiving a score from the at least one machine learning model representing a confidence level of accuracy of the value (see, Ripa, paragraph 144, “The AI/Machine learning model may be trained over multiple epochs until it reaches a good level of accuracy (e.g., 97% or better using an F2 or F4 threshold for detection and approximately 2,000 epochs). This accuracy level may be determined in some embodiments using an F1 score, an F2 score, an F4 score, or any other suitable technique without deviating from the scope of the invention.” [i.e., F1 score from the Machine learning model/ machine learning model representing accuracy]);
comparing the score to a threshold; (see, paragraph 128, “only those results with a confidence score that meets or exceeds a confidence threshold may be provided in some embodiments.” [i.e., Comparing the score to see if it meets or exceeds a confidence threshold]).
and determining that the score satisfies the threshold (see, paragraph 128, “For instance, if the confidence threshold is 80%, outputs with confidence scores exceeding this amount may be used and the rest may be ignored.” [i.e., If the score exceeds/satisfies the threshold at 80%, the amount may be used]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that this accuracy level may be determined in some embodiments... Likewise, only those results with a confidence score that meets or exceeds a confidence threshold may be provided in some embodiments. Additionally, if the confidence threshold is 80%, outputs with confidence scores exceeding this amount may be used and the rest may be ignored. (see Ripa, e.g., paragraph 144 and 128). Doing so would have allowed Batman to use Ripa‘s “trained AI/Machine learning models that achieves an accuracy level of 97% which can then be used to accomplish a task such as performing OCR or deploying another AI/Machine learning models to find relationships between graphical elements.” Likewise, would have allowed Batman to use Ripa‘s technique wherein the neural network may learn to identify graphical elements with these characteristics with a high confidence. Some common types of confidence scores include a decimal number between 0 and 1 (which can be interpreted as a percentage of confidence), a number between negative ∞ and positive ∞, or a set of expressions (e.g., “low,” “medium,” and “high”). Various post-processing calibration techniques may also be employed in an attempt to obtain a more accurate confidence score, such as temperature scaling, batch normalization, weight decay, negative log likelihood (NLL), etc. as suggested by Ripa (see Ripa, paragraph 146 and 128).
Regarding claim 3, as discussed above, Batman discloses the method of claim 2.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose further comprising triggering generation of the workflow with a workflow system based on the workflow template.
In the same field, analogous art Ripa teaches further comprising triggering generation of the workflow with a workflow system based on the workflow template. (see, paragraph 66, “Thus, when building automations, hyper-automation system 100 may provide user interfaces, development environments, API integration, pre-built and/or custom-built AI/Machine learning models, development templates, integrated development environments (IDEs), and advanced AI capabilities. Hyper-automation system 100 enables development, deployment, management, configuration, monitoring, debugging, and maintenance of RPA robots in some embodiments, which may provide automations for hyper-automation system 100.” [i.e., system/hyper-automation system has a template/development template, and system triggers workflow actions such as development, deployment, management, configuration, monitoring, debugging, and maintenance]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that when building automations, hyper-automation system 100 may provide user interfaces, development environments, API integration, pre-built and/or custom-built AI/Machine learning models, development templates, integrated development environments (IDEs), and advanced AI capabilities. Hyper-automation system 100 enables development, deployment, management, configuration, monitoring, debugging, and maintenance of RPA robots in some embodiments, which may provide automations for hyper-automation system 100. (see Ripa, e.g., paragraph 66). Doing so would have allowed Batman to use Ripa‘s automations in regards to hyper-automation systems to implement access control and governance restrictions at the robot and/or robot design application level, for the purpose of added level of security and compliance which is put into the automation process development pipeline, as suggested by Ripa (see Ripa, paragraph 66).
Regarding claim 4, as discussed above, Batman discloses the methods of claim 2.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose further comprising interacting with the user in the natural language to request and receive data associated with the workflow template by the at least one machine learning model.
In the same field, analogous art Ripa teaches further comprising interacting with the user in the natural language to request and receive data associated with the workflow template by the at least one machine learning model (see, paragraphs 37, 179 and 71, “In certain embodiments, both local and global AI/Machine learning models may be included. For instance, the local AI/Machine learning model may learn preferences of a given user while the global AI/Machine learning model learns collective preferences from many or all users.” – paragraph 37, “The data structure may include associations of labels to values, and potentially associations of label/value pairs to screen(s), which may assist in template functionality” – paragraph 179, “Pre-built AI/Machine learning models, model templates, and various deployment options may make such functionality accessible even to those who are not data scientists. Deployed automations (e.g., RPA robots) may call AI/Machine learning models from the AI center, such as AI/Machine learning models 132. Performance of the AI/Machine learning models may be monitored, and be trained and improved using human-validated data, such as that provided by data review center 160” – paragraph 71 [i.e., AI/Machine learning model collects preferences/values/data from users, and receiving data/values that may assist template functionality/for the template using machine learning model]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that “The data structure may include associations of labels to values, and potentially associations of label/value pairs to screen(s), which may assist in template functionality.” (see Ripa, e.g., paragraph 179). Doing so would have allowed Batman to use Ripa’s technique from previous data entry sessions may be used to make the semantic logic more accurate and/or to provide templates for future completions, as suggested by Ripa (see Ripa, paragraph 179).
Regarding claim 8, as discussed above, Batman discloses the method of claim 1.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose wherein one of the at least one machine learning model is a generative pre-trained transformer.
In the same field, analogous art Ripa teaches wherein one of the at least one machine learning model is a generative pre-trained transformer. (see, paragraph 30, “However, in some embodiments, the source may not have a schema per-se. In such cases, a natural language processing (NLP) model may be employed. For instance, GPT-3 may be used, which is an autoregressive language model that uses deep learning to produce human-like text.” [i.e., An NLP model may be employed such as GPT-3 which is a generative pre-trained transformer]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that “in some embodiments, the source may not have a schema per-se. In such cases, a natural language processing (NLP) model may be employed. For instance, GPT-3 may be used, which is an autoregressive language model that uses deep learning to produce human-like text.” see Ripa, e.g., paragraph 30). Doing so would have allowed Batman to use Ripa‘s inputs to the NLP model to be the source document and a description of what the user wants to extract (e.g., in the form of a paragraph of text) in plain English so that the source document may then be analyzed and information therein may be extracted using the NLP model based on this input and so that semantic understanding can be applied to the text of the document as a whole by the NLP model to “guess” which information is desired, as suggested by Ripa (see Ripa paragraph 30).
Regarding claim 12, as discussed above, Batman discloses the system of claim 11.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose “wherein the instructions further cause the system to: receive a score from the at least one machine learning model representing a confidence level of accuracy of the value generated;
compare the score to a threshold;
and determine that the score satisfies the threshold.”
In the same field, analogous art Ripa teaches wherein the instructions further cause the system to: receive a score from the at least one machine learning model representing a confidence level of accuracy of the value generated; (see, paragraph 144, “The AI/Machine learning model may be trained over multiple epochs until it reaches a good level of accuracy (e.g., 97% or better using an F2 or F4 threshold for detection and approximately 2,000 epochs). This accuracy level may be determined in some embodiments using an F1 score, an F2 score, an F4 score, or any other suitable technique without deviating from the scope of the invention.” [i.e., F1 score from the Machine learning model/ machine learning model representing accuracy]).
compare the score to a threshold; (see, paragraph 128, “only those results with a confidence score that meets or exceeds a confidence threshold may be provided in some embodiments. ” [i.e., Comparing the score to see if it meets or exceeds a confidence threshold]).
and determine that the score satisfies the threshold. (see, paragraph 128, “For instance, if the confidence threshold is 80%, outputs with confidence scores exceeding this amount may be used and the rest may be ignored.” [i.e., If the score exceeds/satisfies the threshold at 80%, the amount may be used]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that this accuracy level may be determined in some embodiments... Likewise, only those results with a confidence score that meets or exceeds a confidence threshold may be provided in some embodiments. Additionally, if the confidence threshold is 80%, outputs with confidence scores exceeding this amount may be used and the rest may be ignored. (see Ripa, e.g., paragraphs 144 and 128). Doing so would have allowed Batman to use Ripa‘s “trained AI/Machine learning models that achieves an accuracy level of 97% which can then be used to accomplish a task such as performing OCR or deploying another AI/Machine learning models to find relationships between graphical elements.” Likewise, would have allowed Batman to use Ripa‘s technique wherein the neural network may learn to identify graphical elements with these characteristics with a high confidence and “ Various post-processing calibration techniques may also be employed in an attempt to obtain a more accurate confidence score”, as suggested by Ripa (see Ripa, paragraphs 146 and 128).
Regarding claim 13, as discussed above, Batman discloses the methods of claim 12.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose wherein the instructions further cause the system to trigger generation of the workflow with a workflow system based on the workflow template.
In the same field, analogous art Ripa teaches wherein the instructions further cause the system to trigger generation of the workflow with a workflow system based on the workflow template. (see, paragraph 66, “Thus, when building automations, hyper-automation system 100 may provide user interfaces, development environments, API integration, pre-built and/or custom-built AI/Machine learning models, development templates, integrated development environments (IDEs), and advanced AI capabilities. Hyper-automation system 100 enables development, deployment, management, configuration, monitoring, debugging, and maintenance of RPA robots in some embodiments, which may provide automations for hyper-automation system 100” [i.e., System/hyper-automation system as a template/development template wherein the system triggers workflow actions such as development, deployment, management, configuration, monitoring, debugging, and maintenance…]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that when building automations, hyper-automation system 100 may provide user interfaces, development environments, API integration, pre-built and/or custom-built AI/Machine learning models, development templates, integrated development environments (IDEs), and advanced AI capabilities. Hyper-automation system 100 enables development, deployment, management, configuration, monitoring, debugging, and maintenance of RPA robots in some embodiments, which may provide automations for hyper-automation system 100 (see Ripa, e.g., paragraph 66). Doing so would have allowed Batman to use Ripa‘s automations in regards to hyper-automation systems to implement access control and governance restrictions at the robot and/or robot design application level, for the purpose of added level of security and compliance which is put into the automation process development pipeline , as suggested by Ripa (see Ripa, paragraph 66).
Regarding claim 16, as discussed above, Batman discloses the method of claim 11.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose wherein one of the at least one matching learning model is a generative pre-trained transformer.
In the same field, analogous art Ripa teaches wherein one of the at least one machine learning model is a generative pre-trained transformer. (see, paragraph 30, “However, in some embodiments, the source may not have a schema per-se. In such cases, a natural language processing (NLP) model may be employed. For instance, GPT-3 may be used, which is an autoregressive language model that uses deep learning to produce human-like text.” [i.e., An NLP model may be employed such as GPT-3 which is a generative pre-trained transformer]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that “in some embodiments, the source may not have a schema per-se. In such cases, a natural language processing (NLP) model may be employed. For instance, GPT-3 may be used, which is an autoregressive language model that uses deep learning to produce human-like text.” see Ripa, e.g., paragraph 30). Doing so would have allowed Batman to use Ripa‘s method of using inputs to the NLP model to be the source document and a description of what the user wants to extract (e.g., in the form of a paragraph of text) in plain English. The source document may then be analyzed and information therein may be extracted using the NLP model based on this input. Semantic understanding is applied to the text of the document as a whole by the NLP model to “guess” which information is desired, as suggested by Ripa (see Ripa, paragraph 30).
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Batman in view of Ripa.
Regarding claim 19, Batman discloses the invention as claimed including receiving a problem statement specified by a user in a natural language through a conversational virtual agent linked with a software application including a workflow system; (see, Batman, paragraphs 71 and 26, “may input an inquiry (e.g., verbal or written) into the second clinical device 140… In various implementations, the workflow system 108 may work like a virtual assistant in answering questions of the second care provider 138 after the first care provider 102 has left the premises. The workflow system 108 may, when prompted by the second care provider 138, share various details about the condition of the patient 104 in a manner similar to a conversation.” “The workflow system 108 may be implemented in hardware, software, or a combination thereof. For example, the workflow system 108 may include software being executed on one or more servers located in the clinical environment or remote from the clinical environment.” [i.e., problem statement/ details about the condition of the patient, is conveyed to the virtual agent/virtual assistant which is linked to the workflow system. Also, (Inquiry/problem statement) is verbal or written language/natural language]);
inferring a workflow template that maps to the problem statement by at least one machine learning model (see, paragraph 142, “modifying the template by training a machine learning model based on the inquiry and the response” [i.e., (inferring a workflow template/modifying the template) based on a problem statement/inquiry via a machine learning model]);
interacting with the user in the natural language through the conversational virtual agent to request and receive data associated with the workflow template by the at least one machine learning model (see, paragraph 71, “The workflow system 108 may include a computing model configured to identify words, such as “chance,” “patient 104,” and “sepsis,” and may also identify the meaning of the inquiry. In response, the workflow system 108 may evaluate previously obtained patient data of the patient 104 relevant to the sepsis risk of the patient… In various implementations, the workflow system 108 may work like a virtual assistant in answering questions of the second care provider 138 after the first care provider 102 has left the premises. The workflow system 108 may, when prompted by the second care provider 138, share various details about the condition of the patient 104 in a manner similar to a conversation.” [i.e., When workflow system is prompted by a care provider with details in manner similar to conversation/natural language, the workflow system which includes a computing model can identify word and meaning of the inquiry]);
populating the workflow template with the data by the at least one machine learning model (see, paragraphs 71 and 73, “The workflow system 108 may include a computing model configured to identify words, such as “chance,” “patient 104,” and “sepsis,” and may also identify the meaning of the inquiry” “The computing model may be trained to take in training data indicating previous interactions between the second clinical device 140 and the second care provider 138, such as input signals received by the second clinical device 140 and output signals provided by the second clinical device 140… For example, the workflow system 108 may add the additional EMR data to the handoff report… According to some examples, the computing model may generate a template associated with the second care provider 138 that indicates relevant components of the handoff report for the second care provider 138. According to some examples, the template is stored in a database and can be accessed in advance of generating a new handoff report for the second care provider 138.” [i.e., Adding/populating data/value into the handoff report which is associated with the template (handoff report in the form of or using the template) generated by machine learning model/computing model with data also taken in by the model]).
Although Batman substantially teaches the claimed invention, Batman does not explicitly disclose triggering generation of a workflow by the workflow system with the workflow template.
In the same field, analogous art Ripa teaches triggering generation of a workflow by the workflow system with the workflow template. (see, paragraph 66, “Thus, when building automations, hyper-automation system 100 may provide user interfaces, development environments, API integration, pre-built and/or custom-built AI/Machine learning models, development templates, integrated development environments (IDEs), and advanced AI capabilities. Hyper-automation system 100 enables development, deployment, management, configuration, monitoring, debugging, and maintenance of RPA robots in some embodiments, which may provide automations for hyper-automation system 100.” [i.e., System/hyper-automation system as a template/development template wherein the system triggers workflow actions such as development, deployment, management, configuration, monitoring, debugging, and maintenance]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Ripa so that when building automations, hyper-automation system 100 may provide user interfaces, development environments, API integration, pre-built and/or custom-built AI/Machine learning models, development templates, integrated development environments (IDEs), and advanced AI capabilities. Hyper-automation system 100 enables development, deployment, management, configuration, monitoring, debugging, and maintenance of RPA robots in some embodiments, which may provide automations for hyper-automation system 100. (see Ripa, e.g., paragraph 66). Doing so would have allowed Batman to use Ripa‘s automations in regards to hyper-automation systems to implement access control and governance restrictions at the robot and/or robot design application level, for the purpose of added level of security and compliance which is put into the automation process development pipeline, as suggested by Ripa (see Ripa, paragraph 66).
Regarding claim 20, as discussed above, Batman in view of Ripa teaches the method of claim 19.
Batman further discloses determining one or more missing parameters of the workflow template; (see, e.g., paragraphs 5, 11, 110, and 124, “key patient information can be lost as the patients are transferred between care providers.”, “the workflow system is configured to monitor patient data … and to generate, based on the patient data, a handoff report for a second care provider taking over care of the patients. … the workflow system determines what data … is relevant to the second care provider. The workflow system may generate the handoff report based on … a personalized template associated with the second care provider”, “the first predictive model 504 includes one or more parameters that are optimized based on training data.”, “the second predictive model 706 includes one or more parameters that are optimized based on training data.” [i.e., key patient information/parameters can be lost between care providers and the transfer of patient and handoff report to a 2nd care provider is based on a template which includes parameters and data and during the transfer, a comparison is made of the data from a 1st to a 2nd set of parameters to determine the missing/lost parameters]);
translating the one or more missing parameters into one or more natural language requests for the data with the at least one machine learning model (see, paragraph 71, “the handoff report may act as a “personal digital assistant” to help bring the second care provider 138 up-to-speed on the patients… the second care provider 138 may input an inquiry (e.g., verbal or written) into the second clinical device 140… The workflow system 108 may include a computing model configured to identify words... In response, the workflow system 108 may evaluate previously obtained patient data of the patient 104 relevant to the sepsis risk of the patient.” [i.e., the second care provider may want additional patient data about the patient (missing data). The inquiry, either verbal or written (natural language) is put into the workflow system which contains a computing model configured to identify words and meanings of phrases]);
sending the one or more natural language requests to the user with the conversational virtual agent as part of interacting with the user (see, paragraph 71, “The workflow system 108 may include a computing model configured to identify words, such as “chance,” “patient 104,” and “sepsis,” and may also identify the meaning of the inquiry. In response, the workflow system 108 may evaluate previously obtained patient data of the patient 104 relevant to the sepsis risk of the patient … the workflow system 108 may work like a virtual assistant in answering questions of the second care provider 138 after the first care provider 102 has left the premises. The workflow system 108 may, when prompted by the second care provider 138, share various details about the condition of the patient 104 in a manner similar to a conversation.” [i.e., when workflow system is prompted by a care provider with details in manner similar to conversation/natural language, the workflow system which includes a computing model can identify word and meaning of the inquiry]);
updating the workflow template to include parameter values received in response to the one or more natural language requests. (see, paragraph 139, “from the template database, a template associated with the second care provider; identifying patient data including: message data that includes at least one text, audio, or video message … generating a response to the inquiry based on the patient data; transmitting, to the second computing device, the response; modifying the template by training a machine learning model based on the inquiry and the response; and based on modifying the template, storing the template in the template database.” [i.e., inquiry can be in natural language/conversation , and as audio or video message inquiry and be transmitted to a computing device with the response modifying/updating the template with information from inquiry]).
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Batman as applied to claims 1, 9, 11 and 17 above and further in view of Sharma, Aminish et al. (U.S. Publication No. 20220138644, hereinafter “Sharma”).
Regarding claim 10, as discussed above, Batman discloses the method of claim 9.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose wherein the software application is a financial management software application.
In the same field, analogous art Sharma teaches wherein the software application is a financial management software application (see, paragraph 22, “For example, the computing functionality may include…financial management, business management, … and/or various other functions that a computing device performs for a user...The user computing system (102) may be a mobile device (e.g., phone, tablet, digital assistant, laptop, etc.) or any other computing device (e.g., desktop, terminal, workstation, etc.) with a computer processor (not shown) and memory (not shown) capable of running computer software.” [i.e., The user computing system has incorporated a digital assistant/virtual agent with a computer processor having computer software capable of financial management tasks]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Sharma so that “the user may be a company employee that acts as a sender, a potential sender, or a requestor of services performed by a company (e.g., a client, a customer, etc.) of the user computing system. The user computing system (102) may be a mobile device (e.g., phone, tablet, digital assistant, laptop, etc.) or any other computing device (e.g., desktop, terminal, workstation, etc.) with a computer processor (not shown) and memory (not shown) capable of running computer software.” (see Sharma, e.g., paragraph 22). Doing so would have allowed Batman to use Sharma ‘s computer system which includes a computer program which is a software application stored in memory that when the instructions execute, it will enable a device to perform functions of one or more embodiment, as suggested by Sharma (see Sharma paragraph 22).
Regarding claim 18, as discussed above, Batman discloses the system of claim 17.
Although Batman substantially discloses the claimed invention, Batman does not explicitly disclose wherein the software application is a financial management software application.
In the same field, analogous art Sharma teaches wherein the software application is a financial management software application (see, paragraph 22, “For example, the computing functionality may include…financial management, business management, … and/or various other functions that a computing device performs for a user...The user computing system (102) may be a mobile device (e.g., phone, tablet, digital assistant, laptop, etc.) or any other computing device (e.g., desktop, terminal, workstation, etc.) with a computer processor (not shown) and memory (not shown) capable of running computer software.” [i.e., The user computing system has incorporated a digital assistant/virtual agent with a computer processor having computer software capable of financial management tasks]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batman to incorporate the teachings of Sharma so that “the user may be a company employee that acts as a sender, a potential sender, or a requestor of services performed by a company (e.g., a client, a customer, etc.) of the user computing system. The user computing system (102) may be a mobile device (e.g., phone, tablet, digital assistant, laptop, etc.) or any other computing device (e.g., desktop, terminal, workstation, etc.) with a computer processor (not shown) and memory (not shown) capable of running computer software.” (see Sharma, e.g., paragraph 22). Doing so would have allowed Batman to use Sharma ‘s computer system which includes a computer program which is a software application stored in memory that when the instructions execute, it will enable a device to perform functions of one or more embodiment, as suggested by Sharma (see Sharma paragraph 22).
Conclusion
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/STEVEN PENG/ Examiner, Art Unit 2125
/KAMRAN AFSHAR/ Supervisory Patent Examiner, Art Unit 2125
1 As indicated in the objection to this claim above, “matching” should read “machine”
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