DETAILED ACTION
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 .
Priority
Applicant claims the benefit of prior-filed foreign application No. IN-202011033521, has been acknowledge by the examiner.
Drawings
The drawings were received on 08/04/2021. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 03/31/2025, 11/21/2024, 06/10/2024, 01/26/2024, and 03/22/2022 been considered by the examiner.
Response to Arguments
Applicant's arguments filed 12/03/2025 have been fully considered but they are not persuasive.
Regarding applicant remarks the remarks are directed to subject matter not previously examined by the examiner. See the current office action below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 24-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claim 1: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… associate a data collector with a class . select the class based on a requested characteristic of a task request select the data collector associated with the class … and responsive to a query from (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
machine-readable instructions to cause at least one processor circuit to at least: associate a data collector with a class by executing a machine-learning model based on a first characteristic associated with a first user device, … the first characteristic from a digital personalized user agent installed on the first user device; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… a first characteristic associated with a first user device, the first user device associated with the data collector,… a task request from a distribution agent …a query from the digital personalized user agent: (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
and cause transmission of the content for output at the first user device via the digital personalized user agent. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
and cause transmission of the content for output at the first user device via the digital personalized user agent… and cause the selection to be sent via a network to the distribution agent to cause a work order to be transmitted to the first user device; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
Alternatively: the first characteristic from a digital personalized user agent installed on the first user device; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Storing and retrieving information in memory)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception; and that generally link the use of a judicial exception to a particular technological environment and/or directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above. The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea of claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the classification learning controller circuitry is to execute the machine-learning model based on a second characteristic, the second characteristic including at least one of a skill level of the data collector, a performance rating of the data collector, one or more interests of the data collector, a location of the data collector, or device information of the data collector. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea of claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the machine- learning model is at least one of a classification algorithm, a preferential learning algorithm, a relevance ranking and scoring algorithm, or a collaborative algorithm. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 4: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… an acceptance of the work order. (Considered directed to a Mental Process: Making evaluations and judgements of observations as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the classification learning controller circuitry is to update the machine-learning model based on an acceptance of the work order. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
Alternatively, on an acceptance of the work order (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application. Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 5: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… accept work order... (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 6: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea of claim 5.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the second machine-learning model is at least one of a natural language understanding algorithm, a preferential learning algorithm, or a relevance ranking and scoring algorithm. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 7: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea of claim 5.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
… to provide a second user input … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
wherein the digital personalized user agent to prompt the data collector to provide a second user input and the personal learning controller circuitry is to update the second machine-learning model based on the second user input. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above. The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 8: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea of claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the task request includes at least one of a request to capture a photograph, log data, write a description, or answer a questionnaire. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 9: Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… associate a data collector with a class by executing a machine-learning model based on a first characteristic associated with a first user device,… select the class based on a requested characteristic of a task request (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising machine-readable instructions to cause at least one processor circuit to at least: associate a data collector with a class by executing a machine-learning model based on a first characteristic associated with a first user device, the first user device associated with the data collector … the first characteristic from a digital personalized user agent installed on the first user device; and responsive to a query from the digital personalized user agent: select content based on the class with which the data collector is associated, (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… a first characteristic associated with a first user device, the first user device associated with the data collector,… a task request from a distribution agent …a query from the digital personalized user agent: (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
and cause the selection to be sent via a network to the distribution agent to cause a work order to be transmitted to the first user device; … and cause transmission of the content for output at the first user device via the digital personalized user agent. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
and cause transmission of the content for output at the first user device via the digital personalized user agent… and cause the selection to be sent via a network to the distribution agent to cause a work order to be transmitted to the first user device; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
Alternatively: the first characteristic from a digital personalized user agent installed on the first user device; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Storing and retrieving information in memory)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception; and that generally link the use of a judicial exception to a particular technological environment and/or directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above. The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Regarding claims 10-13, the claim limitations are similar to claim 2-4 and 8 respectively and thus rejected under the same rationale.
Claim 24 Dose claim fall within a statutory category? Yes:
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
select training content for the data collector based on the class of the data collector (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the classification learning controller circuitry is to select training content for the data collector based on the class of the data collector, the data interface circuitry to send the training content for output at the first user device. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
send the training content for output at the first user device (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above. The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Regarding claim 25, the limitations are similar with claim 24 limitations and rejected under the same rationale.
As shown above, claims 1-13 and 24-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more” than the recited judicial exception. The claims are therefore directed to an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13 and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Vishnoi et al. (US 20200342850, hereinafter ‘Vis’) in view of Route et al. (US 20210357682, hereinafter ‘Ro’).
Regarding independent claim 1, Vis teaches an apparatus, comprising : classification learning controller circuitry to associate a data collector with a class by executing a machine-learning model based on a first characteristic associated with a first user device, the first user device associated with the data collector, the first characteristic from a digital personalized user agent installed on the first user device (in 0094-0095: … Accordingly, the training data for the intent model 320 could include, for each intent associated with the skill bot system 300, a set of utterances representative of the intent [classification learning controller circuitry to associate a data collector with a class by executing a machine-learning model based on a first characteristic associated with a first user device, the class as the decision model associated with the training data for supporting predictions of the appropriate learning model used by the digital assitant]. Similarly, the training data for the skill bot model 224 could include, for each skill bot in the chatbot system, a set of utterances representative of the skill bot… In some embodiments, training data and/or trained parameters for all predictive models employed by a digital assistant [the first user device associated with the data collector, the first characteristic from a digital personalized user agent installed on the first user device as the collection of predictive models employed by a digital assistant] (e.g., system intent model 222, skill bot model 224, intent model 320) is maintained in a central location (e.g., data store 250). Alternatively, each skill bot can maintain its own training data and/or trained parameters in a separate data store [the first data collector characteristic corresponding to the data collector]…..; And in 0063 In certain embodiments, as part of this processing, the digital assistant determines if the user input utterance explicitly identifies a skill bot using its invocation name. If an invocation name is present in the user input, then it is treated as an explicit invocation of the skill bot corresponding to the invocation name. In such a scenario, the digital assistant may route the user input to the explicitly invoked skill bot for further handling [the first user device associated with the data collector as user data collected to model and provide digital assistance using a machine learning model]. If there is no specific or explicit invocation, in certain embodiments, the digital assistant evaluates the received user input utterance and computes confidence scores for the system intents and the skill bots associated with the digital assistant…)
selection generator circuitry to: select the class based on a requested characteristic of a task request from a distribution agent; and select the data collector associated with the class; and data interface circuitry to send, via a network, the selection to the distribution agent to cause a work order to be transmitted to the first user device (0062-0063: At the master bot or digital assistant level [a distribution agent], when a user inputs a phrase or utterance to the digital assistant [a task request from a distribution agent;], the digital assistant is configured to perform processing to determine how to route the utterance and the related conversation [selection generator circuitry to: select the class based on a requested characteristic of a task request from a distribution agent]. The digital assistant determines this using a routing model, which can be rules-based, AI-based, or a combination thereof. The digital assistant uses the routing model to determine whether the conversation corresponding to the user input utterance is to be routed to a particular skill for handling [to cause a work order for executing instruction for performing a task associated with a particular skill], is to be handled by the digital assistant or master bot itself per a built-in system intent [selection generator circuitry to: select the class based on a requested characteristic of a task request from a distribution agent], or is to be handled as a different state in a current conversation flow. In certain embodiments, as part of this processing, the digital assistant determines if the user input utterance explicitly identifies a skill bot using its invocation name. If an invocation name is present in the user input, then it is treated as an explicit invocation of the skill bot corresponding to the invocation name. In such a scenario, the digital assistant may route the user input to the explicitly invoked skill bot for further handling. If there is no specific or explicit invocation, in certain embodiments, the digital assistant evaluates the received user input utterance and computes confidence scores for the system intents and the skill bots associated with the digital assistant. The score computed for a skill bot or system intent represents how likely the user input is representative of a task that the skill bot is configured to perform or is representative of a system intent. Any system intent or skill bot with an associated computed confidence score exceeding a threshold value (e.g., a Confidence Threshold routing parameter) is selected as a candidate for further evaluation [and select the data collector associated with the class]. The digital assistant then selects [and data interface circuitry to send, via a network, the selection to the distribution agent to cause a work order to be transmitted to the first user device], from the identified candidates [and select the data collector associated with the class], a particular system intent or a skill bot for further handling of the user input utterance. In certain embodiments, after one or more skill bots are identified as candidates, the intents associated with those candidate skills are evaluated (using the trained model for each skill) and confidence scores are determined for each intent….. If a system intent is selected [and select the data collector associated with the class], then one or more actions are performed by the master bot [and data interface circuitry to send, via a network, the selection to the distribution agent to cause a work order to be transmitted to the first user device] itself according to the selected system intent.)
and responsive to a query from the digital personalized user agent, the query associated with execution of the work order using the first user device, the classification learning controller circuitry to select content responsive to the query based on the class with which the data collector is associated, and the data interface circuitry to send the content responsive to the query for output at the first user device via the digital personalized user agent. (in [0204] At 502, a determination is made that (i) an utterance received from a user while the user is interacting with a first chatbot of a chatbot system is an invalid input to the first chatbot or (ii) the first chatbot is attempting to route the utterance to a destination associated with the first chatbot [responsive to a query from the digital personalized user agent, the query associated with execution of the work order using the first user device, ]. As discussed earlier, there are various ways in which these two situations can be detected, such as monitoring the status of a flag variable indicating whether a skill bot has received an invalid input, or intercepting a call from a skill to a component that infers intents. If neither of these situations occurs, then the utterance can be handled according to the dialog flow definition configured for the first chatbot. However, as discussed earlier, there may be certain situations where an utterance is intercepted (e.g., by a master bot) before reaching the skill bot that the user is interacting with (e.g., invocation of the Exit system intent or an explicit invocation of another skill bot) [responsive to a query from the digital personalized user agent, the query associated with execution of the work order using the first user device, the classification learning controller circuitry to select content responsive to the query based on the class with which the data collector is associated]. If either (i) or (ii) is true, then processing proceeds to 504. [0205] At 504, a second chatbot is identified, in response to the determination in 502, for generating a response to the utterance received while the user is interacting with the first chatbot [the query associated with execution of the work order using the first user device]. The second chatbot can be identified based on computing one or more types of confidence scores. For example, the processing in 504 can be implemented using the same steps discussed above in connection with blocks 408 to 424 in FIG. 4. Thus, the second chatbot could be identified by computing, using a predictive model […, the classification learning controller circuitry to select content responsive to the query based on the class with which the data collector is associated] , separate confidence scores for the first chatbot and the second chatbot and determining that the second chatbot is a match to the utterance based on a confidence score computed for the second chatbot satisfying one or more confidence score thresholds. However, as indicated above, the processing in 408 to 424 does not always lead to a response being generated by a skill bot. For example, in some instances, the response to an utterance is generated based on a system intent. Further, it may be possible that the skill bot identified for generating the response is the same bot that the user is currently interacting with, i.e., the first chatbot is the same as the second chatbot. [0206] At 506, the utterance received while the user is interacting with the first chatbot is routed to the second chatbot. If the first chatbot and the second chatbot are different, the digital assistant may output a message indicating that a switch to the second chatbot is occurring, or the digital assistant may prompt the user to confirm that the user wants to proceed with the switch [the data interface circuitry to send the content responsive to the query for output at the first user device via the digital personalized user agent]. As discussed earlier, such a message or prompt can be based on the Interrupt Prompt Confidence Threshold. Accordingly, there can be additional interaction between the user and the digital assistant before the second chatbot is permitted to generate a response to the utterance [and the data interface circuitry to send the content responsive to the query for output at the first user device via the digital personalized user agent].
Examiner notes that Vis teaches claimed circuitry as hardware processor executing computing instructions as claimed, in 0018 For purposes of this disclosure, a “digital assistant” is an entity that helps users of the digital assistant accomplish various tasks through natural language conversations. A digital assistant can be implemented using software only (e.g., the digital assistant is a digital entity implemented using programs, code, or instructions executable by one or more processors), using hardware, or using a combination of hardware and software…; And in 0033 While the embodiment in FIG. 1 shows digital assistant 106 comprising a master bot 114 and skill bots 116-1, 116-2, and 116-3, this is not intended to be limiting. A digital assistant can include various other components (e.g., other systems and subsystems) that provide the functionalities of the digital assistant. These systems and subsystems may be implemented only in software (e.g., code, instructions stored on a computer-readable medium and executable by one or more processors), in hardware only, or in implementations that use a combination of software and hardware.
Furthermore, Vis teaches the use of machine learning models to classify user input intention class, for processing information using an appropriate predictive models of a digital assistance system where the claimed work order is any input prompt/instructions for the machine learning system to process as noted above. And as in line with the appropriated broadest reasonable interpretation in light of applicant specification paragraph in [0003] Manufacturers of Consumer-Packaged Goods (CPG) often hire data collectors to study display characteristics and/or prices of their products in retail stores in a particular geographic location. In some cases, the data collectors are hired auditors, store employees, or independent that accept or reject work orders sent through manual processes by the CPG manufacturers or a consumer research entity. The work orders may involve instructions or tasks to research pricing, interview customers and employees, and/or collect images. (emphasis added)
Thus, claimed work order includes instructions as prompts as disclosed by the Vis reference noted above.
Additionally, Ro expressly teaches processing prompts/query by the artificial machine learning agent to collect images as a work order request, in [0038] The artificial intelligence (AI) chatbot 54 can apply a method for understanding sentiment plus content of the intended use to create the pattern for the search criteria. For example, the artificial intelligence (AI) chatbot 54 may perform image selection based on themes, events, categories, and sentiment [the selection to the distribution agent to cause a work order to be transmitted to the first user device]. In one example, the artificial intelligence (AI) chatbot 54 of the artificial intelligence (AI) driven intent based system 100 for retrieving images may be directed to a milestone event, such as a milestone birthday. In this example, the user 20 may enter the milestone event into the chatbot in response to a data entry field. The artificial intelligence (AI) chatbot 54 for the artificial intelligence (AI) driven intent based system 100 for retrieving images may retrieve images in response to an image type [the selection to the distribution agent to cause a work order to be transmitted to the first user device]. For example, the user may request for images showing people that are happy. The artificial intelligence (AI) bot 54 for the artificial intelligence (AI) driven intent based system 100 for retrieving images may retrieve images in response to a content for the images. For example, the user may request for images including groups of people, friends, family photos with a person of prominence and combinations thereof….
Ro and Vis are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for implementing and developing a computer implemented method for retrieving electronic images, as disclosed by Ro with the method of developing information retrieval and data processing techniques of digital assistant platforms using machine learning methods and models, as disclosed by Vis.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Ro and Vis as disclosed above. Doing so allows for providing an artificial intelligence (AI) driver image retrieval solution that applies a method that first understands the sentiment of the intended use of the image and then uses a combination of machine learning, structured data retrieval, folder search techniques and image identification to bring the specific context to aid a real-time classification and retrieval of images, (Ro, 0015).
Regarding claim 2, the rejection of claim 1 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 1, wherein the classification learning controller circuitry is to execute the machine-learning model based on a second characteristic, the second characteristic including at least one of a skill level of the data collector, a performance rating of the data collector, one or more interests of the data collector, a location of the data collector, or device information of the data collector. (in 0094-0095: … Accordingly, the training data for the intent model 320 could include, for each intent associated with the skill bot system 300, a set of utterances representative of the intent [wherein the classification learning controller circuitry is to execute the machine-learning model based on a second characteristic, the second characteristic including at least one of a …]. Similarly, the training data for the skill bot model 224 could include, for each skill bot in the chatbot system, a set of utterances representative of the skill bot… In some embodiments, training data and/or trained parameters for all predictive models employed by a digital assistant (e.g., system intent model 222, skill bot model 224, intent model 320) is maintained in a central location (e.g., data store 250). Alternatively, each skill bot can maintain its own training data and/or trained parameters in a separate data store…..; And in 0063 In certain embodiments, as part of this processing, the digital assistant determines if the user input utterance explicitly identifies a skill bot using its invocation name [one of a skill level of the data collector]. If an invocation name is present in the user input, then it is treated as an explicit invocation of the skill bot corresponding to the invocation name. In such a scenario, the digital assistant may route the user input to the explicitly invoked skill bot for further handling. If there is no specific or explicit invocation, in certain embodiments, the digital assistant evaluates the received user input utterance and computes confidence scores for the system intents and the skill bots associated with the digital assistant… )
Regarding claim 3, the rejection of claim 1 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 1, wherein the machine- learning model is at least one of a classification algorithm, a preferential learning algorithm, a relevance ranking and scoring algorithm, or a collaborative algorithm. (0077: …Each of the models 222, 224 can be implemented as a rules-based and/or AI-based model. For instance, the models 222, 224 can be implemented as neural networks [wherein the machine- learning model is at least one of a classification algorithm, a preferential learning algorithm,] trained to infer which system intent or skill bot is most suited for responding to the utterance 202…; And in 0046: The intents and their associated example utterances are used as training data to train the skill bot. Various different training techniques may be used. As a result of this training, a predictive model is generated that is configured to take an utterance as input and output an intent inferred for the utterance. In some instances, input utterances are provided to an intent analysis engine (e.g., a rules-based or machine-learning based classifier[wherein the machine- learning model is at least one of a classification algorithm, a preferential learning algorithm,..] executed by the skill bot), which is configured to use the trained model to predict or infer an intent for the input utterance….; And in 0049: …. In certain embodiments, the skill bot uses a predictive model that is trained using the training data and allows the skill bot to discern what users say (or in some cases, are trying to say). DABP 102 provides various different training techniques that can be used by a skill bot designer to train a skill bot, including various machine-learning based training techniques, rules-based training techniques, and/or combinations thereof...)
Regarding claim 4, the rejection of claim 1 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 1, wherein the classification learning controller circuitry is to update the machine-learning model based on an acceptance of the work order. (0079 The input to the models 222, 224 can include the utterance 202 itself and/or information derived from the utterance 202, e.g., the extracted information 205. For instance, when implemented as neural networks, each of the models 222, 224 can receive an encoded version of the utterance 202 for processing (e.g., a set of word embeddings containing a separate embedding for each word in the utterance 202, where each embedding is a multi-dimensional feature vector containing values for features of a corresponding word). Each model 222, 224 may be configured to infer a class or category based on the input to the model, where the class/category represents a particular system intent or skill bot [wherein the classification learning controller circuitry is to update the machine-learning model based on an acceptance of the work order]. Training data can include example utterances and, for each example utterance, a label (ground truth) indicating which system intent or skill bot should be used to handle the example utterance […based on an acceptance of the work order]. The models 222, 224 can be trained by comparing the outputs generated based on the example utterances to their corresponding labels, and then adjusting parameters of the models 222, 224 [wherein the classification learning controller circuitry is to update the machine-learning model based on an acceptance of the work order] (e.g., a weight and/or bias value employed in an activation function of a node in a neural network) when there is a difference between the inference and the label. For instance, the training may involve backpropagation to minimize a loss function.)
Ro teaches processing prompts/query by the artificial machine learning agent to collect images as a work order request, in [0029] The registration database 51 includes user information that is used by the system for image retrieval… The user history database 52 may include sentiment information on previously delivered images to the user 20 from the system 100 in response to a user request. The user history database 52 may continually be updated with information directed to the user's interaction with the artificial intelligence (AI) driven intent based system 100 for retrieving images [wherein the classification learning controller circuitry is to update the machine-learning model based on an acceptance of the work order]... [0033] Referring to FIG. 2, the system 100 may include an inquiry context designator 53 for receiving the inputs from the user 10 and using those inputs to determine the context, e.g., intent, for the image retrieval…. In some embodiments, a chatbot 54 will include use existing conversation data (if available) to understand the type of questions people ask. The chatbot 54 can analyze correct answers to those questions through a ‘training’ period [wherein the classification learning controller circuitry is to update the machine-learning model based on an acceptance of the work order]. The artificial intelligence (AI) driven chatbot 54 can use machine learning and natural language processing (NLP) to learn context. And in[0038] The artificial intelligence (AI) chatbot 54 can apply a method for understanding sentiment plus content of the intended use to create the pattern for the search criteria. For example, the artificial intelligence (AI) chatbot 54 may perform image selection based on themes, events, categories, and sentiment [… based on an acceptance of the work order]. In one example, the artificial intelligence (AI) chatbot 54 of the artificial intelligence (AI) driven intent based system 100 for retrieving images may be directed to a milestone event, such as a milestone birthday. In this example, the user 20 may enter the milestone event into the chatbot in response to a data entry field. The artificial intelligence (AI) chatbot 54 for the artificial intelligence (AI) driven intent based system 100 for retrieving images may retrieve images in response to an image type […based on an acceptance of the work order]. For example, the user may request for images showing people that are happy. The artificial intelligence (AI) bot 54 for the artificial intelligence (AI) driven intent based system 100 for retrieving images may retrieve images in response to a content for the images. For example, the user may request for images including groups of people, friends, family photos with a person of prominence and combinations thereof….
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Ro and Vis for the same reasons disclosed above.
Regarding claim 5, the rejection of claim 1 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 1, wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model. ((0079 The input to the models 222, 224 can include the utterance 202 itself and/or information derived from the utterance 202, e.g., the extracted information 205. For instance, when implemented as neural networks, each of the models 222, 224 can receive an encoded version of the utterance 202 for processing (e.g., a set of word embeddings containing a separate embedding for each word in the utterance 202, where each embedding is a multi-dimensional feature vector containing values for features of a corresponding word) [wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model]. Each model 222, 224 may be configured to infer a class or category based on the input to the model, where the class/category represents a particular system intent or skill bot [wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model]. Training data can include example utterances and, for each example utterance, a label (ground truth) indicating which system intent or skill bot should be used to handle the example utterance [wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model]. The models 222, 224 can be trained by comparing the outputs generated based on the example utterances to their corresponding labels, and then adjusting parameters of the models 222, 224 (e.g., a weight and/or bias value employed in an activation function of a node in a neural network) when there is a difference between the inference and the label. For instance, the training may involve backpropagation to minimize a loss function.; And in[0206] At 506, the utterance received while the user is interacting with the first chatbot is routed to the second chatbot. If the first chatbot and the second chatbot are different, the digital assistant may output a message indicating that a switch to the second chatbot is occurring, or the digital assistant may prompt the user to confirm that the user wants to proceed with the switch [t wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model]. As discussed earlier, such a message or prompt can be based on the Interrupt Prompt Confidence Threshold. Accordingly, there can be additional interaction between the user and the digital assistant before the second chatbot is permitted to generate a response to the utterance [wherein the digital personalized user agent includes personal learning controller circuitry to accept work order by executing a second machine- learning model] )
Ro teaches the workorder for processing image collection intents/tasks as noted above in claim 1 rejection.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Ro and Vis for the same reasons disclosed above.
Regarding claim 6, the rejection of claim 5 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 5, wherein the second machine-learning model is at least one of a natural language understanding algorithm, a preferential learning algorithm, or a relevance ranking and scoring algorithm. (0079 The input to the models 222, 224 can include the utterance 202 itself and/or information derived from the utterance 202, e.g., the extracted information 205. For instance, when implemented as neural networks, each of the models 222, 224 can receive an encoded version of the utterance 202 for processing (e.g., a set of word embeddings containing a separate embedding for each word in the utterance 202, where each embedding is a multi-dimensional feature vector containing values for features of a corresponding word) [wherein the second machine-learning model is at least one of a natural language understanding algorithm,…]. Each model 222, 224 may be configured to infer a class or category based on the input to the model, where the class/category represents a particular system intent or skill bot. Training data can include example utterances and, for each example utterance, a label (ground truth) indicating which system intent or skill bot should be used to handle the example utterance [wherein the second machine-learning model is at least one of a natural language understanding algorithm, …]…)
Regarding claim 7, the rejection of claim 5 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 5, wherein the digital personalized user agent to prompt the data collector to provide a second user input and the personal learning controller circuitry is to update the second machine-learning model based on the second user input. (in 0065-0066: As shown in FIG. 2, the MB system 200 includes a language processing subsystem 210 and a routing subsystem 220. The language processing subsystem 210 is configured to process an utterance 202 provided by a user. As discussed in the Chatbot System Overview section above, such processing can involve NLU processing performed to understand the meaning of an utterance… Routing subsystem 220 is configured to determine which bot (e.g., one of a set of available skill bots 216-1 to 216-3 or the MB system 200 itself) should handle the utterance 202. For example, as discussed earlier, a master bot can be configured with one or more system intents (Exit, Help, UnresolvedIntent, etc.)…; And in 0088: Examples of different types of confidence thresholds that can be applied by the routing subsystem 220 include: Confidence Threshold—Applicable to system intents, skills, and bot intents. This is the minimum score required for a system intent/skill/bot intent to be deemed a candidate (i.e., a potential match) for an input utterance, e.g., 0.4 or 40%. … Therefore, when the Confidence Win Margin is not satisfied, multiple candidates could be deemed to be matching and this may trigger further evaluation by the digital assistant and/or disambiguation processing (e.g., prompting the user for additional input confirming which candidate the user wishes to invoke) [wherein the digital personalized user agent to prompt the data collector to provide a second user input and the personal learning controller circuitry is to update the second machine-learning model based on the second user input]…; And in 0047-0050] (3) Configuring entities for one or more intents of the skill bot—In some instances, additional context may be needed to enable the skill bot to properly respond to a user utterance [wherein the digital personalized user agent to prompt the data collector to provide a second user input and the personal learning controller circuitry is to update the second machine-learning model based on the second user input]. For example, there may be situations where a user input utterance resolves to the same intent in a skill bot. For instance, in the above example, utterances “What's my savings account balance?” and “How much is in my checking account?” both resolve to the same CheckBalance intent, but these utterances are different requests asking for different things... Creating a dialog flow [wherein the digital personalized user agent to prompt the data collector to provide a second user input and the personal learning controller circuitry is to update the second machine-learning model based on the second user input] for the skill bot—A dialog flow specified for a skill bot describes how the skill bot reacts as different intents for the skill bot are resolved responsive to received user input. The dialog flow defines operations or actions that a skill bot will take, e.g., how the skill bot responds to user utterances, how the skill bot prompts users for input [wherein the digital personalized user agent to prompt the data collector to provide a second user input and the personal learning controller circuitry is to update the second machine-learning model based on the second user input], and how the skill bot returns data. A dialog flow is like a flowchart that is followed by the skill bot. The skill bot designer specifies a dialog flow using a language, such as markdown language. In certain embodiments, a version of YAML called OBotML may be used to specify a dialog flow for a skill bot. The dialog flow definition for a skill bot acts as a model for the conversation itself, one that lets the skill bot designer choreograph the interactions between a skill bot and the users that the skill bot services.)
Regarding claim 8, the rejection of claim 1 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 1, wherein the task request includes at least one of a request to capture a photograph, log data, write a description, or answer a questionnaire. (in 0066: … Similarly, the Help intent may have a dialog flow associated with it that guides the user through a series of questions [wherein the task request includes at least one of a request to capture a ... answer a questionnaire] and answers designed to provide the user with help about using the digital assistant or interacting with skill bots in general.)
Regarding claim 9, the limitations are similar with claim 1 limitations and rejected under the same rationale.
Regarding claims 10-13, the claim limitations are similar to claim 2-4 and 8 respectively and thus rejected under the same rationale.
Regarding claim 24, the rejection of claim 1 is incorporated and Vis in combination with Ro further teaches the apparatus of claim 1, wherein the classification learning controller circuitry is to select training content for the data collector based on the class of the data collector, the data interface circuitry to send the training content for output at the first user device. (in [0204] At 502, a determination is made that (i) an utterance received from a user while the user is interacting with a first chatbot of a chatbot system is an invalid input to the first chatbot or (ii) the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. As discussed earlier, there are various ways in which these two situations can be detected, such as monitoring the status of a flag variable indicating whether a skill bot has received an invalid input, or intercepting a call from a skill to a component that infers intents. If neither of these situations occurs, then the utterance can be handled according to the dialog flow definition configured for the first chatbot. However, as discussed earlier, there may be certain situations where an utterance is intercepted (e.g., by a master bot) before reaching the skill bot that the user is interacting with (e.g., invocation of the Exit system intent or an explicit invocation of another skill bot) [wherein the classification learning controller circuitry is to select training content for the data collector based on the class of the data collector,…]. If either (i) or (ii) is true, then processing proceeds to 504. [0205] At 504, a second chatbot is identified, in response to the determination in 502, for generating a response to the utterance received while the user is interacting with the first chatbot. The second chatbot can be identified based on computing one or more types of confidence scores [wherein the classification learning controller circuitry is to select training content for the data collector based on the class of the data collector]. For example, the processing in 504 can be implemented using the same steps discussed above in connection with blocks 408 to 424 in FIG. 4. Thus, the second chatbot could be identified by computing, using a predictive model [wherein the classification learning controller circuitry is to select training content for the data collector based on the class of the data collector …] , separate confidence scores for the first chatbot and the second chatbot and determining that the second chatbot is a match to the utterance based on a confidence score computed for the second chatbot satisfying one or more confidence score thresholds. However, as indicated above, the processing in 408 to 424 does not always lead to a response being generated by a skill bot. For example, in some instances, the response to an utterance is generated based on a system intent. Further, it may be possible that the skill bot identified for generating the response is the same bot that the user is currently interacting with, i.e., the first chatbot is the same as the second chatbot. [0206] At 506, the utterance received while the user is interacting with the first chatbot is routed to the second chatbot. If the first chatbot and the second chatbot are different, the digital assistant may output a message indicating that a switch to the second chatbot is occurring, or the digital assistant may prompt the user to confirm that the user wants to proceed with the switch [the data interface circuitry to send the training content for output at the first user device]. As discussed earlier, such a message or prompt can be based on the Interrupt Prompt Confidence Threshold. Accordingly, there can be additional interaction between the user and the digital assistant before the second chatbot is permitted to generate a response to the utterance [the data interface circuitry to send the training content for output at the first user device].
Regarding claim 25, the limitations are similar with claim 23 limitations and rejected under the same rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chaturvedi et al. (US 20200327425): teaches the use of prediction/classification unit to provide the prognosis information for a monitored asset to the work order management unit and the work order management unit that produces a work order to have a technician or other human user check the status of the monitored asset.
Pan et al. (US 20210083994): teaches determining whether an input utterance is unrelated to a set of skill bots associated with a master bot. In some embodiments, a system described herein includes a training system and a master bot. The training system trains a classifier of the master bot.
Lui et al. (US 12019685): teaches maintaining contextual information from a first user request associated with a first task by a context engine, wherein the first task is associated with a first agent, receiving a second user request associated with a second task from a client system, wherein the second user request comprises an ambiguous mention and the second task is associated with a second agent, determining a context carryover is required for the second agent to execute the second task, determining the ambiguous mention corresponds to one or more data items associated with the contextual information from the first user request, and providing the one or more data items to the second agent for execution of the second task.
Gangadharaiah et al. (US 11797769): teaches determining that a particular sequence of natural language input has been generated by a first entity participating in a multi-interaction dialog, a first representation of accumulated dialog state associated with the sequence is obtained from a machine learning model at an artificial intelligence service.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUWATOSIN ALABI whose telephone number is (571)272-0516. The examiner can normally be reached Monday-Friday, 8:00am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OLUWATOSIN ALABI/ Primary Examiner, Art Unit 2129
Prosecution Insights