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 .
Drawings
The drawings were received on 07/26/2022. These drawings are acceptable.
Response to Arguments
Applicant's arguments filed 10/21/2025 have been fully considered but they are not persuasive.
Regarding the rejection of claims under 25 USC 112(b) , the applicant argues that the term aggregation refers to the grouping things together and the examiners interpretation does not take into consideration the plan meaning of aggregate.
Examiner disagrees. The phrase aggregate is defined plaining as a combination of units, processes or quantifiable elements. The problem here is that it is unclear how one would group reactions as claimed “receiving an aggregated reaction from the virtual conference provider; and outputting one or more graphical representations of the aggregated reaction …wherein the aggregated reaction comprises a clapping reaction, a cheering reaction, or a laughing reaction” The examiner highlights even the applicant’s own example in page 6 of remarks noted below highlights the subjective nature of this term “aggregated reactions”:
Applicant respectfully asserts that the term "aggregated reactions" is clear on its face according to the plain meaning of the two terms. To "aggregate" things is to group them together. For example, if multiple people all individually provide a particular reaction, e.g., a clap emoji, these individual reactions can be aggregated into a single clap emoji, rather than providing multiple individual reactions. The proposed construction of any input that is used by system to analyze or determine an reaction or sentiment associated with the observed input" does not take into consideration the plain meaning of "aggregate," and thus is incorrect. Office Action, p. 2; M.P.E.P. § 2111.01 ("Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification."). In view of this, Applicant respectfully asserts that the proper interpretation of the claim term should be its plain meaning and that the claim term is clear on its face. (emphasis added)
It is unclear how the plain definition of aggregate makes it clear how to identify and determine the claimed “aggregated reactions”. In the applicant’s example when multiple people provide the same reaction the alleged aggregate reaction is single instance of the same reaction. Where/what is combined or grouped in that example, highlighted in the example above? It only helps to further highlight the confusion in using a term that is typically used to group or combine quantifiable or listed units or processes to subjective elements that are not easily quantifiable or separated.
This appears to an argument, directed to a preferred embodiment or interpretation that is not highlighted or expressly noted in the claim limitation. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993) and MPEP 2111. The rejection made in the previous office action has been maintained. If the applicant intends that the phrase “aggregated reaction” refer to a selection among a plurality of the same reaction from different people, then that needs to be clarified in the claim limitation. Otherwise, any interpretation made of record can be alleged inappropriate based on some subjective example provided by the applicant. Applicant should considered clarifying the claim limitation so that one of ordinary skill in the art is able to clearly ascertain the intended scope of the claimed invention.
Regarding the rejection of claims under 35 USC 101: Abstract idea , the applicant agues that the claims are directed to patentable subject matter because the recited limitations reduce network bandwidth consumed by the participants in a virtual conference by interpreting audio signals, received by the computing network, by analyzed them to determine reactions as the only signals that are also transmitted using the computing network.
Examiner disagrees. The MPEP 2106.04(d)(1) discloses the evaluation of claimed improvements in the functioning of a computer or improvement to a technical field in step 2A prong two. The MPEP section discloses “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification…” (emphasis added)
Examiner notes that the claims do not appear to reflect the disclosed improvement as the claimed conference client application appears to receive the audio signals and processes them to share with other in the virtual conference as transmitted reactions. How then is the alleged reduction in the network bandwidth consumed by the participants in a virtual conference, those not appear to be reflected in the applicant’s claimed invention, thus the rejection made in the previous office action has been maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, claim 1 recites the limitation “not transmitting the audio signals to the virtual conference provider” appears to a negative limitation that appear to have basis in the original disclosure. The claim excludes audio signals from being transmitted to the virtual conference provider that is hosting a webinar. It would seem like the host or presenters audio signal would be transmitted to capture the reactions modeled by the model. MPEP 2173.05(i) requires the following:
Any negative limitation or exclusionary proviso must have basis in the original disclosure. If alternative elements are positively recited in the specification, they may be explicitly excluded in the claims. See In re Johnson, 558 F.2d 1008, 1019, 194 USPQ 187, 196 (CCPA 1977) ("[the] specification, having described the whole, necessarily described the part remaining."). See also Ex parte Grasselli, 231 USPQ 393 (Bd. App. 1983), aff’d mem., 738 F.2d 453 (Fed. Cir. 1984). In describing alternative features, the applicant need not articulate advantages or disadvantages of each feature in order to later exclude the alternative features. See Inphi Corporation v. Netlist, Inc., 805 F.3d 1350, 1356-57, 116 USPQ2d 2006, 2010-11 (Fed. Cir. 2015)… Any claim containing a negative limitation which does not have basis in the original disclosure should be rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. See MPEP § 2163 - § 2163.07(b) for a discussion of the written description requirement of 35 U.S.C. 112(a) and pre-AIA 35 U.S.C. 112, first paragraph. (emphasis added)
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 8 and 9 the limitation “aggregated reaction” is not a term of art, and the limitations do not clarify how one of ordinary skill in the art would ascertain the intended scope of the limitation. The examiner interprets this term as any input that is used by system to analyze or determine an reaction or sentiment associated with the observed input.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claim 1: Dose claim fall within a statutory category? Yes: A method.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… determining, … a plurality of candidate reactions associated with the audio signals, … selecting a reaction from the plurality of candidate reactions;... (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).
receiving, by a machine learning (“ML”) model of a conference client application, audio signals received from a microphone of a client device … and transmitting the reaction to the virtual conference provider. (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)
receiving, by a machine learning (“ML”) model of a conference client application …, the client device connected to a virtual meeting via the conference client application, … determining, by the ML 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; 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).)
… the virtual meeting being a webinar hosted by a virtual conference provider; … a plurality of candidate reactions associated with the audio signals, … the ML comprising a plurality of convolutional neural network (“CNN”) layers and at least one fully connected layer; … and not transmitting the audio signals to the virtual conference provider. (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; 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));
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: A method.
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 ML model further comprises a gated recurrent unit between the plurality of CNN layers and the at least one fully connected layer. (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: A method.
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 ML model further comprises a skip connection between an input node of the ML model and a first fully connected layer of the at least one fully connected layers. (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: A method.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
further comprising selecting the reaction having a greatest probability of the plurality of candidate reactions. (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).
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.
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: A method.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
further comprising selecting the reaction exceeding a first threshold and having a greatest probability of the plurality of candidate reactions. (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).
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 6: Dose claim fall within a statutory category? Yes: A method.
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 ML model further comprises a plurality of fully connected layers. (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 7: Dose claim fall within a statutory category? Yes: A method.
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 plurality of candidate reactions comprises a clapping reaction, a cheering reaction, or a laughing reaction. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation simply link the judicial exception to a field of use and/or technology environment, see MPEP 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.
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: A method.
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).
receiving an aggregated reaction from the virtual conference provider; (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 outputting one or more graphical representations of the aggregated reaction. (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).)
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.
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 9 Dose claim fall within a statutory category? Yes: A method.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea noted in claim 8.
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 aggregated reaction comprises a clapping reaction, a cheering reaction, or a laughing reaction. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation simply link the judicial exception to a field of use and/or technology environment, see MPEP 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 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 10: Dose claim fall within a statutory category? Yes: A system.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Claim limitations are similar to claim 1; and thus rejected under the same rationale.
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).
a non-transitory computer-readable medium; a communications interface; and one or more processors communicatively coupled to the non-transitory computer-readable medium and the communications interface, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to:... (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).
recites similar limitations to claim 1 and are thus rejected under the same rationale.
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));
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 11-15, the claim limitations are similar to claim 2-6 respectively and thus rejected under the same rationale.
Regarding claims 16-20, the claim limitations are similar to claim 1-5 respectively and thus rejected under the same rationale.
As shown above, claims 1-20 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, 6, 8, 10, and 15-16, are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20220038580, hereinafter ‘Li’) in view of Sainath et l. (US 10229700, hereinafter ‘Sai’) and in further view of Biswas et al. (US 20230134899, hereinafter ‘Bis’).
Regarding independent claim 1, Li teaches a method comprising: receiving, by a machine learning ("ML") model of a conference client application, audio signals received from a … of a client device, (0017] As different types of audio signals may be received from different users during a presentation, the present disclosure intelligently applies trained AI processing that can contextually adapt to analyze acoustic features of multiple audio inputs [receiving, by a machine learning ("ML") model of a conference client application] and derive accurate results for determining feedback therefrom. For instance, trained AI processing may apply different audio analysis models that are trained and tuned to analyze audio streams in different contextual scenarios (e.g., from different users/user groups and/or different locations) and even when users may mute their audio [audio signals received from a microphone of a client device, microphone muted by the user on client device] but still consent to audio signal monitoring. As a non-limiting example, different audio analysis models may comprise models tuned to analyze audio of a presenter, onsite audience, and remote audience…)
the client device connected to a virtual meeting via the conference client application, the virtual meeting being a webinar hosted by a virtual conference provider; (0001 Electronic meetings [the client device connected to a virtual meeting via the conference client application, the virtual meeting hosted by a virtual conference provider] have become commonplace often replacing the need for users to conduct in-person meetings. While in-person meetings still commonly occur, there are a greater number of users that may join a meeting virtually [virtual meeting being a webinar] either due to preference or requirement…; And in 0019: Moreover, non-limiting examples of the present disclosure further extend to improvements in a GUI of an application/service [the client device connected to a virtual meeting via the conference client application, the virtual meeting being a webinar hosted by a virtual conference provider] (e.g., presentation broadcast service) that may be adapted to provide application command control for management of a live presentation. Further, an adapted GUI may also be adapted to automatically provide reaction indication(s) based on a result of analyzing one or more audio streams without requiring a user to take manual action to provide feedback.)
determining, by the ML model, a plurality of candidate reactions associated with the audio signals, (in 0020: In one non-limiting example, an audio stream associated with a live presentation is detected and analyzed. For instance, the audio stream may be associated with a user (e.g., audience member) that is accessing a live electronic presentation through a presentation broadcasting service. One or more trained AI models may be selected and applied to analyze the audio stream based on identification of a classification of a user (e.g., audience member) or group of users from which the audio stream is received... Exemplary trained AI models [determining, by the ML model] are configured to automatically analyze acoustic features of the audio stream [associated with the audio signals] using first trained data trained to indicate target classes that each identify specific user reactions [determining, by the ML model, a plurality of candidate reactions associated with the audio signals] to the live electronic presentation and second trained data trained to indicate non-target classes that each identify audio types that are associated with the locational classification of the user.)
the ML comprising a plurality of convolutional neural network ("CNN") layers and at least one fully connected layer; (in 0042 ... Exemplary AI processing may be applicable to aid any type of determinative or predictive processing by the acoustic analysis component 106, via any of: supervised learning; unsupervised learning; semi-supervised learning; or reinforcement learning, among other examples. Non-limiting examples of supervised learning that may be applied comprise but are not limited to: nearest neighbor processing; naive bayes classification processing; decision trees; linear regression; support vector machines (SVM) neural networks (e.g., convolutional neural network (CNN) [the ML comprising a plurality of convolutional neural network ("CNN") layers and at least one fully connected layer] or recurrent neural network (RNN)); and transformers, among other examples)
selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider: and not transmitting the audio signals to the virtual conference provider. (in 0045 FIG. 1B illustrates an exemplary process flow 120 providing non-limiting examples of processing executed by exemplary trained AI processing that is configured to aid automatic generate of reaction indications [selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider] to live presentations, with which aspects of the present disclosure may be practiced… However, it is to be understood that the present disclosure applies to training/tuning audio analysis models for any type of user that may attend a live presentation so as to improve the accuracy and efficiency in determining user intent (e.g., user reactions/feedback) [selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider]. Processing described herein may be configured to analyze multiple different types of audio input (e.g., audio streams) individually and concurrently, providing the ability to cross-reference different types of audio signal data to improve predictive accuracy when classifying audio streams and generating exemplary reaction indications [selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider: and not transmitting the audio signals to the virtual conference provider]… Examiner notes that the generated reaction indications includes non-verbal emojis, in [0090] Continuing the above example, as the first selectable GUI feature 308 is activated, reaction indication(s) 312 may be automatically displayed for the presenter and/or users connected to the live presentation without requiring audience members take action to provide feedback… As a non-limiting example, reaction indications may be presented in a form that is easy to visualize and understand such as emojis or icons [selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider: and not transmitting the audio signals to the virtual conference provider]…)
While Li teaches artificial intelligence system for processing audio steams of users or participants using electronic meeting applications Li does not expressly teach the limitations: … received from a microphone of a client device, … ML comprising a … and at least one fully connected layer…
Sai expressly teaches:… received from a microphone of a client device, … (in 5:37-41: The neural network receives a raw audio waveform (202). For example, the neural network may be included on a user device and may receive the raw audio waveform from a microphone [audio signals received from a microphone of a client device]. The neural network may be part of a voice activity detection system. )
ML comprising a … and at least one fully connected layer… (in 5:14-18: In some implementations, the neural network 100, e.g., the CLDNN neural network [ML comprising a plurality of convolutional neural network ("CNN") layers and at least one fully connected layer], may be trained using the asynchronous stochastic gradient descent (ASGD) optimization strategy with the cross-entropy criterion. The neural network 100 may initialize the CNN layers 102 [a plurality of convolutional neural network ("CNN") layers]…; And in 4:33-37: The neural network 100 provides the third representation to one or more DNN layers 108. The DNN layers may be feed-forward fully connected layers [and at least one fully connected layer] with k hidden layers and n hidden units per layer…)
Li and Sai are analogous art because both involve developing information/data 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 developing techniques that automatically analyze acoustic features of audio streams and automatically generate exemplary reaction indications as disclosed by Li with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as disclosed by Sai.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Li and Sai to enable the detection and classification audio signals using neural network machine learning models (Sai, 1:25-33).
Li does not expressly disclose that the virtual meeting as hosted by a presenter as a webinar.
Bis does expressly teach the virtual meeting as hosted by a presenter as a webinar, in 0002] A virtual video conversation, also referred to as a virtual meeting, is a method of communication that allows multiple participants from anywhere in the world to meet in real time and to interact in the same space without physically being present. Through technological devices with reception and transmission of audio-video signal capabilities (i.e., laptops, tablets, webcams, etc.), participants can communicate back and forth with each other using audio, video conferencing, screen sharing, and webinars on a virtual meeting platform.
Additionally, Bis teaches that a emoji can be displayed as a non-verbal reaction, thus requiring no audio transmission, in [0054] In an embodiment, contextual timeline marker augmentation program 122 receives an input from the one or more participants as the virtual meeting chair is presenting. In an embodiment, contextual timeline marker augmentation program 122 receives an input from the one or more participants in the format of a verbal response or a non-verbal response [selecting a reaction… and transmitting the reaction to the virtual conference provider: and not transmitting the audio signals to the virtual conference provider](e.g., sending a chat message or sending an emoji that corresponds to a participant’s reaction to the virtual meeting chair’s presentation)…
Bis, Sai and Li are analogous art because both involve developing information/data 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 automated data processing techniques to support augmentation of virtual video conversations, as disclosed by Bis with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Bis, Sai and Li allows for automated contextual augmentation program to help capture relevant information about the user to create a user profile (Bis, 0043).
Regarding claim 6, the rejection of claim 1 is incorporated Sai further teaches the method of claim 1, wherein the ML model further comprises a plurality of fully connected layers. (in 4:33-37: The neural network 100 provides the third representation to one or more DNN layers 108. The DNN layers may be feed-forward fully connected layers [wherein the ML model further comprises a plurality of fully connected layers] with k hidden layers and n hidden units per layer…)
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 Li and Sai for the same reasons disclosed above.
Regarding claim 8, the rejection of claim 1 is incorporated and Li in combination with Sai and Bis teaches the method of claim 1, further comprising: receiving an aggregated reaction from the virtual conference provider; (in 0057] As indicated in the foregoing, an intensity evaluation of the present disclosure enables the acoustic analysis component 106 to gain an aggregate understanding of an audio stream or group of audio streams (e.g., from a plurality of users) [further comprising: receiving an aggregated reaction from the virtual conference provider]. This evaluation can help determine how to present a reaction indication. For instance, determination of intensity of one or more audio streams may be utilized to select one or more of: a size of the reaction indication (or content provided thereof); a quantity of content (e.g., icons/emojis) for inclusion in the reaction indication; an arrangement of content included in the reaction indication; and a layout of content included in the reaction indication, among other examples. In examples where a reaction indication is generated for a plurality of audio streams (e.g., of a group of users), intensity determinations may be used to reflect the collective (or aggregate) view of a plurality of users…; And in 0071-0072: …For example, components may be executed on one or more network-enabled computing devices, connected over a distributed network, that enable access to live presentations (e.g., live electronic presentations) through a presentation broadcast service [from the virtual conference provider]. While some examples described herein reference content provided through a presentation service or presentation broadcast service, it is to be recognized that the present disclosure may be configured to work with any type of application/service in which content may be presented without departing from the spirit of the present disclosure. For instance, a GUI of an application/service may be adapted and configured to provide GUI elements that, when selected, enable presentation of content therethrough and analysis of audio streams to generate reaction indications described herein…. As identified above, one or more different types of users may be connected to a live presentation (e.g., presenter(s), on-site audience, remote audience). State information for a user connection may be identified, for example, to determine whether audio streams of specific users are to be monitored. Exemplary state information may pertain to user configuration settings while attending a live presentation (e.g., live electronic presentation) [from the virtual conference provider]. Users may be presented, through a GUI of an application/service, GUI elements that enable users to control aspects of a live presentation such as whether they allow their audio streams to be monitored during the live presentation.)
and outputting one or more graphical representations of the aggregated reaction.. (in 0057-0058: .. For instance, an aggregate reaction indication may be generated for a group of users that presents a summarized representation of content for a group of users rather than providing individual icons representative of each response by an audience member… triggered reaction may be generated based on a result of the aggregation analysis (processing operation 128). As indicated in the foregoing, a triggered reaction may be a reaction indication. Exemplary reaction indications provide feedback for live presentations that can be presented [and outputting one or more graphical representations of the aggregated reaction] in real-time (or near real-time) without requiring a user to manually take action to provide any feedback. As an example, a reaction indication may then be generated that provides a visual representation of a user reaction to the live presentation [and outputting one or more graphical representations of the aggregated reaction]. Generation of an exemplary reaction indication may occur based on a result of analysis of the one or more frames of the audio stream that correlate with the one or more of the target classes and/or non-target classes. )
Regarding claims 10 and 16, the limitations are similar to claim 1 and the claims are rejected under the same rationale as claim 1.
Li teaches a non-transitory computer-readable medium; a communications interface; and one or more processors communicatively coupled to the non-transitory computer-readable medium and the communications interface, the one or more processors configured to execute processor-executable instructions stored in the non- transitory computer-readable medium to: (in 0095-0100: IG. 4 illustrates a computing system 401 suitable for implementing processing operations described herein related to generation and provision of reaction indications to a live presentation, with which aspects of the present disclosure may be practiced. As referenced above, computing system 401 may be configured to implement processing operations of any component described herein including the linguistic analysis component(s). As such, computing system 401 may be configured to execute specific processing operations to solve the technical problems described herein, which comprise processing operations for analyzing acoustic aspects of audio streams, generation of reaction indications to live presentation (e.g., live electronic presentations) and rendering/provisioning of exemplar reaction indications… Referring still to FIG. 4, processing system 402 may comprise processor, a micro-processor and other circuitry that retrieves and executes software 405 from storage system 403… Software 405 may be implemented in program instructions and among other functions may, when executed by processing system 402, direct processing system 402 to operate as described with respect to the various operational scenarios, sequences, and processes illustrated herein. For example, software 405 may include program instructions for executing one or more acoustic analysis component(s) 406a as described herein….)
Regarding claim 15, the limitations are similar to claim 6 and the claims are rejected under the same rationale as claim 6.
Claims 2-5, 11-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20220038580, hereinafter ‘Li’) in view of Sai and Bis, in further view of Le et al. (US 20190340236, hereinafter ‘Le’).
Regarding claim 2, the rejection of claim 1 is incorporated and Li does not expressly teach claim 2 limitations. Sai teaches the method of claim 1, wherein the ML model further comprises a … recurrent unit between the plurality of CNN layers and the at least one fully connected layer. (in 2:25-30: Providing, by the automated voice activity detection system, the raw audio waveform to the neural network may include providing, by the automated voice activity detection system, the raw audio waveform to a convolutional, long short-term memory [wherein the ML model further comprises a … recurrent unit between the plurality of CNN layers and the at least one fully connected layer.], fully connected deep neural network (CLDNN).)
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 Li and Sai for the same reasons disclosed above.
Sai does not expressly teach the use of claimed …gated recurrent unit between the plurality of CNN layers and the at least one fully connected layer.
Le expressly teaches teach the use of claimed ...gated recurrent unit between the plurality of CNN layers and the at least one fully connected layer. (As depicted in Fig. 1 [wherein the ML model further comprises a gated recurrent unit between the plurality of CNN layers and the at least one fully connected layer] and in 0046 The recurrent neural network 108 [wherein the ML model further comprises a gated recurrent unit between the plurality of CNN layers and the at least one fully connected layer] may be implemented as any appropriate neural network that maintains an internal state between time steps, for example, as a long short-term memory (LSTM) network, a recurrent neural network with gated recurrent units [wherein the ML model further comprises a gated recurrent unit between the plurality of CNN layers and the at least one fully connected layer], or a recurrent multi-layer perceptron network (RMLP). In some cases, the recurrent neural network 108 may be augmented by an external memory… )
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Le, Bis, Sai and Li are analogous art because both involve developing information/data 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 developing techniques that automatically processing sequential data derived from electronic records using neural network models as disclosed by Le with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Bis, Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Le, Bis, Sai and Li to enable a classification system for processing sequential language data using recurrent neural network models (Le, 0020-0021). Doing so allows developing a text processing system that can process text sequences to effectively generate corresponding outputs by processing only a fraction processed sequence input data using a recurrent neural network, (Le, 0027).
Regarding claim 3, the rejection of claim 1 is incorporated. Li does not expressly teach claim 2 limitation. Sai teaches the method of claim 1, … a first fully connected layer of the at least one fully connected layers. (in 4:33-42: The neural network 100 provides the third representation to one or more DNN layers 108. The DNN layers may be feed-forward fully connected layers […a first fully connected layer of the at least one fully connected layers] with k hidden layers and n hidden units per layer. The DNN layers 108 may use a rectified linear unit (ReLU) function for each hidden layer. The DNN layers 108 may use a softmax function with two units to predict speech and non-speech in the raw audio waveform. For example, the DNN layers 108 [ … a first fully connected layer of the at least one fully connected layers] may output a value, e.g., a binary value, that indicates whether the raw audio waveform included speech…)
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 Li and Sai for the same reasons disclosed above.
Sai does not expressly teach the use of claimed wherein the ML model further comprises a skip connection between an input node of the ML model and a first fully connected layer of the at least one fully connected layers.
Le expressly teaches teach the use of claimed wherein the ML model further comprises a skip connection between an input node of the ML model and a first fully connected layer of the at least one fully connected layers. (As depicted in Fig. 1 [wherein the ML model further comprises a skip connection between an input node of the ML model and a first fully connected layer of the at least one fully connected layers] and in 0050: As will be described in more detail with reference to FIG. 3, the system 100 can determine which tokens to designate as tokens to be skipped [wherein the ML model further comprises a skip connection between an input node of the ML model] using a jump prediction neural network 112. More specifically, after the recurrent neural network 108 [a first fully connected layer of the at least one fully connected layers] processes a respective token from the text sequence 102 to update the internal state 110 of the recurrent neural network 108, the system 100 may provide the updated internal state 110 of the recurrent neural network 108 as an input to the jump prediction neural network 112…)
Le, Bis, Sai and Li are analogous art because both involve developing information/data 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 developing techniques that automatically processing sequential data derived from electronic records using neural network models as disclosed by Le with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Bis, Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Le, Bis, Sai and Li to enable a classification system for processing sequential language data using recurrent neural network models (Le, 0020-0021). Doing so allows developing a text processing system that can process text sequences to effectively generate corresponding outputs by processing only a fraction processed sequence input data using a recurrent neural network, (Le, 0027).
Regarding claim 4, the rejection of claim 1 is incorporated and Li in combination with Sai and Bis teaches the method of claim 1, further comprising selecting the reaction having a … probability of the plurality of candidate reactions. (in 0069: …An exemplary rolling window creates an overlap (e.g., 10 second clip) which can be used to individually evaluate frames of an audio stream as well as enable the trained AI processing to aggregate frames for a collective/aggregate analysis. For each window, the trained AI modeling generates scores for each class [further comprising selecting the reaction having a … probability of the plurality of candidate reaction] (target and non-target) and a model prediction is provided…; And in 0076: …. Respective trained AI models (e.g., audio analysis models) may apply one or more classifiers trained to analyze acoustic features of audio signal data relative to target and non-target classes. That is, trained AI modeling may comprise trained data indicating target classes that each identify specific user reactions [further comprising selecting the reaction having a … probability of the plurality of candidate reaction] to the live electronic presentation as well as trained data indicating non-target classes that each identify audio types that are associated with the locational classification of the user…)
Li, Sai, and Bis does not expressly teach the limitation ….greatest probability of the plurality of candidate ….
Sai expressly teaches the limitation ….greatest probability of the plurality of candidate … (in 0058: The system 100 may select an output 104 from the set of possible outputs based on the output scores [...greatest probability of the plurality of candidate …] 122... For example, the system 100 may determine a probability distribution over the set of possible outputs from the output scores 122 (e.g., by processing the output scores 122 using a soft-max function) and sample an output 104 in accordance with the probability distribution. In some implementations, the system 100 selects an output 104 based on the output scores 122 by selecting an output 104 from the set of possible outputs with a highest score [….greatest probability of the plurality of candidate …] according to the output scores 122. )
Le, Bis, Sai and Li are analogous art because both involve developing information/data 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 developing techniques that automatically processing sequential data derived from electronic records using neural network models as disclosed by Le with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Bis, Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Le, Bis, Sai and Li to enable a classification system for processing sequential language data using recurrent neural network models (Le, 0020-0021). Doing so allows developing a text processing system that can process text sequences to effectively generate corresponding outputs by processing only a fraction processed sequence input data using a recurrent neural network, (Le, 0027).
Regarding claim 5, the rejection of claim 1 is incorporated and Li in combination with Sai and Bis teaches the method of claim 1, further comprising selecting the reaction exceeding a first threshold (in 0053] To improve processing efficiency and reduce latency, some of the controllable triggering rules, when applied, may be configured to filter out frames and/or audio streams that do not appear to correlate with one of the target classes of a trained AI model. This may remove the need for subsequent processing to be applied to every audio stream even when it may be discarded or not used for generation of a reaction indication. For example, a threshold (e.g., confidence scoring) [further comprising selecting the reaction exceeding a first threshold] may be set to determine if one or more frames of an audio stream comprises audio signal data that correlates with a target class [further comprising selecting the reaction exceeding a first threshold]. In cases where audio signal data does not correlate with a target class, that audio may be discarded from further analysis…)
and having a … probability of the plurality of candidate reactions. (in 0069: …An exemplary rolling window creates an overlap (e.g., 10 second clip) which can be used to individually evaluate frames of an audio stream as well as enable the trained AI processing to aggregate frames for a collective/aggregate analysis. For each window, the trained AI modeling generates scores for each class [ having a … probability of the plurality of candidate reactions] (target and non-target) and a model prediction is provided…; And in 0076: …. Respective trained AI models (e.g., audio analysis models) may apply one or more classifiers trained to analyze acoustic features of audio signal data relative to target and non-target classes. That is, trained AI modeling may comprise trained data indicating target classes that each identify specific user reactions [further comprising selecting the reaction having a … probability of the plurality of candidate reaction] to the live electronic presentation as well as trained data indicating non-target classes that each identify audio types that are associated with the locational classification of the user…)
Li and Sai does not expressly teach the limitation having a greatest probability of the plurality of candidate...
Sai expressly teaches the limitation …. having a greatest probability of the plurality of candidate... (in 0058: The system 100 may select an output 104 from the set of possible outputs based on the output scores [...having a greatest probability of the plurality of candidate...] 122... For example, the system 100 may determine a probability distribution over the set of possible outputs from the output scores 122 (e.g., by processing the output scores 122 using a soft-max function) and sample an output 104 in accordance with the probability distribution. In some implementations, the system 100 selects an output 104 based on the output scores 122 by selecting an output 104 from the set of possible outputs with a highest score [….having a greatest probability of the plurality of candidate...] according to the output scores 122. )
Le, Bis, Sai and Li are analogous art because both involve developing information/data 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 developing techniques that automatically processing sequential data derived from electronic records using neural network models as disclosed by Le with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Bis, Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Le, Bis, Sai and Li to enable a classification system for processing sequential language data using recurrent neural network models (Le, 0020-0021). Doing so allows developing a text processing system that can process text sequences to effectively generate corresponding outputs by processing only a fraction processed sequence input data using a recurrent neural network, (Le, 0027).
Regarding claims 11-15, the claim limitations are similar to claim 2-5 respectively and thus rejected under the same rationale.
Regarding claims 17-20, the claim limitations are similar to claim 2-5 respectively and thus rejected under the same rationale.
Claims 7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Sai and Bis, in further view of Levacher et al. (US 20230419934, hereinafter ‘Lev’).
Regarding claim 7, the rejection of claim 1 is incorporated and Li in combination with Sai teaches the method of claim 1, wherein the plurality of candidate reactions ….; (in 0065: Moreover, in some examples, an additional level of deep learning may be applied to improve the accuracy of exemplary audio analysis models (e.g., specifically trained AI models). For instance, processing operation 148 describes the usage of an attention layer, which is a trained layer of AI processing (e.g., CNN, RNN, Transformer or the like) that is specifically configured to draw correlations between acoustic feature data and classification predictions usable to classify audio streams and/or generate reaction indications [wherein the plurality of candidate reactions]…)
Li, Sai and Bis does not expressly teach the use of claimed … candidate reactions comprises a clapping reaction, a cheering reaction, or a laughing reaction.
Lev expressly teaches teach the use of claimed … candidate reactions comprises a clapping reaction, a cheering reaction, or a laughing reaction. (in 0032: …. Using these machine learning techniques 246, controller 110 may improve an ability to dynamically generate vocal data that is synchronized with the band and feels organic to the environment. For example, controller 110 may identify over time certain types of modifications that improve or decrease synchronization (e.g., when to slow down or speed up vocal output), and may further learn what types of modifications cause positive audience reactions [candidate reactions comprises a clapping reaction, a cheering reaction, or a laughing reaction] (e.g., cause the audience to cheer, to clap, to laugh) and/or limit negative audience reactions (e.g., the audience suddenly getting unexpectedly quiet, or the audience snickering, or the like), therein modifying thresholds and preferences of threshold and preference data 242 and/or updating rules of a model controlling controller 110 actions accordingly..)
Lev, Bis, Sai and Li are analogous art because both involve developing information/data 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 developing techniques that automatically processing audio data using neural network machine learning models as disclosed by Lev with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Bis, Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Le, Bis, Sai and Li to enable a system for learning the types of natural language data modifications cause positive audience reactions (Lev, 0031-0032).
Regarding claim 9, the rejection of claim 1 is incorporated and Li in combination with Sai and Bis teaches the method of claim 8, wherein the aggregated reaction comprises a … reaction. (in 0065: Moreover, in some examples, an additional level of deep learning may be applied to improve the accuracy of exemplary audio analysis models (e.g., specifically trained AI models). For instance, processing operation 148 describes the usage of an attention layer, which is a trained layer of AI processing (e.g., CNN, RNN, Transformer or the like) that is specifically configured to draw correlations between acoustic feature data and classification predictions usable to classify audio streams and/or generate reaction indications [wherein the aggregated reaction comprises a … reaction]…)
Li and Sai does not expressly teach the use of claimed … comprises a clapping reaction, a cheering reaction, or a laughing reaction.
Lev expressly teaches teach the use of claimed … comprises a clapping reaction, a cheering reaction, or a laughing reaction. (in 0032: …. Using these machine learning techniques 246, controller 110 may improve an ability to dynamically generate vocal data that is synchronized with the band and feels organic to the environment. For example, controller 110 may identify over time certain types of modifications that improve or decrease synchronization (e.g., when to slow down or speed up vocal output), and may further learn what types of modifications cause positive audience reactions [comprises a clapping reaction, a cheering reaction, or a laughing reaction] (e.g., cause the audience to cheer, to clap, to laugh) and/or limit negative audience reactions (e.g., the audience suddenly getting unexpectedly quiet, or the audience snickering, or the like), therein modifying thresholds and preferences of threshold and preference data 242 and/or updating rules of a model controlling controller 110 actions accordingly..)
Lev, Bis, Sai and Li are analogous art because both involve developing information/data 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 developing techniques that automatically processing audio data using neural network machine learning models as disclosed by Lev with the method of developing speech recognition system may detect voice activity in audio input using neural network machine learning models as collectively disclosed by Bis, Sai and Li.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Le, Bis, Sai and Li to enable a system for learning the types of natural language data modifications cause positive audience reactions (Lev, 0031-0032).
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.
Mitchell et al. (US 20230306666): teaches the in one example, a snigger sound and a belly laugh sound may elicit different visual reactions. In another example, a single clap may result in the processor controlling the user controlled object to make a sarcastic reaction, whereas sustained clapping may result in the processor controlling the user controlled object to make a cheering reaction. And recognizing that the user is laughing, it may be possible to check the video feed for visual confirmation of a laughing motion of the user before making a modification to the virtual environment based on detecting laughter.
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