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 .
Claim Objections
Claim 5 is objected to because of the following informalities:
In claim 5, line 4, “the first conversation to.” should read “the first conversation.”
Appropriate correction is required.
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 6-12 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.
Claim 6 recites the limitation “the first client device” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the first client device” has been interpreted as “a first client device”.
Dependent claims 7-12 are rejected based on being directly or indirectly dependent on rejected claim 6.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating … one or more parameters of a second chat bot based on the set of data”
“after updating the one or more parameters of the second chat bot, processing the first query … to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass updating parameters of a second chat bot based on the data set (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the set of data to update parameters of a second chat bot); and after updating the parameters of the second chat bot, processing a first query to generate a first response based on the context of the first conversation between the first user and the first chat bot (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a first computing device”
“a first chat bot”
“the first chat bot comprising a first machine learning model”
“by the first computing device”
“a second chat bot”
“the second chat bot comprising a second machine learning model”
“with the second chat bot”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“receiving, by a first computing device, a set of data corresponding to a context of a first conversation between a first user and a first chat bot”
“receiving, by the first computing device, a first query from a second user”
“presenting, by the first computing device, the first response to the second user”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“accessing at least one of an information source or a profile associated with the second user to generate the first response”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass accessing an information source or user profile associated with the second user to generate the first response (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate the first response based on an information source or profile associated with the second user).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 3 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“training, based on training data, the second machine learning model of the second chat bot to generate one or more responses, the training data comprising the portion of the first conversation between the first user and the first chat bot, the second machine learning model being trained to establish a relationship between one or more training contexts of one or more conversations and one or more ground- truth responses”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitation:
“wherein the set of data comprises a portion of the first conversation between the first user and the first chat bot”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 1 of receiving a set of data. The limitation of claim 3 further limits the imitation of claim 1 by further defining what the set of data comprises. In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models, and generic training of the machine learning model for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating, based on the portion of the parameters of the first machine learning model, one or more parameters of the second machine learning model of the second chat bot to generate one or more responses,”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass updating parameters of the second machine learning model based on the portion of parameters of the first machine learning model in order to generate the responses (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the portion of parameters of the first machine learning model to update parameters of the second machine learning model in order to generate the responses).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“the second machine learning model being trained to establish a relationship between one or more training contexts of one or more conversations and one or more ground-truth responses”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitation:
“wherein the set of data comprises a portion of parameters of the first machine learning model corresponding to the first conversation”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 1 of receiving a set of data. The limitation of claim 4 further limits the imitation of claim 1 by further defining what the set of data comprises. In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models, and generic training of the machine learning model for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating the portion of parameters of the first machine learning model by identifying a subset of parameters of the first machine learning model that correspond to features of the first conversation to”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating the portion of parameters of the first machine learning model by identifying a subset of parameters that correspond to features of the first conversation (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can identify a subset of parameters corresponding to features of the first conversation to generate the portion of parameters of the first machine learning model).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 4 of a generic computing device, chat bots, and machine learning models, and generic training of the machine learning model are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 4 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models, and generic training of the machine learning model for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 6 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“first client device”
“second client device”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“receiving, from the first client device of the first user, a message from the first user, the message comprising a link to the set of data”
“in response to receiving a request from a second client device of the second user to adopt the context of the first conversation between the first user and the first chat bot, accessing the link to retrieve the set of data”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 6. The limitations of claim 7 are only additional elements to the abstract ideas of claim 6.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the message is received as at least one of a text, video, or audio message, a post to a social network, a meta package as an ingredient to assimilate into another conversation factor, or content on a webpage”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 6 of receiving a message. The limitation of claim 7 further limits the imitation of claim 6 by further defining what the message comprises. In addition, the recitation of additional elements in claim 6 of a generic computing device, chat bots, client devices, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations of claim 6 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining that the location information of the second client device satisfies the one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot”
“controlling presentation of the message on the second client device based on determining that the location information of the second client device satisfies the one or more geographical restrictions”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining that the location information of the second client device satisfies the geographical restrictions associated with adopting the context of the first conversation between the first user and first chat bot (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that the location information of the second client device satisfies the geographical restrictions associated with adopting the context of the first conversation); and controlling presentation of the message on the second client device based on determining that the location information satisfies the geographical restrictions (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can based on determining that the location information of the second client device satisfies the geographical restrictions, control the presentation of the message).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“in response to receiving the request to adopt the context, accessing location information of the second client device”
“accessing one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 6 of a generic computing device, chat bots, client devices, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations of claim 6 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 6. The limitations of claim 9 are only additional elements to the abstract ideas of claim 6.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the message is displayed to client devices that are within a threshold distance of one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 6 of a generic computing device, chat bots, client devices, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations of claim 6 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/displaying/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, “… is displayed to client devices …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 6. The limitations of claim 10 are only additional elements to the abstract ideas of claim 6.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“transmitting a notification to the first client device of the first user in response to receiving the request from the second client device, the notification informing the first user that the context of the first conversation has been adopted by one or more other users”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 6 of a generic computing device, chat bots, client devices, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …”, “presenting …”, and “accessing …” limitations of claim 6 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, “transmitting …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 10. The limitations of claim 11 are only additional elements to the abstract ideas of claim 10.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the notification identifies the second user to the first user”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 10 of transmitting a notification. The limitation of claim 11 further limits the imitation of claim 10 by further defining what the notification comprises. In addition, the recitation of additional elements in claim 10 of a generic computing device, chat bots, client devices, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …”, “presenting …”, “transmitting …”, and “accessing …” limitations of claim 10 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, “transmitting …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 10. The limitations of claim 12 are only additional elements to the abstract ideas of claim 10.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the one or more other users remain anonymous to the first user”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 10 of transmitting a notification. The limitation of claim 12 further limits the imitation of claim 10 by further defining the one or more other users. In addition, the recitation of additional elements in claim 10 of a generic computing device, chat bots, client devices, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …”, “presenting …”, “transmitting …”, and “accessing …” limitations of claim 10 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, client devices, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving, presenting/transmitting, accessing/retrieving data). Furthermore, the “receiving …”, “presenting …”, “transmitting …”, and “accessing …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating the one or more parameters of the second chat bot without sharing or revealing content of the first conversation between the first user and the first chat bot”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass updating the parameters of the second chat bot without sharing or revealing context of the first conversation between the first user and first chat bot (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can, without sharing or revealing content of the first conversation, update the parameters of the second chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein prior to updating the one or more parameters of the second chat bot, … generate a second response to the first query that is different from the first response”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass, prior to updating the parameters of the second chat bot, generating a second response to the first query that is different from the first response (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a second response to the first query that is different from the first response prior to updating the parameters of the second chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“the second chat bot”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the first response comprises one or more unstructured responses”
As drafted, is part of the abstract idea of claim 1 of generating a first response to the first query. The limitation of claim 15 further limits the limitation of claim 1 by further defining what the first response comprises. The above limitation in the context of this claim encompasses after updating the parameters of the second chat bot, processing a first query to generate a first response comprising unstructured responses based on the context of the first conversation between the first user and the first chat bot (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a first response comprising unstructured responses to the first query based on the context of the first conversation between the first user and the first chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the first query comprises one or more unstructured natural language words or phrases or graphical representations such as emojis”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 1 of receiving a first query. The limitation of claim 15 further limits the imitation of claim 1 by further defining what the first query comprises. In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating the first conversation in response to a plurality of interactions between the first user and the first chat bot, the plurality of interactions comprising a set of queries and corresponding responses”
“identifying the context based on the selected portion of the plurality of interactions, the set of data being generated based on the identified context”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating the first conversation in response to a plurality of interactions between the first user and the first chat bot, the interaction comprising queries and corresponding responses (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can, in response to a plurality of interaction comprising queries and responses between a first user and a first chat bot, generate a first conversation); and identifying the context based on the selected portion of the plurality of interactions to generate the set of data comprising the identified context (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can identify the context based on the selected portion of the interactions in order to generate the set of data).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“receiving a first input from the first user that selects a portion of the plurality of interactions”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 16. The limitations of claim 17 are only additional elements to the abstract ideas of claim 16.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“receiving a second input from the first user to share the identified context of the first conversation with one or more other users through at least one of an encrypted message or a non-fungible token (NFT)”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 16 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 16 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating at least one additional parameter of the second chat bot based on the at least one of positive or negative feedback”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass updating an additional parameter of the second chat bot based on the positive or negative feedback (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the positive or negative feedback to update an additional parameter of the second chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“receiving input from the second user that comprises at least one of positive or negative feedback in relation to the first response”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 1 of a generic computing device, chat bots, and machine learning models are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “receiving …” and “presenting …” limitations of claim 1 are additional elements that correspond to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computing device, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating one or more parameters of a second chat bot based on the set of data”
“after updating the one or more parameters of the second chat bot, processing the first query … to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass updating parameters of a second chat bot based on the data set (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the set of data to update parameters of a second chat bot); and after updating the parameters of the second chat bot, processing a first query to generate a first response based on the context of the first conversation between the first user and the first chat bot (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a memory that stores instructions”
“one or more processors configured by the instructions”
“a first chat bot”
“the first chat bot comprising a first machine learning model”
“a second chat bot”
“the second chat bot comprising a second machine learning model”
“with the second chat bot”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“receiving a set of data corresponding to a context of a first conversation between a first user and a first chat bot”
“receiving a first query from a second user”
“presenting the first response to the second user”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic memory, processors, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a non-transitory computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating one or more parameters of a second chat bot based on the set of data”
“after updating the one or more parameters of the second chat bot, processing the first query … to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass updating parameters of a second chat bot based on the data set (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the set of data to update parameters of a second chat bot); and after updating the parameters of the second chat bot, processing a first query to generate a first response based on the context of the first conversation between the first user and the first chat bot (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“at least one processor”
“a first chat bot”
“the first chat bot comprising a first machine learning model”
“a second chat bot”
“the second chat bot comprising a second machine learning model”
“with the second chat bot”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“receiving a set of data corresponding to a context of a first conversation between a first user and a first chat bot”
“receiving a first query from a second user”
“presenting the first response to the second user”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor, chat bots, and machine learning models for applying the abstract ideas) or insignificant extra-solution activity (i.e. receiving and presenting/transmitting data). Furthermore, the “receiving …” and “presenting …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 13-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moon et al. (US 2020/0112526 A1) in view of Bobbarjung et al. (US 2019/0124020 A1).
Regarding Claim 1,
Moon et al. teaches a method ([0004]: "a computer-implemented method includes receiving, using at least one processor, a first signal from a device that includes messaging information, and determining, using the at least one processor, a candidate parameter value for a first parameter of an exchange of data based on the messaging information and information characterizing prior exchanges of data between the device and a computing system" teaches a computer-implemented method for performing operations) comprising:
receiving, by a first computing device, a set of data corresponding to a context of a first conversation between a first user and a first chat bot, the first chat bot comprising a first machine learning model (Fig. 3B; [0083]-[0085]: "predictive engine 146 may receive contextual information 328 … predictive engine 146 may also access transaction database 134, and obtain first transaction data 344A, which characterizes prior exchanges of data involving user 101, and second transaction data 344B, which characterizes prior exchanges of data involving the additional users associated with transaction system 130. Additionally, based on extracted user identifier 306 or extracted device identifier 308, predictive engine 146 may also access chatbot session database 136, and obtain first chatbot session data 346A, which characterizes prior chatbot sessions involving user 101, and second chatbot session data 346B, which characterizes prior chatbot sessions involving the additional users of transaction system 130" teaches the predictive engine of the transaction system (first computing device) receiving contextual information (data corresponding to a context) and first chatbot session data 346A (conversation between first user and first chatbot) for a user 101 (first user). Fig. 1; [0039]-[0040]: "Examples of these natural language processing algorithms may include one or more machine learning processes … the one or more natural language processing algorithms may also include one or more artificial intelligence models … in some examples, the functions of NLP engine 144 may be performed by chatbot engine 142 (e.g., NLP engine 144 is part or component of chatbot engine 142)" teaches that the chatbot engine 142 (first chatbot) comprises the NLP engine 144 comprising natural language processing algorithms including a machine learning model (e.g. the chatbot comprises a machine learning model)).
Moon et al. does not appear to explicitly teach updating, by the first computing device, one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model; receiving, by the first computing device, a first query from a second user; after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot; and presenting, by the first computing device, the first response to the second user.
However, Bobbarjung et al. teaches updating, by the first computing device, one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model (Fig. 1; [0030]: "The multiple users 114, 116, and 118 include any individuals or groups that interact with services 104-108, data source 112, and bot creation and management system 102. In some embodiments, one or more of the users 114-118 are communicating with one or more of the services 104-108 or bot creation and management system 102 using an intelligent conversational interface, chatbot, or voice assistant" teaches a bot creation and management system 102 (computing device) for building chatbots for interactions with multiple users. [0104]-[0105]: "The systems and methods described herein need to be able to recognize correctly the customer's intent in order to give correct and intelligent responses. It is a foundational part of the chatbot system. Given any text input (or text converted from voice input), the system is able to correctly identify the intention behind this message. A machine learning system (also referred to as a machine learning model) handles this task … The machine learning system has two distinct parts: training and prediction" teaches that the chatbot system comprises a machine learning model (e.g. second chatbot comprises second machine learning model) that is trained (updated). [0115]-[0117]: "In the training phase, the systems and methods provide data (typically a large size of data) into a machine learning model and let the model “learn” to recognize predefined patterns. The machine “learns” through a mathematical optimization procedure. In an intent identification module, the system uses deep learning techniques … the training data comes from customer service logs or other applicable conversation logs. Each data point consists of the text content (what the customer was saying) and a ground-truth label (what is the true intent) … The output layer of the neural network consists of N cells, where N is the number of intents (classes). To learn the parameters in the network (the weight on each link in the neural network), the system uses the stochastic gradient descent method" teaches that the machine learning model of the chatbot is trained (parameters updated) based on context of conversation logs (set of data) of previous users (e.g. second chatbot for second user is trained based on conversation logs of a first user with a first chatbot));
receiving, by the first computing device, a first query from a second user (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system" teaches that the bot management system (computing device) receives a request (first query) from a remotes system (e.g. from a second user));
after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system. The bot management system analyzes 504 the text data or voice data in the request to determine an intent associated with the request. Based on the intent associated with the request, the bot management system generates 506 a response to the request. In some embodiments, the response generated 506 may also include declarative configuration information, or any other data, as discussed herein" teaches that the chatbot system (e.g. second chatbot) analyzes the request (first query) and generates a response based on the intent (conversation context) associated with the request. [0058]: "The follow-on/conversation intents are invoked based on the context of the conversation" teaches that the intents are invoked based on the context of the conversation. [0118]: "A prediction phase is part of the production pipeline. For each input message, the system first process it according to the steps defined in the text pre-processing steps to get its clean vector representation. The system then sends the word vectors into the LSTM-RNN model built from training. The model then gives a score between 0 and 1 to each label (possible intent). These scores (one per label) are normalized such that they sum to 1 and represent probabilities. The highest score is associated with the most likely intent, according to the model. The system outputs this intent and the score to the front-end of the system" teaches that the chatbot system (e.g. second chatbot) performs an intent prediction for an input message (processing first query) used for generating a response after training the machine learning model of the chatbot (e.g. second machine learning model of second chatbot) is trained (parameters updated) based on context of conversation logs (set of data) of previous users (first conversation between first user and first chatbot)); and
presenting, by the first computing device, the first response to the second user (Fig. 5; [0047]: "The bot management system then communicates 508 the response to the remote system" teaches that the bot management system (computing device) communicates (presents) the response (first response) to the remote system of the user (e.g. second user)).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate updating, by the first computing device, one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model; receiving, by the first computing device, a first query from a second user; after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot; and presenting, by the first computing device, the first response to the second user as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 2,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Bobbarjung et al. further teaches further comprising: accessing at least one of an information source or a profile associated with the second user to generate the first response (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system. The bot management system analyzes 504 the text data or voice data in the request to determine an intent associated with the request. Based on the intent associated with the request, the bot management system generates 506 a response to the request. In some embodiments, the response generated 506 may also include declarative configuration information, or any other data, as discussed herein" teaches that the chatbot system (e.g. second chatbot) analyzes the request (first query) and generates a response based on the intent (conversation context) associated with the request, declarative configuration information, and any other data discussed herein (e.g. user profile data). [0059]-[0067]: "The systems and methods described herein enable a rich set of interactions that are configured using a GUI and do not require the creator to write any code. Some of the supported actions supported include: … Support for storing/retrieving/deleting data from User profile" teaches that a user profile can be accessed for performing the described methods (e.g. for generating a response)).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising: accessing at least one of an information source or a profile associated with the second user to generate the first response as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 3,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Moon et al. further teaches wherein the set of data comprises a portion of the first conversation between the first user and the first chat bot (Fig. 3B; [0083]-[0085]: "predictive engine 146 may receive contextual information 328 … predictive engine 146 may also access transaction database 134, and obtain first transaction data 344A, which characterizes prior exchanges of data involving user 101, and second transaction data 344B, which characterizes prior exchanges of data involving the additional users associated with transaction system 130. Additionally, based on extracted user identifier 306 or extracted device identifier 308, predictive engine 146 may also access chatbot session database 136, and obtain first chatbot session data 346A, which characterizes prior chatbot sessions involving user 101, and second chatbot session data 346B, which characterizes prior chatbot sessions involving the additional users of transaction system 130" teaches the predictive engine of the transaction system (first computing device) receiving contextual information (data corresponding to a context) and first chatbot session data 346A (conversation between first user and first chatbot) for a user 101 (first user). [0093]: "predictive engine 146 may also perform operations that, for one or more of the missing parameter values, predict a corresponding candidate parameter value based on an application of one or predictive models to model input data selectively extracted or derived from portions of identified parameter data 336, first and second user data 342A and 342B, first and second transaction data 344A and 344B, and first and second chatbot session data 346A and 346B" teaches that the predictive engine performs operations on a portion of the first chatbot session data 346A (conversation between first user and first chatbot)).
Additionally, Bobbarjung et al. further teaches the method further comprising: training, based on training data, the second machine learning model of the second chat bot to generate one or more responses, the training data comprising the portion of the first conversation between the first user and the first chat bot, the second machine learning model being trained to establish a relationship between one or more training contexts of one or more conversations and one or more ground- truth responses ([0115]-[0117]: "In the training phase, the systems and methods provide data (typically a large size of data) into a machine learning model and let the model “learn” to recognize predefined patterns. The machine “learns” through a mathematical optimization procedure. In an intent identification module, the system uses deep learning techniques … the training data comes from customer service logs or other applicable conversation logs. Each data point consists of the text content (what the customer was saying) and a ground-truth label (what is the true intent) … The output layer of the neural network consists of N cells, where N is the number of intents (classes). To learn the parameters in the network (the weight on each link in the neural network), the system uses the stochastic gradient descent method" teaches that the machine learning model of the chatbot is trained (parameters updated) based on training data comprising context of conversation logs of previous users (e.g. second chatbot for second user is trained based on conversation logs of a first user with a first chatbot), the training being to learn intent identification (establish relationship) between text content of conversation logs (training contexts of conversations) and ground-truth labels (ground-truth responses). [0058]: "The follow-on/conversation intents are invoked based on the context of the conversation" teaches that the intents are invoked based on the context of the conversation. Fig. 5; [0047]: "The bot management system analyzes 504 the text data or voice data in the request to determine an intent associated with the request. Based on the intent associated with the request, the bot management system generates 506 a response to the request. In some embodiments, the response generated 506 may also include declarative configuration information, or any other data, as discussed herein" teaches that the trained chatbot system (e.g. second chatbot) generates a response based on the intent (conversation context) associated with the request).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method further comprising: training, based on training data, the second machine learning model of the second chat bot to generate one or more responses, the training data comprising the portion of the first conversation between the first user and the first chat bot, the second machine learning model being trained to establish a relationship between one or more training contexts of one or more conversations and one or more ground- truth responses as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 4,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Moon et al. further teaches wherein the set of data comprises a portion of parameters of the first machine learning model corresponding to the first conversation ([0093]: "predictive engine 146 may also perform operations that, for one or more of the missing parameter values, predict a corresponding candidate parameter value based on an application of one or predictive models to model input data selectively extracted or derived from portions of identified parameter data 336, first and second user data 342A and 342B, first and second transaction data 344A and 344B, and first and second chatbot session data 346A and 346B" teaches that the predictive engine performs operations on a portion of the first chatbot session data 346A (conversation between first user and first chatbot). Fig. 3B; [0088]: "first and second chatbot session data 346A and 346B may include values of parameters that characterize prior chatbot sessions involving respective ones of user 101 and the additional users, and established programmatically between application programs executed by transaction system 130 (e.g., chatbot engine 142) and network-connected devices or systems associated with respective ones of user 101 or the additional users" teaches that the chatbot session data 346A (set of data corresponding to context of conversation between first user and chatbot) comprises parameters characterizing the first conversation between the user 101 (first user) and the chatbot engine 142 (first chatbot) (e.g. comprises portion of parameters of chatbot engine comprising the ML model for the conversation). Fig. 1; [0039]-[0040]: "Examples of these natural language processing algorithms may include one or more machine learning processes … the one or more natural language processing algorithms may also include one or more artificial intelligence models … in some examples, the functions of NLP engine 144 may be performed by chatbot engine 142 (e.g., NLP engine 144 is part or component of chatbot engine 142)" teaches that the chatbot engine 142 (first chatbot) comprises the NLP engine 144 comprising natural language processing algorithms including a machine learning model (e.g. the chatbot comprises a machine learning model)).
Additionally, Bobbarjung et al. further teaches the method further comprising: updating, based on the portion of the parameters of the first machine learning model, one or more parameters of the second machine learning model of the second chat bot to generate one or more responses, the second machine learning model being trained to establish a relationship between one or more training contexts of one or more conversations and one or more ground-truth responses ([0115]-[0117]: "In the training phase, the systems and methods provide data (typically a large size of data) into a machine learning model and let the model “learn” to recognize predefined patterns. The machine “learns” through a mathematical optimization procedure. In an intent identification module, the system uses deep learning techniques … the training data comes from customer service logs or other applicable conversation logs. Each data point consists of the text content (what the customer was saying) and a ground-truth label (what is the true intent) … The output layer of the neural network consists of N cells, where N is the number of intents (classes). To learn the parameters in the network (the weight on each link in the neural network), the system uses the stochastic gradient descent method" teaches that the machine learning model of the chatbot is trained (parameters updated) to learn parameters of the network (e.g. second chatbot for second user is trained to learn (update) parameters based on conversation logs of a first chatbot with a first network model), the training being to learn intent identification (establish relationship) between text content of conversation logs (training contexts of conversations) and ground-truth labels (ground-truth responses). [0058]: "The follow-on/conversation intents are invoked based on the context of the conversation" teaches that the intents are invoked based on the context of the conversation. Fig. 5; [0047]: "The bot management system analyzes 504 the text data or voice data in the request to determine an intent associated with the request. Based on the intent associated with the request, the bot management system generates 506 a response to the request. In some embodiments, the response generated 506 may also include declarative configuration information, or any other data, as discussed herein" teaches that the trained chatbot system (e.g. second chatbot) generates a response based on the intent (conversation context) associated with the request).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method further comprising: updating, based on the portion of the parameters of the first machine learning model, one or more parameters of the second machine learning model of the second chat bot to generate one or more responses, the second machine learning model being trained to establish a relationship between one or more training contexts of one or more conversations and one or more ground-truth responses as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 5,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 4.
In addition, Moon et al. further teaches further comprising: generating the portion of parameters of the first machine learning model by identifying a subset of parameters of the first machine learning model that correspond to features of the first conversation to ([0093]: "predictive engine 146 may also perform operations that, for one or more of the missing parameter values, predict a corresponding candidate parameter value based on an application of one or predictive models to model input data selectively extracted or derived from portions of identified parameter data 336, first and second user data 342A and 342B, first and second transaction data 344A and 344B, and first and second chatbot session data 346A and 346B" teaches that the predictive engine performs operations on a portion of the first chatbot session data 346A (conversation between first user and first chatbot) that are selectively identified subsets of parameters for the first conversation (e.g. subset of parameters that correspond to features of the first conversation). Fig. 3B; [0088]: "first and second chatbot session data 346A and 346B may include values of parameters that characterize prior chatbot sessions involving respective ones of user 101 and the additional users, and established programmatically between application programs executed by transaction system 130 (e.g., chatbot engine 142) and network-connected devices or systems associated with respective ones of user 101 or the additional users" teaches that the chatbot session data 346A (set of data corresponding to context of conversation between first user and chatbot) comprises parameters characterizing the first conversation between the user 101 (first user) and the chatbot engine 142 (first chatbot) (e.g. comprises portion of parameters of chatbot engine comprising the ML model for the conversation). Fig. 1; [0039]-[0040]: "Examples of these natural language processing algorithms may include one or more machine learning processes … the one or more natural language processing algorithms may also include one or more artificial intelligence models … in some examples, the functions of NLP engine 144 may be performed by chatbot engine 142 (e.g., NLP engine 144 is part or component of chatbot engine 142)" teaches that the chatbot engine 142 (first chatbot) comprises the NLP engine 144 comprising natural language processing algorithms including a machine learning model (e.g. the chatbot comprises a machine learning model)).
Regarding Claim 13,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Bobbarjung et al. further teaches further comprising: updating the one or more parameters of the second chat bot without sharing or revealing content of the first conversation between the first user and the first chat bot ([0115]-[0117]: "In the training phase, the systems and methods provide data (typically a large size of data) into a machine learning model and let the model “learn” to recognize predefined patterns. The machine “learns” through a mathematical optimization procedure. In an intent identification module, the system uses deep learning techniques … the training data comes from customer service logs or other applicable conversation logs. Each data point consists of the text content (what the customer was saying) and a ground-truth label (what is the true intent) … The output layer of the neural network consists of N cells, where N is the number of intents (classes). To learn the parameters in the network (the weight on each link in the neural network), the system uses the stochastic gradient descent method" teaches that the machine learning model of the chatbot is trained (parameters updated) based on context of conversation logs (set of data) of previous users (e.g. second chatbot for second user is trained based on conversation logs of a first user with a first chatbot), but the conversation logs are only used for training and are not shared with the user).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising: updating the one or more parameters of the second chat bot without sharing or revealing content of the first conversation between the first user and the first chat bot as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 14,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Bobbarjung et al. further teaches wherein prior to updating the one or more parameters of the second chat bot, the second chat bot is configured to generate a second response to the first query that is different from the first response ([0126]: "The described systems and methods also perform sentiment analysis, which refers to detecting if a user's message is of positive or negative sentiment. Strongly negative sentiment means strong dissatisfaction and thus the bot may refer the user to a human customer service agent. This problem is formulated as a binary classification task, where there are two classes: negative (bad sentiment) and positive (OK or good sentiment). Each sentence, message, or portion of a message is categorized into one of the classes. The system also uses the Recurrent Neural Network technique with Long Short-Term Memory (LSTM-RNN) for this task. The rest of the process (training and scoring with LSTM-RNN) is quite similar to intent classification, as described above. Message text will be converted into vector representations and the system learns weights of the LSTM-RNN network using stochastic gradient descent" teaches that the chatbot provides an initial response (second response that is different from the first response after training) and then train the model (update chatbot model parameters) based on a user message as feedback in order to provide better responses to subsequent queries).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein prior to updating the one or more parameters of the second chat bot, the second chat bot is configured to generate a second response to the first query that is different from the first response as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 16,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Moon et al. further teaches further comprising: generating the first conversation in response to a plurality of interactions between the first user and the first chat bot, the plurality of interactions comprising a set of queries and corresponding responses (Fig. 2A; Fig. 2B; [0062]-[0066]: "Referring to FIG. 2A, client device 102 may present chatbot interface 200 on a corresponding portion of display unit 116A … The automatic presentation of introductory message 203 may simulate a conversation between user 101 and the programmatic chatbot maintained by transaction system 130, and as illustrated in FIG. 2A, introductory message greet user 101 and prompts user 101 to further interact with the established chatbot session … Referring to FIG. 2B, executed payment application 108 may process the input, and may present message 208 within a corresponding portion of fillable text box 204. Further, user 101 may provide additional input to client device 102 that requests a submission of message 208 to the established chatbot session by selecting “Submit” icon 206 … In response to the additional user input that selects “Submit” icon 206, executed payment application 108 may perform operations that package all or a portion of message 208 into corresponding portions of session data, along with the unique identifier of user" teaches generating a first chatbot session data (conversation) in response to a plurality of interactions between a first user and a first chatbot, the interactions comprising queries and corresponding responses);
receiving a first input from the first user that selects a portion of the plurality of interactions (Fig. 3B; [0096]-[0097]: "As illustrated in FIG. 3B, predictive engine 146 may obtain modelling data 348 (e.g., from one or more tangible, non-transitory memories) that specifies a composition and/or a structure of discrete elements of input data associated with each of the predictive models … the composition of structure of one or more of the discrete elements of input data may be … specific to a particular user or customer of the financial institution associated with transaction system 130. … In some examples, and based on portions of modelling data 348, predictive engine 146 may generate the discrete elements of input data based on a selective extraction and subsequent processing of certain portions of identified parameter data 336, first and second user data 342A and 342B, first and second transaction data 344A and 344B, and first and second chatbot session data 346A and 346B (e.g., as specified within modelling data 348)" teaches the predictive engine may receive selected portions of the first chatbot session data 346A (plurality of interactions between first user and first chatbot) for a user 101 (first user) based on input data from a user (e.g. input data from the user selects the portion of chatbot session data)); and
identifying the context based on the selected portion of the plurality of interactions, the set of data being generated based on the identified context ([0038]: "NLP engine 144 may apply one or more natural language processing algorithms to portions of received message data. Based on the application of these adaptive, statistical, or dynamic natural language processing algorithms, NLP engine 144 may parse the received message data to identify one or more discrete linguistic elements (e.g., a word, a combination of morphemes, a single morpheme, etc.), and to generate contextual information that establishes the meaning or a context of one or more discrete linguistic elements" teaches that the NLP engine 144 (chatbot) generates contextual information that establishes the meaning or a context of one or more discrete linguistic elements (set of data) of the selected portion of message data (plurality of interactions). Fig. 1; [0039]-[0040]: "Examples of these natural language processing algorithms may include one or more machine learning processes … the one or more natural language processing algorithms may also include one or more artificial intelligence models … in some examples, the functions of NLP engine 144 may be performed by chatbot engine 142 (e.g., NLP engine 144 is part or component of chatbot engine 142)" teaches that the chatbot engine 142 (first chatbot) comprises the NLP engine 144 comprising natural language processing algorithms including a machine learning model (e.g. the NLP engine is part of the chatbot)).
Regarding Claim 18,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
In addition, Bobbarjung et al. further teaches further comprising: receiving input from the second user that comprises at least one of positive or negative feedback in relation to the first response ([0126]: "The described systems and methods also perform sentiment analysis, which refers to detecting if a user's message is of positive or negative sentiment. Strongly negative sentiment means strong dissatisfaction and thus the bot may refer the user to a human customer service agent. This problem is formulated as a binary classification task, where there are two classes: negative (bad sentiment) and positive (OK or good sentiment). Each sentence, message, or portion of a message is categorized into one of the classes. The system also uses the Recurrent Neural Network technique with Long Short-Term Memory (LSTM-RNN) for this task. The rest of the process (training and scoring with LSTM-RNN) is quite similar to intent classification, as described above. Message text will be converted into vector representations and the system learns weights of the LSTM-RNN network using stochastic gradient descent" teaches receiving positive or negative user feedback to the response. [0142]: "As an example, a user message of “How can I find an ATM in a foreign country?” could match with both an ATM locator intent and a knowledge base article. While the system can always offer the user a choice between the two matches by presenting a question like “Would like to find an ATM by location or search the knowledge base?”, a better solution is to notify the bot creator that this conflict is occurring and giving the creator the option to choose the winner. In this case, searching the knowledge base is more appropriate for this request, so the bot creator provides that feedback. Subsequently, this enriches the data for training the models" teaches receiving input from the bot creator (second user) that provides feedback about which response is better for a given query (e.g. comprises positive feedback about the selected better response)); and
updating at least one additional parameter of the second chat bot based on the at least one of positive or negative feedback ([0126]: "The described systems and methods also perform sentiment analysis, which refers to detecting if a user's message is of positive or negative sentiment. Strongly negative sentiment means strong dissatisfaction and thus the bot may refer the user to a human customer service agent. This problem is formulated as a binary classification task, where there are two classes: negative (bad sentiment) and positive (OK or good sentiment). Each sentence, message, or portion of a message is categorized into one of the classes. The system also uses the Recurrent Neural Network technique with Long Short-Term Memory (LSTM-RNN) for this task. The rest of the process (training and scoring with LSTM-RNN) is quite similar to intent classification, as described above. Message text will be converted into vector representations and the system learns weights of the LSTM-RNN network using stochastic gradient descent" teaches receiving positive or negative user feedback to the response and training a parameter of the network based on this feedback. [0142]: "As an example, a user message of “How can I find an ATM in a foreign country?” could match with both an ATM locator intent and a knowledge base article. While the system can always offer the user a choice between the two matches by presenting a question like “Would like to find an ATM by location or search the knowledge base?”, a better solution is to notify the bot creator that this conflict is occurring and giving the creator the option to choose the winner. In this case, searching the knowledge base is more appropriate for this request, so the bot creator provides that feedback. Subsequently, this enriches the data for training the models" teaches that the provided feedback is used for enriching the data for the model training (e.g. the model of the chatbot is further trained (updated) based on the feedback)).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising: receiving input from the second user that comprises at least one of positive or negative feedback in relation to the first response; and updating at least one additional parameter of the second chat bot based on the at least one of positive or negative feedback as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 19,
Moon et al. teaches a system comprising: a memory that stores instructions; and one or more processors configured by the instructions to perform operations ([0003]: “an apparatus includes a communications unit, a storage unit storing instructions, and at least one processor coupled to the communications unit and the storage unit. The at least one processor is configured to execute the instructions to receive, via the communications unit, a first signal from a device that includes messaging information, and determine a candidate parameter value for a first parameter of an exchange of data based on the messaging information and on information characterizing prior exchanges of data between the device and the apparatus" teaches an apparatus comprising a storage unit (memory) storing instructions and a processor configured to execute the instructions to perform operations) comprising:
receiving a set of data corresponding to a context of a first conversation between a first user and a first chat bot, the first chat bot comprising a first machine learning model (Fig. 3B; [0083]-[0085]: "predictive engine 146 may receive contextual information 328 … predictive engine 146 may also access transaction database 134, and obtain first transaction data 344A, which characterizes prior exchanges of data involving user 101, and second transaction data 344B, which characterizes prior exchanges of data involving the additional users associated with transaction system 130. Additionally, based on extracted user identifier 306 or extracted device identifier 308, predictive engine 146 may also access chatbot session database 136, and obtain first chatbot session data 346A, which characterizes prior chatbot sessions involving user 101, and second chatbot session data 346B, which characterizes prior chatbot sessions involving the additional users of transaction system 130" teaches the predictive engine of the transaction system receiving contextual information (data corresponding to a context) and first chatbot session data 346A (conversation between first user and first chatbot) for a user 101 (first user). Fig. 1; [0039]-[0040]: "Examples of these natural language processing algorithms may include one or more machine learning processes … the one or more natural language processing algorithms may also include one or more artificial intelligence models … in some examples, the functions of NLP engine 144 may be performed by chatbot engine 142 (e.g., NLP engine 144 is part or component of chatbot engine 142)" teaches that the chatbot engine 142 (first chatbot) comprises the NLP engine 144 comprising natural language processing algorithms including a machine learning model (e.g. the chatbot comprises a machine learning model)).
Moon et al. does not appear to explicitly teach updating one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model; receiving a first query from a second user; after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot; and presenting the first response to the second user.
However, Bobbarjung et al. teaches updating one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model (Fig. 1; [0030]: "The multiple users 114, 116, and 118 include any individuals or groups that interact with services 104-108, data source 112, and bot creation and management system 102. In some embodiments, one or more of the users 114-118 are communicating with one or more of the services 104-108 or bot creation and management system 102 using an intelligent conversational interface, chatbot, or voice assistant" teaches a bot creation and management system 102 for building chatbots for interactions with multiple users. [0104]-[0105]: "The systems and methods described herein need to be able to recognize correctly the customer's intent in order to give correct and intelligent responses. It is a foundational part of the chatbot system. Given any text input (or text converted from voice input), the system is able to correctly identify the intention behind this message. A machine learning system (also referred to as a machine learning model) handles this task … The machine learning system has two distinct parts: training and prediction" teaches that the chatbot system comprises a machine learning model (e.g. second chatbot comprises second machine learning model) that is trained (updated). [0115]-[0117]: "In the training phase, the systems and methods provide data (typically a large size of data) into a machine learning model and let the model “learn” to recognize predefined patterns. The machine “learns” through a mathematical optimization procedure. In an intent identification module, the system uses deep learning techniques … the training data comes from customer service logs or other applicable conversation logs. Each data point consists of the text content (what the customer was saying) and a ground-truth label (what is the true intent) … The output layer of the neural network consists of N cells, where N is the number of intents (classes). To learn the parameters in the network (the weight on each link in the neural network), the system uses the stochastic gradient descent method" teaches that the machine learning model of the chatbot is trained (parameters updated) based on context of conversation logs (set of data) of previous users (e.g. second chatbot for second user is trained based on conversation logs of a first user with a first chatbot));
receiving a first query from a second user (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system" teaches that the bot management system receives a request (first query) from a remotes system (e.g. from a second user));
after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system. The bot management system analyzes 504 the text data or voice data in the request to determine an intent associated with the request. Based on the intent associated with the request, the bot management system generates 506 a response to the request. In some embodiments, the response generated 506 may also include declarative configuration information, or any other data, as discussed herein" teaches that the chatbot system (e.g. second chatbot) analyzes the request (first query) and generates a response based on the intent (conversation context) associated with the request. [0058]: "The follow-on/conversation intents are invoked based on the context of the conversation" teaches that the intents are invoked based on the context of the conversation. [0118]: "A prediction phase is part of the production pipeline. For each input message, the system first process it according to the steps defined in the text pre-processing steps to get its clean vector representation. The system then sends the word vectors into the LSTM-RNN model built from training. The model then gives a score between 0 and 1 to each label (possible intent). These scores (one per label) are normalized such that they sum to 1 and represent probabilities. The highest score is associated with the most likely intent, according to the model. The system outputs this intent and the score to the front-end of the system" teaches that the chatbot system (e.g. second chatbot) performs an intent prediction for an input message (processing first query) used for generating a response after training the machine learning model of the chatbot (e.g. second machine learning model of second chatbot) is trained (parameters updated) based on context of conversation logs (set of data) of previous users (first conversation between first user and first chatbot)); and
presenting the first response to the second user (Fig. 5; [0047]: "The bot management system then communicates 508 the response to the remote system" teaches that the bot management system communicates (presents) the response (first response) to the remote system of the user (e.g. second user)).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate updating one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model; receiving a first query from a second user; after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot; and presenting the first response to the second user as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Regarding Claim 20,
Moon et al. teaches a non-transitory computer-readable medium comprising instructions stored thereon that are executable by at least one processor to cause the at least one processor to perform operations ([0003]: "an apparatus includes a communications unit, a storage unit storing instructions, and at least one processor coupled to the communications unit and the storage unit. The at least one processor is configured to execute the instructions to receive, via the communications unit, a first signal from a device that includes messaging information, and determine a candidate parameter value for a first parameter of an exchange of data based on the messaging information and on information characterizing prior exchanges of data between the device and the apparatus" teaches an apparatus comprising a storage unit (memory) storing instructions and a processor configured to execute the instructions to perform operations. [0158]-[0159]: "Exemplary embodiments of the subject matter described in this specification … can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory program carrier for execution by, or to control the operation of, a data processing apparatus (or a computer system). … Additionally, or alternatively, the program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them" teaches a non-transitory computer storage medium storing instructions that are executable by the apparatus comprising a processor for performing operations) comprising:
receiving a set of data corresponding to a context of a first conversation between a first user and a first chat bot, the first chat bot comprising a first machine learning model (Fig. 3B; [0083]-[0085]: "predictive engine 146 may receive contextual information 328 … predictive engine 146 may also access transaction database 134, and obtain first transaction data 344A, which characterizes prior exchanges of data involving user 101, and second transaction data 344B, which characterizes prior exchanges of data involving the additional users associated with transaction system 130. Additionally, based on extracted user identifier 306 or extracted device identifier 308, predictive engine 146 may also access chatbot session database 136, and obtain first chatbot session data 346A, which characterizes prior chatbot sessions involving user 101, and second chatbot session data 346B, which characterizes prior chatbot sessions involving the additional users of transaction system 130" teaches the predictive engine of the transaction system receiving contextual information (data corresponding to a context) and first chatbot session data 346A (conversation between first user and first chatbot) for a user 101 (first user). Fig. 1; [0039]-[0040]: "Examples of these natural language processing algorithms may include one or more machine learning processes … the one or more natural language processing algorithms may also include one or more artificial intelligence models … in some examples, the functions of NLP engine 144 may be performed by chatbot engine 142 (e.g., NLP engine 144 is part or component of chatbot engine 142)" teaches that the chatbot engine 142 (first chatbot) comprises the NLP engine 144 comprising natural language processing algorithms including a machine learning model (e.g. the chatbot comprises a machine learning model)).
Moon et al. does not appear to explicitly teach updating one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model; receiving a first query from a second user; after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot; and presenting the first response to the second user.
However, Bobbarjung et al. teaches updating one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model (Fig. 1; [0030]: "The multiple users 114, 116, and 118 include any individuals or groups that interact with services 104-108, data source 112, and bot creation and management system 102. In some embodiments, one or more of the users 114-118 are communicating with one or more of the services 104-108 or bot creation and management system 102 using an intelligent conversational interface, chatbot, or voice assistant" teaches a bot creation and management system 102 for building chatbots for interactions with multiple users. [0104]-[0105]: "The systems and methods described herein need to be able to recognize correctly the customer's intent in order to give correct and intelligent responses. It is a foundational part of the chatbot system. Given any text input (or text converted from voice input), the system is able to correctly identify the intention behind this message. A machine learning system (also referred to as a machine learning model) handles this task … The machine learning system has two distinct parts: training and prediction" teaches that the chatbot system comprises a machine learning model (e.g. second chatbot comprises second machine learning model) that is trained (updated). [0115]-[0117]: "In the training phase, the systems and methods provide data (typically a large size of data) into a machine learning model and let the model “learn” to recognize predefined patterns. The machine “learns” through a mathematical optimization procedure. In an intent identification module, the system uses deep learning techniques … the training data comes from customer service logs or other applicable conversation logs. Each data point consists of the text content (what the customer was saying) and a ground-truth label (what is the true intent) … The output layer of the neural network consists of N cells, where N is the number of intents (classes). To learn the parameters in the network (the weight on each link in the neural network), the system uses the stochastic gradient descent method" teaches that the machine learning model of the chatbot is trained (parameters updated) based on context of conversation logs (set of data) of previous users (e.g. second chatbot for second user is trained based on conversation logs of a first user with a first chatbot));
receiving a first query from a second user (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system" teaches that the bot management system receives a request (first query) from a remotes system (e.g. from a second user));
after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot (Fig. 5; [0047]: "FIG. 5 is a flow diagram depicting an embodiment of a method 500 for responding to messages or requests received from a remote system. Initially, a bot management system receives 502 a request from a remote system. The bot management system analyzes 504 the text data or voice data in the request to determine an intent associated with the request. Based on the intent associated with the request, the bot management system generates 506 a response to the request. In some embodiments, the response generated 506 may also include declarative configuration information, or any other data, as discussed herein" teaches that the chatbot system (e.g. second chatbot) analyzes the request (first query) and generates a response based on the intent (conversation context) associated with the request. [0058]: "The follow-on/conversation intents are invoked based on the context of the conversation" teaches that the intents are invoked based on the context of the conversation. [0118]: "A prediction phase is part of the production pipeline. For each input message, the system first process it according to the steps defined in the text pre-processing steps to get its clean vector representation. The system then sends the word vectors into the LSTM-RNN model built from training. The model then gives a score between 0 and 1 to each label (possible intent). These scores (one per label) are normalized such that they sum to 1 and represent probabilities. The highest score is associated with the most likely intent, according to the model. The system outputs this intent and the score to the front-end of the system" teaches that the chatbot system (e.g. second chatbot) performs an intent prediction for an input message (processing first query) used for generating a response after training the machine learning model of the chatbot (e.g. second machine learning model of second chatbot) is trained (parameters updated) based on context of conversation logs (set of data) of previous users (first conversation between first user and first chatbot)); and
presenting the first response to the second user (Fig. 5; [0047]: "The bot management system then communicates 508 the response to the remote system" teaches that the bot management system communicates (presents) the response (first response) to the remote system of the user (e.g. second user)).
Moon et al. and Bobbarjung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate updating one or more parameters of a second chat bot based on the set of data, the second chat bot comprising a second machine learning model; receiving a first query from a second user; after updating the one or more parameters of the second chat bot, processing the first query with the second chat bot to generate a first response to the first query based on the context of the first conversation between the first user and the first chat bot; and presenting the first response to the second user as taught by Bobbarjung et al. to the disclosed invention of Moon et al.
One of ordinary skill in the art would have been motivated to make this modification to "improve the quality and quantity of chatbot skills available and may ultimately improve the chatbots built on the platform" (Bobbarjung et al. [0171]).
Claims 6-12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Moon et al. (US 2020/0112526 A1) in view of Bobbarjung et al. (US 2019/0124020 A1) and further in view of Reuss (US 2019/0036864 A1).
Regarding Claim 6,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
Moon et al. in view of Bobbarjung et al. does not appear to explicitly teach wherein the receiving of the set of data comprises: receiving, from the first client device of the first user, a message from the first user, the message comprising a link to the set of data; and in response to receiving a request from a second client device of the second user to adopt the context of the first conversation between the first user and the first chat bot, accessing the link to retrieve the set of data.
However, Reuss teaches wherein the receiving of the set of data comprises: receiving, from the first client device of the first user, a message from the first user, the message comprising a link to the set of data (Fig. 1; [0028]: "As suggested by FIG. 1, the third party 112 sends a digital content item from the third-party device 106 to the social networking system 102 to share with other users (e.g., users 122a and 122b). As used in this disclosure, the term “digital content item” refers to any digital image, photo, text, symbol, video, file, or any combination thereof capable of posting to or sharing through a social networking system. For example, a digital content item can include an image and accompanying text with a call-to-action button posted within a newsfeed for a user of the social networking system 102. As another example, a digital content item can include a video with an accompanying link posted within a newsfeed for a user of the social networking system 102" teaches that a third party (first user) uses a third-party device 106 (first client device) to send a message comprising a digital content item including a link to other users (e.g. second user). [0031]-[0033]: "When the client device 116a presents the digital content item within a newsfeed for the user 122a, for example, the client device 116a receives an indication of a selection by the user 122a of a selectable option associated with the digital content item for the third party 112. In some embodiments, this selection triggers the social networking system 102 to provide a messaging thread to the client device 116a … a messaging thread may include multiple digital messages sent and received exclusively by users of the social networking system 102. But a messaging thread may likewise include a single digital message sent exclusively from one user to another user of the social networking system 102. In some embodiments, both the third party 112 and a chatbot that represents the third party 112 have access to a messaging thread between the third party 112 and another user of the social networking system 102, such as the user 122a" teaches that the digital content item including a link includes a messaging thread (conversation) between users and a chatbot (set of data)); and
in response to receiving a request from a second client device of the second user to adopt the context of the first conversation between the first user and the first chat bot, accessing the link to retrieve the set of data (Fig. 1; [0031]-[0033]: "When the client device 116a presents the digital content item within a newsfeed for the user 122a, for example, the client device 116a receives an indication of a selection by the user 122a of a selectable option associated with the digital content item for the third party 112. In some embodiments, this selection triggers the social networking system 102 to provide a messaging thread to the client device 116a … a messaging thread may include multiple digital messages sent and received exclusively by users of the social networking system 102. But a messaging thread may likewise include a single digital message sent exclusively from one user to another user of the social networking system 102. In some embodiments, both the third party 112 and a chatbot that represents the third party 112 have access to a messaging thread between the third party 112 and another user of the social networking system 102, such as the user 122a" teaches that a client device 116a (second client device) of a user 112a (second user) can access a selectable option associated with the digital content item (e.g. a link) in order to retrieve a messaging thread (conversation) between users and a chatbot (set of data)).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the receiving of the set of data comprises: receiving, from the first client device of the first user, a message from the first user, the message comprising a link to the set of data; and in response to receiving a request from a second client device of the second user to adopt the context of the first conversation between the first user and the first chat bot, accessing the link to retrieve the set of data as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 7,
Moon et al. in view of Bobbarjung et al. and further in view of Reuss teaches the method of claim 6.
In addition, Reuss further teaches wherein the message is received as at least one of a text, video, or audio message, a post to a social network, a meta package as an ingredient to assimilate into another conversation factor, or content on a webpage (Fig. 1; [0028]: "As suggested by FIG. 1, the third party 112 sends a digital content item from the third-party device 106 to the social networking system 102 to share with other users (e.g., users 122a and 122b). As used in this disclosure, the term “digital content item” refers to any digital image, photo, text, symbol, video, file, or any combination thereof capable of posting to or sharing through a social networking system. For example, a digital content item can include an image and accompanying text with a call-to-action button posted within a newsfeed for a user of the social networking system 102. As another example, a digital content item can include a video with an accompanying link posted within a newsfeed for a user of the social networking system 102" teaches that the message comprises a digital content item that can be a text, video, social network post or link).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the message is received as at least one of a text, video, or audio message, a post to a social network, a meta package as an ingredient to assimilate into another conversation factor, or content on a webpage as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 8,
Moon et al. in view of Bobbarjung et al. and further in view of Reuss teaches the method of claim 6.
In addition, Reuss further teaches further comprising: in response to receiving the request to adopt the context, accessing location information of the second client device (Fig. 1; [0031]-[0034]: "When the client device 116a presents the digital content item within a newsfeed for the user 122a, for example, the client device 116a receives an indication of a selection by the user 122a of a selectable option associated with the digital content item for the third party 112. In some embodiments, this selection triggers the social networking system 102 to provide a messaging thread to the client device 116a … a messaging thread may include multiple digital messages sent and received exclusively by users of the social networking system 102. But a messaging thread may likewise include a single digital message sent exclusively from one user to another user of the social networking system 102. In some embodiments, both the third party 112 and a chatbot that represents the third party 112 have access to a messaging thread between the third party 112 and another user of the social networking system 102, such as the user 122a … In addition to being private, the provided messaging thread is shown to a verified user. The social networking system 102 verifies an identity of the user 122a sometime before providing the messaging thread. For example, in some embodiments, the social networking system 102 compares a username and password to a username and password associated with an account of the user 122a. Additionally, in certain embodiments, the social networking system 102 also compares a location of the client device 116a and a device identifier of the client device 116a to locations and device identifiers of one or more client devices tracked within a user history for the user 122a" teaches that a client device 116a (second client device) of a user 112a (second user) that receives a message for accessing a messaging thread (e.g. to adopt the context) will have its location information accessed);
accessing one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot (Fig. 1; [0054]: "Upon receipt of the digital content item for the third party 112, the social networking system 102 adds the digital content item to some (or all) newsfeeds for users of the social networking system 102. In some embodiments, for example, the social networking system 102 adds the digital content item as sponsored content to newsfeeds for users who satisfy certain demographic criteria, who have performed certain actions within the social networking system 102 (e.g., liking an organization's page, followed a particular user), or who are associated with a geographic location" teaches accessing an associated geographic location (geographical restriction) for sending the message with the digital content item from the third party 112 (first user));
determining that the location information of the second client device satisfies the one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot (Fig. 1; [0054]: "Upon receipt of the digital content item for the third party 112, the social networking system 102 adds the digital content item to some (or all) newsfeeds for users of the social networking system 102. In some embodiments, for example, the social networking system 102 adds the digital content item as sponsored content to newsfeeds for users who satisfy certain demographic criteria, who have performed certain actions within the social networking system 102 (e.g., liking an organization's page, followed a particular user), or who are associated with a geographic location" teaches determining the users whose location information is associated with the geographic location (geographic restriction) of the message with the digital content item from the third party 112 (first user) (i.e. determining if the location information for a second client device of a second user satisfies the geographic location requirement to receive the message with the digital content item)); and
controlling presentation of the message on the second client device based on determining that the location information of the second client device satisfies the one or more geographical restrictions (Fig. 1; [0054]: "Upon receipt of the digital content item for the third party 112, the social networking system 102 adds the digital content item to some (or all) newsfeeds for users of the social networking system 102. In some embodiments, for example, the social networking system 102 adds the digital content item as sponsored content to newsfeeds for users who satisfy certain demographic criteria, who have performed certain actions within the social networking system 102 (e.g., liking an organization's page, followed a particular user), or who are associated with a geographic location" teaches that the message with the digital content item from the third party 112 (first user) is sent to all users associated with a specific geographic location (e.g. satisfies a geographical restriction) (i.e. the message is sent to a second client device of a second user that is within a geographic location)).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising: in response to receiving the request to adopt the context, accessing location information of the second client device; accessing one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot; determining that the location information of the second client device satisfies the one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot; and controlling presentation of the message on the second client device based on determining that the location information of the second client device satisfies the one or more geographical restrictions as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 9,
Moon et al. in view of Bobbarjung et al. and further in view of Reuss teaches the method of claim 6.
In addition, Reuss further teaches wherein the message is displayed to client devices that are within a threshold distance of one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot (Fig. 1; [0054]: "Upon receipt of the digital content item for the third party 112, the social networking system 102 adds the digital content item to some (or all) newsfeeds for users of the social networking system 102. In some embodiments, for example, the social networking system 102 adds the digital content item as sponsored content to newsfeeds for users who satisfy certain demographic criteria, who have performed certain actions within the social networking system 102 (e.g., liking an organization's page, followed a particular user), or who are associated with a geographic location" teaches that the message with the digital content item from the third party 112 (first user) is sent to all users associated with a specific geographic location (e.g. within a threshold distance of a geographical restriction)).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the message is displayed to client devices that are within a threshold distance of one or more geographical restrictions associated with adopting the context of the first conversation between the first user and the first chat bot as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 10,
Moon et al. in view of Bobbarjung et al. and further in view of Reuss teaches the method of claim 6.
In addition, Reuss further teaches further comprising: transmitting a notification to the first client device of the first user in response to receiving the request from the second client device, the notification informing the first user that the context of the first conversation has been adopted by one or more other users (Fig. 1; [0077]-[0081]: "in some embodiments, the social networking system 102 verifies an identity of the user 122a at any point before adding a digital signature to the digital document. For example, in some embodiments, the social networking system 102 verifies an identity of the user 122a when the client device 116a launches the social networking application 118a or the messaging application 120a … In addition to verifying an identity of users, in some embodiments, the social networking system 102 also adds a digital signature to a digital document. As further shown in FIG. 2B, the social networking system 102 performs the act 232 of adding the digital signature to the digital document. In some embodiments, the social networking system 102 adds the digital signature based on receiving an indication of a selection of the add-signature option and verifying the identity of the user 122a … in certain instances, the social networking system 102 sends a notice to the third-party device 106 that the digital signature has been added to the digital document stored by the social networking system 102" teaches that the system verifies the identity of the user 112a (second user) that the digital content item, including a link includes a messaging thread (conversation) between users and a chatbot, is sent to by the third party 112 (first user) for adoption, and sends a notice (notification) to the third-party device 106 (first client device) of the third party once a digital signature verifying the identity of the user 112a has been added (e.g. once the user has been verified and the digital content item has been adopted)).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising: transmitting a notification to the first client device of the first user in response to receiving the request from the second client device, the notification informing the first user that the context of the first conversation has been adopted by one or more other users as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 11,
Moon et al. in view of Bobbarjung et al. and further in view of Reuss teaches the method of claim 10.
In addition, Reuss further teaches wherein the notification identifies the second user to the first user (Fig. 1; [0077]-[0081]: "in some embodiments, the social networking system 102 verifies an identity of the user 122a at any point before adding a digital signature to the digital document. For example, in some embodiments, the social networking system 102 verifies an identity of the user 122a when the client device 116a launches the social networking application 118a or the messaging application 120a … In addition to verifying an identity of users, in some embodiments, the social networking system 102 also adds a digital signature to a digital document. As further shown in FIG. 2B, the social networking system 102 performs the act 232 of adding the digital signature to the digital document. In some embodiments, the social networking system 102 adds the digital signature based on receiving an indication of a selection of the add-signature option and verifying the identity of the user 122a … in certain instances, the social networking system 102 sends a notice to the third-party device 106 that the digital signature has been added to the digital document stored by the social networking system 102" teaches that the notice (notification) identifies the signature of the user 112a (second user) to the third party (first user) (e.g. notification identifies second user to first user)).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the notification identifies the second user to the first user as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 12,
Moon et al. in view of Bobbarjung et al. and further in view of Reuss teaches the method of claim 10.
In addition, Reuss further teaches wherein the one or more other users remain anonymous to the first user (Fig. 7; [0196]: "A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by social networking system 702 or shared with other systems (e.g., third-party system 708), such as, for example, by setting appropriate privacy settings. Third-party-content-object stores may be used to store content objects received from third parties, such as a third-party system 708" teaches that the other users may set privacy settings to prevent their actions from being logged or shared with the third party (first user) (e.g. other users may remain anonymous to the first user)).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the one or more other users remain anonymous to the first user as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Regarding Claim 17,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 16.
Moon et al. in view of Bobbarjung et al. does not appear to explicitly teach further comprising: receiving a second input from the first user to share the identified context of the first conversation with one or more other users through at least one of an encrypted message or a non-fungible token (NFT).
However, Reuss teaches further comprising: receiving a second input from the first user to share the identified context of the first conversation with one or more other users through at least one of an encrypted message or a non-fungible token (NFT) (Fig. 1; [0028]-[0031]: " As suggested by FIG. 1, the third party 112 sends a digital content item from the third-party device 106 to the social networking system 102 to share with other users (e.g., users 122a and 122b). As used in this disclosure, the term “digital content item” refers to any digital image, photo, text, symbol, video, file, or any combination thereof capable of posting to or sharing through a social networking system. For example, a digital content item can include an image and accompanying text with a call-to-action button posted within a newsfeed for a user of the social networking system 102 … When the client device 116a presents the digital content item within a newsfeed for the user 122a, for example, the client device 116a receives an indication of a selection by the user 122a of a selectable option associated with the digital content item for the third party 112. In some embodiments, this selection triggers the social networking system 102 to provide a messaging thread to the client device 116a. The provided messaging thread is private and between the third party 112 and the user 122a. In some embodiments, the social networking system 102 digitally encrypts the messages sent and received over the network 114 as part of the messaging thread" teaches that the third party (first user) shares the message comprising a digital content item including a messaging thread (conversation) between users and a chatbot with other users, the message being digitally encrypted).
Moon et al., Bobbarjung et al., and Reuss are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising: receiving a second input from the first user to share the identified context of the first conversation with one or more other users through at least one of an encrypted message or a non-fungible token (NFT) as taught by Reuss to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to "allow users to securely exchange sensitive information away from the more visible digital forums of some social networking systems" (Reuss [0026]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Moon et al. (US 2020/0112526 A1) in view of Bobbarjung et al. (US 2019/0124020 A1) and further in view of Fung et al. (US 2018/0083894 A1).
Regarding Claim 15,
Moon et al. in view of Bobbarjung et al. teaches the method of claim 1.
Moon et al. in view of Bobbarjung et al. does not appear to explicitly teach wherein the first query comprises one or more unstructured natural language words or phrases or graphical representations such as emojis; and wherein the first response comprises one or more unstructured responses.
However, Fung et al. teaches wherein the first query comprises one or more unstructured natural language words or phrases or graphical representations such as emojis ([0052]: "As another example, if a user in a conversation is determined (based on the present and/or past conversations) to be a heavy user of emojis, then a bot may also interact with that user using one or more emojis" teaches that the user inputs (first query) to the conversation may comprise emojis); and
wherein the first response comprises one or more unstructured responses ([0052]: "As another example, if a user in a conversation is determined (based on the present and/or past conversations) to be a heavy user of emojis, then a bot may also interact with that user using one or more emojis" teaches that the responses from the chatbot may comprise emojis (unstructured responses)).
Moon et al., Bobbarjung et al., and Fung et al. are analogous to the claimed invention because they are directed towards machine learning chatbots.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the first query comprises one or more unstructured natural language words or phrases or graphical representations such as emojis; and wherein the first response comprises one or more unstructured responses as taught by Fung et al. to the disclosed invention of Moon et al. in view of Bobbarjung et al.
One of ordinary skill in the art would have been motivated to make this modification to allow "the content and/or style of the bot's interactions [to] dynamically vary based on one or more of: the content of the conversation determined using natural language processing, the identities of the users in the conversations, and one or more conversational contexts" (Fung et al. [0051]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
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/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125