DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 13 May 2026 has been entered.
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 .
Response to Amendment
This action is in response to the submission filed 13 May 2026 for application 16/585,221. Currently claims 2, 3, 5, and 7 are canceled. Claims 1, 4, 6, 8, 9, and 17 are amended. Claims 21-24 are newly added. Claims 1, 4, 6, and 8-24 are pending and have been examined.
The 112(b) rejection on claims 17-20 has been withdrawn in view of the amendments made to independent claim 17.
Response to Arguments
Regarding applicant’s arguments, filed 13 May 2026, see pages 11-14, with respect to claim rejections under 35 USC 101, Applicant specifically argues on Page 14 (Paragraph 2) that independent claims 1, 9 and 17, as amended herein, recite patentable subject matter that provides a technical solution to a technical problem and is not abstract. As such, Assignee's representative respectfully requests that the rejection of independent claims 1, 9 and 17 (and dependent claims 4, 6 and 10-16, 18-24) be withdrawn and all pending claims be allowed.
Examiners response: Applicant's arguments, have been fully considered but they are not persuasive. Examiner disagrees that the rejection can be withdrawn because firstly although Applicant argues that the claims provide a technical solution to a technical problem Applicant has failed to failed to clearly provide an explanation of what the technical problem is and what the technical solution to it is the arguments. Secondly, even if there is a technical problem that the instant case is trying to provide a solution for as shown the detailed rejection below the claim limitations were either identified as abstract ideas or were identified as additional elements which are recited so generically or were merely gathering data or outputting data that it did not integrate the abstract idea into a practical application. Lastly, although Applicant argues that the claims are not abstract as explained below and also shown in the detailed rejection below some limitations are identified as abstract and are explained why. Hence, the rejection cannot be withdrawn.
Regarding applicant’s arguments, filed 13 May 2026, see pages 11-14, with respect to claim rejections under 35 USC 101, Applicant specifically argues on Page 14 (Paragraph 3) that the claimed invention utilizes electronic, wireless and complex processing and aggregation of data feeds from internet of things (IOT) devices, training of machine learning algorithms (e.g., bidirectional long-short term models) to determine (and employs feedback to update) relationships between users and computing devices in the environment and employs natural language machine learning algorithms to determine types of questions for training the bidirectional long-short term models and to determine whether a user is in an environment and available to receive messages from the computing device in the environment. The steps are performed with no human interaction and is a complex machine-learning based solution for providing a virtual assistance via devices to a user in an environment. The steps cannot be performed in the mind of a human and are not abstract.
Examiners response: Applicant's arguments, have been fully considered but they are not persuasive. Examiner disagrees that the rejection can be withdrawn because firstly although Applicant argues that the claims are not abstract, Applicant fails to provide any substantial reasons or explanation as to why the limitations that are identified as abstract are not directed to an abstract idea. Secondly, in the detailed rejection below for example in claim 1, only the limitations of “identifying, an interaction of a user with a computing device; aggregating, in a corpus, the audio and physical environmental sensed data, the environmental constraints, usage statistics such as statistics of commands from the user, and environmental statistics such as the context of an environment that the user is located within preceding and after utilizing an interface of the computing device” are identified as abstract because a person can easily identify an interaction of a user with a computing device by observing and can aggregate data, constraints, statistics, and context by evaluating and both of these are mental processes and therefore fall into the mental process grouping of the abstract idea. Lastly, the limitations of training of machine learning algorithms (e.g., bidirectional long-short term models) and employing natural language machine learning algorithms to determine something are identified as additional elements and not abstract ideas. Furthermore, since the details of the actual training steps are not positively recited in the claims they are like black box with no details but merely being applied to the judicial exceptions to achieve an end result. Hence, the rejection cannot be withdrawn.
Regarding applicant’s arguments, filed 13 May 2026, see pages 14-16, with respect to claim rejections under 35 USC 101, Applicant specifically argues on Page 14 (Last but one Paragraph) that further, the claimed invention addresses reduction in operations of a virtual assistant server by offloading tasks to local IOT devices and the computing device thereby increasing processing resources of the server and network resources of the server and on Page 15 (last Paragraph) that further, out of an abundance of caution, Assignee's representative submits that there is no support in the MPEP that any explicit recitation of improvement in the claim is required to overcome the rejection under 35 USC 101. Further, there are no cases in which the courts have ever said that the technical improvement to the technical problem must be recited in the claim itself, and thus, Assignee's representative respectfully submits in advance that such is not required.
Furthermore, Applicant argues on Page 16 (Paragraph 1) that the technical solution to the problem is recited in the independent claim 1 and, as such, Assignee's representative respectfully requests that the rejection of claim 1 be withdrawn and request that claim 1 (and claims 2-7, which depend from claim 1) be allowed.
Lastly, Applicant argues on Page 16 (Paragraph 2) that based at least on similar reasons provided for claim 1 (and claims 2-7), claims 8-25 also recite patentable subject matter and Assignee's representative respectfully requests that the rejection be withdrawn.
Examiners response: Applicant's arguments, have been fully considered but they are not persuasive. Examiner disagrees that the rejection can be withdrawn because firstly although Applicant argues that the claimed invention addresses reduction in operations of a virtual assistant server it is not completely clear as to whether that is the technical problem that it is trying to have a solution for. Even if it is, as stated in MPEP 2106.04(d)(1), first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). If the instant case is improving the technology it is still unclear as to what that exact technology is? Is the technology a specific type of machine learning? But if so, there are no details of that actual technology recited at all. Specifically, the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology. Hence, the rejection is maintained.
Lastly, based at least on similar reasons provided for independent claim 1, independent claims 9 and 17, and all of their dependent claims are rejected.
Regarding Applicant’s arguments, filed 13 May 2026, see pages 16 and 17, with respect to claim rejections under 35 USC 103, Applicant argues that the rejection should be withdrawn and claims be allowed.
Examiners response: Applicant's arguments, have been fully considered but they are not persuasive. Examiner disagrees that the claims be allowed because firstly, as shown in the detailed rejection below the cited references teach each and every element of the claimed invention. Secondly, Applicant did not provide any reasons or explanations as to why the cited references do not teach the claims. Thirdly, it is very unclear from the arguments as to which exact limitations the Applicant is arguing about. Lastly, Applicant’s arguments, with respect to the newly amended features as recited in independent claim 1 (and similarly in independent claims 9 and 17) have been considered but are moot because the new ground of rejection citing the new combination of references for teaching the new amendments does not rely on any reference combination applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Hence, the rejection is maintained.
Claim Objections
Claim 17 is objected to because of the following informalities:
The last limitation of “and program instructions to generate and transmit from the computer system to the user via a telecommunication network, an electronic notification message for the user based at least in part on the knowledge base” of claim 17 on Page 8 from the claim set dated 7/30/2025 is missing from claim 17 of both claim sets dated 12/31/2025 and the latest claim set of 5/13/2026. Appropriate correction is required.
For the purposes of examination that limitation will be treated as though it has been deleted from the claim via a strikethrough.
Claim 22 is objected to because of the following informalities: The phrase “The computer implemented of claim 21…” is awkwardly worded. For the purposes of examination the phrase is interpreted as “The computer implemented method of claim 21…”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 6, and 8-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Regarding claim 1:
According to the first step (Step 1) of the 101 analysis, claim 1 is directed to a computer-implemented method of a machine learning based voice-response system (process) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
In the next step (Step 2A, prong 1) of the analysis, the limitations of:
identifying, an interaction of a user with a computing device;
aggregating, in a corpus, the audio and physical environmental sensed data, the environmental constraints, usage statistics such as statistics of commands from the user, and environmental statistics such as the context of an environment that the user is located within preceding and after utilizing an interface of the computing device.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitations:
the machine learning based voice-response system operatively coupled to a plurality of processors and comprising a plurality of interconnected Internet of Things (IOT) devices,
by the machine learning based voice-response system,
training, by the machine learning based voice-response system, a machine learning model comprising bi-directional long short-term memory to detect patterns in information in the usage statistics and the environmental statistics comprising the context of the environment and stored in the corpus, wherein the training is to determine a relationship between the user and the computing device by determining correlations between commands of the user to one or more of the plurality of interconnected IOT devices and the context;
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a computer-implemented method using the machine learning based voice-response system operatively coupled to a plurality of processors and comprising a plurality of interconnected Internet of Things (IOT) devices, by the machine learning based voice-response system, training, by the machine learning based voice-response system, a machine learning model comprising bi-directional long short-term memory to detect patterns in information in the usage statistics and the environmental statistics comprising the context of the environment and stored in the corpus, wherein the training is to determine a relationship between the user and the computing device by determining correlations between commands of the user to one or more of the plurality of interconnected IOT devices and the context) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the same step (Step 2A, prong 2) of the analysis, the limitation:
collecting, by the plurality of interconnected devices of the machine learning based voice-response system, audio and physical environmental sensed data regarding an operating environment in which the user is located and wherein the physical environment sensed data is also associated with environmental constraints and is derived from and comprises integrated Internet of Things (IOT) feeds, camera feeds and environment data feeds, is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity.
In the same step (Step 2A, prong 2) of the analysis, the limitation:
and outputting, by the machine learning based voice-response system, by the machine learning based voice response system, a notification message to the user based on the context and the corpus, wherein the outputting utilizes natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output the notification message to provide to the user from the computing device, is considered to be an additional element and as recited represents insignificant extra-solution activity that is data output, because it is a mere nominal or tangential addition to the claim and is therefore not indicative of integration into a practical application. See MPEP 2106.05(g).
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method using the machine learning based voice-response system operatively coupled to a plurality of processors and comprising a plurality of interconnected Internet of Things (IOT) devices, by the machine learning based voice-response system, training, by the machine learning based voice-response system, a machine learning model comprising bi-directional long short-term memory to detect patterns in information in the usage statistics and the environmental statistics comprising the context of the environment and stored in the corpus, wherein the training is to determine a relationship between the user and the computing device by determining correlations between commands of the user to one or more of the plurality of interconnected IOT devices and the context, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
In the same step (Step 2B) of the analysis, as discussed above, the additional element of collecting, by the plurality of interconnected devices of the machine learning based voice-response system, audio and physical environmental sensed data regarding an operating environment in which the user is located and wherein the physical environment sensed data is also associated with environmental constraints and is derived from and comprises integrated Internet of Things (IOT) feeds, camera feeds and environment data feeds, is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more.
In the same step (Step 2B) of the analysis, as discussed above, the additional element of outputting, by the machine learning based voice-response system, by the machine learning based voice response system, a notification message to the user based on the context and the corpus, wherein the outputting utilizes natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output the notification message to provide to the user from the computing device, amounts to insignificant extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 4:
In Step 2A, prong 1 of the analysis, the limitation of:
identifying, a reaction of the user to the context of the environment, wherein the reaction is determined based on the commands from the user in view of the environment;
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the same Step 2A, prong 1 of the analysis, the limitations of:
and updating, by the machine learning based voice-response system, a reward function of a reinforcement learning model of the corpus based on whether the machine learning based voice-response system determines that the reaction of the user denotes a negative connotation or response thereby resulting in the reinforcement learning model reducing the reward function, under the broadest reasonable interpretation, the limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, by the machine learning based voice-response system, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a computer-implemented method using the machine learning based voice-response system) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer-implemented method using a system is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6:
In Step 2A, prong 1 of the analysis, the limitation of:
based on the updating the reward function by the reinforcement learning model by reducing the reward function:
determining, a different relationship between the user and the computing device;
or determining, a different combination of conditions of the context based on the reaction of the user;
and updating the corpus with the different relationship or the different combination of conditions of the context.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation,
by the machine learning based voice-response system
is considered to be an additional elements and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a computer-implemented method using the machine learning based voice-response system) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer-implemented method using the machine learning based voice-response system is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8:
In Step 2A, prong 1 of the analysis, the limitation of:
wherein the determined relationship includes an influence of the first set of conditions on the user that is correlated to inducing an interaction between the user and the computing device.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible.
Regarding claim 9:
According to the first step (Step 1) of the 101 analysis, claim 1 is directed to a computer program product (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
In the next step (Step 2A, prong 1) of the analysis, the limitations of:
identify an interaction of a user with a computing device;
determine, a first set of conditions of an operating environment that includes the interaction of the user with the computing device and data corresponding to the operating environment of the computing device from one or more interconnected devices;
generate, a knowledge base that includes the determined relationship, the first set of conditions of the operating environment, and the interaction of the user with the computing device;
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitations:
a computer program product comprising:
one or more non-transitory computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to train and apply a reinforcement learning process to determine a context of the interaction between the user and the computing device;
program instructions to train and apply a machine learning model, employing a bidirectional long-short term model, to determine a relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device by applying the machine learning model to commands of the user and environmental statistics to determine one or more patterns in usage and environmental statistics correlations between commands of the user to a smart speaker and the context, wherein the context is the environmental statistics, wherein the environmental statistics are determined via integrations with Internet of Things feeds, camera feeds, and environment data comprising weather;
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a computer program product comprising: one or more non-transitory computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: Program instructions to train and apply a reinforcement learning process to determine a context of the interaction between the user and the computing device and program instructions to train and apply a machine learning model employing a bidirectional long-short term model, to determine a relationship and patterns) that it represents no more than mere instructions to apply the judicial exception on a computer. Although training and applying a reinforcement learning process is recited to achieve a determination of the context, it is recited so generally without reciting any detailed steps of the actual training that the training is like a mere black box. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the same step (Step 2A, prong 2) of the analysis, the limitation:
and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing the bidirectional long-short term model, is considered to be an additional element and as recited represents insignificant extra-solution activity that is data output, because it is a mere nominal or tangential addition to the claim and is therefore not indicative of integration into a practical application. See MPEP 2106.05(g).
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer program product comprising: one or more non-transitory computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: Program instructions to train and apply a reinforcement learning process to determine a context of the interaction between the user and the computing device and program instructions to train and apply a machine learning model employing a bidirectional long-short term model, to determine a relationship and patterns is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
In the same step (Step 2B) of the analysis, as discussed above, the additional element of and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing the bidirectional long-short term model, amounts to insignificant extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 10:
In Step 2A, prong 1 of the analysis, the limitations of:
identify, a second set of conditions in the operating environment that includes the interaction of the user with the computing device;
determine, that the second set of conditions in the operating environment matches the determined first set of conditions of the operating environment included in the knowledge base.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, the computer program product of claim 9, further comprising program instructions, stored on the one or more computer readable storage media and perform, a defined action based at least in part on the user interaction of the knowledge base, are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the computer program product of claim 9, further comprising program instructions, stored on the one or more computer readable storage media and performs, a defined action based at least in part on the user interaction of the knowledge base) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer program product of claim 9, further comprising program instructions, stored on the one or more computer readable storage media and perform a defined action based at least in part on the user interaction of the knowledge base is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11:
In Step 2A, prong 1 of the analysis, the limitations of:
determine whether a count of a number of occurrences of the determined relationship exceeds a defined threshold of occurrences over a defined timeframe;
and in response to determining that the count of the number of occurrences of the determined relationship exceeds the defined threshold of occurrences over a defined timeframe, modify the knowledge base based on the number of occurrences of the determined relationship.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation the computer program product of claim 9, further comprising program instructions, stored on the one or more computer readable storage media, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than the computer program product of claim 9, further comprising program instructions, stored on the one or more computer readable storage media) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer program product of claim 9, further comprising program instructions, stored on the one or more computer readable storage media is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 12:
In Step 2A, prong 1 of the analysis, the limitation of:
detect a reaction of the user to performing the defined action, wherein the reaction of the user is selected from a group consisting of: affirmative actions and negation actions,
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the same Step 2A, prong 1 of the analysis, the limitation of: and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user, under the broadest reasonable interpretation, the limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, the computer program product of claim 10, further comprising program instructions, stored on the one or more computer readable storage media, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the computer program product of claim 10, further comprising program instructions, stored on the one or more computer readable storage media) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer program product of claim 10 further comprising program instructions, stored on the one or more computer readable storage media is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 13:
In Step 2A, prong 1 of the analysis, the limitation of:
aggregate, the collected data that includes conditions of the operating environment, wherein the collected data corresponds to a defined time period that includes events prior to and subsequent the user interaction with the computing device.
Under the broadest reasonable interpretation, the above limitation is a step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, the computer program product of claim 9, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is the computer program product of claim 9) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method using a processor is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
Regarding claim 14:
In Step 2A, prong 1 of the analysis, the limitation of:
select an output state of the reinforcement learning model, wherein the output state includes a determined relationship with a maximum reward value.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, the computer program product of claim 9, wherein program instructions to determine the relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device, further comprise program instructions to and input the first set of conditions of the operating environment for the interaction of the user with the computing device into a reinforcement learning model, are considered to be additional elements and they do not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the computer program product of claim 9, wherein program instructions to determine the relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device, further comprise program instructions to and inputs the first set of conditions of the operating environment for the interaction of the user with the computing device into a reinforcement learning model) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer program product of claim 9, wherein program instructions to determine the relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device, further comprise program instructions to and inputting the first set of conditions of the operating environment for the interaction of the user with the computing device into a reinforcement learning model is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 15:
In Step 2A, prong 1 of the analysis, the limitation of:
identify an action of the user in the knowledge base, wherein the action corresponds to a determined relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, The computer program product of claim 10, wherein program instructions to perform the defined action based at least in part on the user interaction of the knowledge base, further comprise program instructions to and perform the identified action, wherein performance of the identified action is selected from a group consisting of: previously performed actions of a user and providing a performance confirmation request, are considered to be additional elements and they do not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the computer program product of claim 10, wherein program instructions to perform the defined action based at least in part on the user interaction of the knowledge base, further comprise program instructions to and perform the identified action, wherein performance of the identified action is selected from a group consisting of: previously performed actions of a user and providing a performance confirmation request) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer program product of claim 10, wherein program instructions to perform the defined action based at least in part on the user interaction of the knowledge base, further comprise program instructions to and perform the identified action, wherein performance of the identified action is selected from a group consisting of: previously performed actions of a user and providing a performance confirmation request is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 16:
In Step 2A, prong 1 of the analysis, the limitation of:
wherein the determined relationship includes an influence of the first set of conditions on the user that is correlated to inducing an interaction between the user and the computing device.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis it does not integrate into a practical application because it does not add any additional elements that integrate the abstract idea into practical application.
In the last step (Step 2B) of the analysis, it does not add any additional elements that amount to significantly more than the abstract idea and thus fails to add an inventive concept. The claim is not patent eligible
Regarding claim 17:
According to the first step (Step 1) of the 101 analysis, claim 1 is directed to a computer system (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
In the next step (Step 2A, prong 1) of the analysis, the limitations of:
identify, an interaction of the user with the IOT computing device;
determine, a first set of conditions of an operating environment that includes the interaction of the user with the IOT computing device;
determine, a relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device;
generate, a knowledge base that includes the determined relationship, the first set of conditions of the operating environment, and the interaction of the user with the IOT computing device;
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitations of:
A computer system comprising:
one or more computer processors;
one or more Internet of Things (IOT) computing devices having sensors and coupled to the one or more computer processors;
one or more computer readable storage media;
and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising:
program instructions to initiate data sensing via a sensor of an IOT computing device of the one or more IOT computing devices;
and generate, an electronic notification message for the user based at least in part on the knowledge base.
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a computer system comprising: one or more computer processors; one or more Internet of Things (IOT) computing devices having sensors and coupled to the one or more computer processors; one or more computer readable storage media; and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to initiate data sensing via a sensor of an IOT computing device of the one or more IOT computing devices; and generate, an electronic notification message for the user based at least in part on the knowledge base) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the same step (Step 2A, prong 2), the limitation of:
and transmit from the computer system to the user via a telecommunication network;
is considered to be an additional element and as recited represents insignificant extra-solution activity that is merely transmitting data, because it is a mere nominal or tangential addition to the claim and is therefore not indicative of integration into a practical application. See MPEP 2106.05(g).
In the same step (Step 2A, prong 2), the limitation of:
program instructions to collect audio and physical environmental sensed data regarding an operating environment in which the user is located and wherein the physical environmental sensed data is derived from integrated IOT feeds from the IOT computing device, camera feeds and environment data comprising weather data;
is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity.
In the same step (Step 2A, prong 2) of the analysis, the limitation:
and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing a machine learning model comprising a bidirectional long-short term model that determines the relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device, is considered to be an additional element and as recited represents insignificant extra-solution activity that is data output, because it is a mere nominal or tangential addition to the claim and is therefore not indicative of integration into a practical application. See MPEP 2106.05(g).
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, a computer system comprising: one or more computer processors; one or more computer readable storage media; and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to perform some steps is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
In the same step (Step 2B), the limitations of:
transmit from the computer system to the user via a telecommunication network
amounts to insignificant extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data transmission (see MPEP 2106.05(d)). The courts have similarly found limitations directed to receiving or transmitting data over a network, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "Receiving or transmitting data over a network."). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.
In the same step (Step 2B), as discussed above the additional element of: program instructions to collect audio and physical environmental sensed data regarding an operating environment in which the user is located and wherein the physical environmental sensed data is derived from integrated IOT feeds from the IOT computing device, camera feeds and environment data comprising weather data, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more.
In the same step (Step 2B) of the analysis, as discussed above, the additional element of and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing a machine learning model comprising a bidirectional long-short term model that determines the relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device, amounts to insignificant extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 18:
In Step 2A, prong 1 of the analysis, the limitations of:
identify a second set of conditions in the operating environment that includes the interaction of the user with the camera having the sensor that records a video or photographs an image of the visual aspect of the operating environment;
determine that the second set of conditions in the operating environment matches the determined first set of conditions of the operating environment included in the knowledge base.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation:
the computer system further comprising a camera having a sensor that records a video or photographs an image of a visual aspect of the operating environment and that is coupled to the one or more processors, and the computer system further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, to:
and perform a defined action based at least in part on the user interaction of the knowledge base,
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that the computer system further comprising a camera having a sensor that records a video or photographs an image of a visual aspect of the operating environment and that is coupled to the one or more processors, and the computer system further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, to: perform a defined action based at least in part on the user interaction of the knowledge base) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer system further comprising a camera having a sensor that records a video or photographs an image of a visual aspect of the operating environment and that is coupled to the one or more processors, and the computer system further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, to:
and perform a defined action based at least in part on the user interaction of the knowledge base, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 19:
In Step 2A, prong 1 of the analysis, the limitations of:
determine whether a count of a number of occurrences of the determined relationship exceeds a defined threshold of occurrences over a defined timeframe;
and in response to determining that the count of the number of occurrences of the determined relationship exceeds the defined threshold of occurrences, over a defined timeframe, modify the knowledge base based on the number of occurrences of the determined relationship.
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation the computer system of claim 17, further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than the computer system of claim 17, further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer system of claim 17, further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 20:
In Step 2A, prong 1 of the analysis, the limitation of:
detect a reaction of the user to performing the defined action, wherein the reaction of the user is selected from a group consisting of: affirmative actions and negation actions,
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the same Step 2A, prong 1 of the analysis, the limitation of: and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user, under the broadest reasonable interpretation, the limitation is a process step that recites mathematical relationships and calculations but for the recitation of generic computer components. If a claim, under its broadest reasonable interpretation covers mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitation, the computer system of claim 18, further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the computer system of claim 18, further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the computer system of claim 18, further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 21:
In Step 2A, prong 1 of the analysis, the limitation of:
adding, to the corpus, an importance level of the context and a reaction of the user to the context, wherein the context is an environmental parameter, the reaction is a voice command and an importance level is a captured audio phrase;
performing, processing to update data of the corpus to determine another relationship between the context and the interaction of the user.
Under the broadest reasonable interpretation, the above limitation is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitations,
by the machine learning based voice-response system,
by the machine learning based voice-response system, machine learning processing;
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the machine learning based voice-response system, by the machine learning based voice-response system, machine learning processing) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the machine learning based voice-response system, by the machine learning based voice-response system, machine learning processing is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 22:
In the step (Step 2A, prong 2) of the analysis, the limitation,
wherein the environmental parameter comprises one or more indicators that provides information about or describes the state of the environment such as the operating environment of the computing device.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a computer implemented method wherein the environmental parameter comprises one or more indicators that provides information about or describes the state of the environment such as the operating environment of the computing device) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, a computer implemented method wherein the environmental parameter comprises one or more indicators that provides information about or describes the state of the environment such as the operating environment of the computing device, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 23:
In the step (Step 2A, prong 2) of the analysis, the limitation,
training, by the machine learning based voice-response system, the bidirectional long- short term memory model employing historical answers of the user to pre-defined questions for the one or more IOT devices to determine types of the questions to generate for the one or more IOT devices and to determine the type of notification message to output to the user;
and performing, by the machine learning based voice-response system, NLP techniques such as natural language understanding to process verbal responses of the user to questions of the type determined by the training of the bidirectional long-short term memory.
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a computer implemented method further comprising training, by the machine learning based voice-response system, the bidirectional long- short term memory model employing historical answers of the user to pre-defined questions for the one or more IOT devices to determine types of the questions to generate for the one or more IOT devices and to determine the type of notification message to output to the user; and performing, by the machine learning based voice-response system, NLP techniques such as natural language understanding to process verbal responses of the user to questions of the type determined by the training of the bidirectional long-short term memory) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, a computer implemented method further comprising training, by the machine learning based voice-response system, the bidirectional long- short term memory model employing historical answers of the user to pre-defined questions for the one or more IOT devices to determine types of the questions to generate for the one or more IOT devices and to determine the type of notification message to output to the user; and performing, by the machine learning based voice-response system, NLP techniques such as natural language understanding to process verbal responses of the user to questions of the type determined by the training of the bidirectional long-short term memory, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 24:
In the step (Step 2A, prong 2) of the analysis, the limitation,
aggregating, by the machine learning based voice-response system, data of the one or more IOT devices that is a camera to identify that the user is present in an environment and employing, by the system, data of another one of the one or more IOT devices to identify that the user is not currently engaged in any activity with another IOT device and is therefore available to receive a question of the types of questions generated by the bidirectional long-short term memory model;
and training, by the machine learning based voice-response system, the bidirectional long- short term memory model, to identify an appropriate time frame to transmit a question to the user through the computing device using data of the IOT device feeds or camera feeds based on the aggregating the data of the one or more IOT devices that is the camera and the employing the data of another one of the IOT devices to identify that the user is available to receive the question.
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional elements are recited so generically (no details whatsoever are provided other than that it is a computer implemented method further comprising aggregating, by the machine learning based voice-response system, data of the one or more IOT devices that is a camera to identify that the user is present in an environment and employing, by the system, data of another one of the one or more IOT devices to identify that the user is not currently engaged in any activity with another IOT device and is therefore available to receive a question of the types of questions generated by the bidirectional long-short term memory model; and training, by the machine learning based voice-response system, the bidirectional long- short term memory model, to identify an appropriate time frame to transmit a question to the user through the computing device using data of the IOT device feeds or camera feeds based on the aggregating the data of the one or more IOT devices that is the camera and the employing the data of another one of the IOT devices to identify that the user is available to receive the question) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional elements do not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, a computer implemented method further comprising aggregating, by the machine learning based voice-response system, data of the one or more IOT devices that is a camera to identify that the user is present in an environment and employing, by the system, data of another one of the one or more IOT devices to identify that the user is not currently engaged in any activity with another IOT device and is therefore available to receive a question of the types of questions generated by the bidirectional long-short term memory model; and training, by the machine learning based voice-response system, the bidirectional long- short term memory model, to identify an appropriate time frame to transmit a question to the user through the computing device using data of the IOT device feeds or camera feeds based on the aggregating the data of the one or more IOT devices that is the camera and the employing the data of another one of the IOT devices to identify that the user is available to receive the question, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. 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, 8-11, 13, 15-19, and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Fratkina et al (US 7539656 B2) in view of Gharaibeh, et al (Smart Cities: A Survey on Data Management, Security, and Enabling Technologies, 2017).
Regarding claim 1
Fratkina teaches: A computer-implemented method of a machine learning based voice-response system, the computer-implemented method comprising: identifying, by the machine learning based voice-response system operatively coupled to a plurality of processors and comprising a plurality of interconnected Internet of Things (IOT) devices, an interaction of a user with a computing device ([Abstract] The invention can be implemented so that it can interact with customers through a wide variety of communication channels including the Internet, wireless devices (e.g., telephone, pager, etc.), handheld devices such as a Personal Data Assistant (PDA), email, and via a telephone where the automated system is delivered using an interactive voice response (IVR) and/or speech-recognition system. [Column 4, Lines 16-19] The purpose of a dialog engine is to facilitate the following in an electronic interaction between a human being and a machine (computer or other device including for example a telephone or personal data assistant). Note: Human being corresponds to the user. Note: Also see Figure 1 with several computers corresponding to a system operatively coupled to a plurality of processors and comprising a plurality of interconnected Internet of Things (IOT) devices);
collecting, by the plurality of interconnected devices of the machine learning based voice-response system, audio and physical environmental sensed data regarding an operating environment in which the user is located ([Column 2, Lines 56-62] The invention can be implemented so that it can interact with customers through a wide variety of communication channels including the Internet, wireless devices (e.g., telephone, pager, etc.), handheld devices such as a personal data assistant (PDA), email, and via a telephone where the automated system is delivered using an interactive voice response (IVR) and/or speech-recognition system. [Column 13, Lines 15-24] In some embodiments, a similar type of interaction can be achieved vocally via an interactive voice response (IVR) type of system. In these embodiments, the user speaks their requests and responses into telephone 4 or other microphone and may also provide other input by pressing buttons (e.g. buttons on the telephone's keypad). The user's spoken responses are passed to a voice recognition system, which turns the responses into data that dialog engine 232 can process. The dialog engine 232 response is passed to a text-to-speech system that turns it into a vocal response to the user. Note: User speaking corresponds to audio sensed data and input gathered by pressing buttons corresponds to physical environmental sensed data. [Column 32, Lines 24-28] In one embodiment of the subject invention, it is envisioned that the system could log the data, apply known machine learning techniques, and feed analysis back into the system to provide a mechanism by which the system "learns" from prior user query patterns);
aggregating, by the machine learning based voice-response system, in a corpus, the audio and physical environmental sensed data, the environmental constraints, usage statistics such as statistics of commands from the user, and environmental statistics such as the context of an environment that the user is located within preceding and after utilizing an interface of the computing device ([Column 13, Lines 15-24] In some embodiments, a similar type of interaction can be achieved vocally via an interactive voice response (IVR) type of system. In these embodiments, the user speaks their requests and responses into telephone 4 or other microphone and may also provide other input by pressing buttons (e.g. buttons on the telephone's keypad). The user's spoken responses are passed to a voice recognition system, which turns the responses into data that dialog engine 232 can process. The dialog engine 232 response is passed to a text-to-speech system that turns it into a vocal response to the user. [Column 40, Lines 9-27] To further illustrate the principles of the present invention, a practical example of a dialog is provided in FIGS. 19 21. Consider the situation that occurs when a person walks into a restaurant to order a meal. For the purposes of this example, assume that all service in this particular restaurant are provided by the present invention with the help of robots to deliver "documents" (or dishes) to the customers. The goal of this dialog is to get the right dishes to the customer. As shown in FIG. 19, sample dialog begins when the hostess prompts the user with the question "Yes?" In response, the user responds with an answer "Two for Lunch." Referring to FIG. 19, it is shown that this answer resolves one goal of the dialog. That is, it identifies the correct meal. The waiter then prompts a user with a follow-up question "We have eggs and pancakes. What would you like?" The user's answer further refines the number of available nodes by eliminating the "Pancakes" node 2010 (FIG. 20) from the Transaction goal. This iterative process continues until the dialog engine fully satisfies the user's requests as shown in FIG. 21. Note: Situation when a person walks into a restaurant corresponds to environment and the dialog to get the right dishes is the context and also shows the usage statistics. Figure 21 also shows the interface of the computing device);
and outputting, by the machine learning based voice-response system, by the machine learning based voice response system, a notification message to the user based on the context and the corpus, wherein the outputting utilizes natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output the notification message to provide to the user from the computing device ([Column 9, Lines 30-67] 95) Definitions: User: A person creating a session with the dialog engine. (97) Knowledge container: A combination of content and meta-data in the form of tags to a knowledge map. (98) Web service: A software application accessible by universal resource locator (URL) or extensive markup language (XML). (99) Common ground: A representation of a knowledge session that may be displayed to a user to show how the dialog engine interprets the information elicited from the user or inferred during dialog processing and that may be modified by the user in order to correct or change the dialog engine's interpretation. Knowledge map: A structured representation (model) of the real world encapsulated in a set of classifications and linked by relationships or rules. Interaction form: A standard mechanism for eliciting additional information from an application user by automatically generating a query. Knowledge session: An interaction between a human and a dialog engine as mediated through a knowledge map. Knowledge session state (also known as "dialog state"): The aggregate set of meta-data (tags), text and dialog information that encapsulates the known facts about a knowledge session. Question: The natural language text communicated by a user to the dialog engine. Query: An interaction form combined with a graphical user interface (GUI) representation on a screen sent by the dialog engine to the user. Search space: The set of knowledge containers passed to the dialog engine by a search/retrieval engine as potentially useful given the session state).
However, Fratkina does not explicitly disclose: and wherein the physical environmental sensed data is also associated with environmental constraints and is derived from and comprises integrated Internet of Things (IOT) feeds, camera feeds and environment data feeds; training, by the machine learning based voice-response system, a machine learning model comprising bi-directional long short-term memory to detect patterns in information in the usage statistics and the environmental statistics comprising the context of the environment and stored in the corpus, wherein the training is to determine a relationship between the user and the computing device by determining correlations between commands of the user to one or more of the plurality of interconnected IOT devices and the context;
Gharaibeh teaches, in an analogous system: and wherein the physical environmental sensed data is also associated with environmental constraints and is derived from and comprises integrated Internet of Things (IOT) feeds, camera feeds and environment data feeds ([Page 2456, Abstract] Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. [Page 2461, Column 2, Paragraph 2] In this project, lamp posts are equipped with cameras, environmental sensors and WiFi connections, among other technologies. Note: Also see Figures 4, 5, and 6 etc);
training, by the machine learning based voice-response system, a machine learning model comprising bi-directional long short-term memory to detect patterns in information in the usage statistics and the environmental statistics comprising the context of the environment and stored in the corpus, wherein the training is to determine a relationship between the user and the computing device by determining correlations between commands of the user to one or more of the plurality of interconnected IOT devices and the context ([Abstract] Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. [Page 2464, Column 1, Last but one Paragraph] Information and Communication System: The system includes a control system to manage DER and ESS to ensure the efficient operation of the VPP through bidirectional communication. The control system is also responsible for load forecasting, monitoring and coordination between the VPP components. [Page 2469, Column 1, Paragraph 1] Through a study survey, Breetzke and Flowerday [132] conclude that using an Interactive Voice Response system in smart city crowdsourcing is beneficial [Page 2470, Column 2, Last Paragraph] In the following subsections, we discuss the role of machine learning, Deep learning and Real-time Analytics in the context of smart cities for knowledge discovery. [Page 2473, Column 2, Last Paragraph] Long Short Term Memory (LSTM): The main motivation behind the use of LSTM is to deal with the problems of vanishing and exploding gradients. Vanishing gradient can cause the deep learning algorithm to either learn at a very slow pace or stop learning altogether, while exploding gradients can cause the learning algorithm to diverge. LSTM allows a neuron cell to read, write, or erase the current state of the cell via gates. Note: Figure 1 also shows users in correlation to IOT).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Fratkina to incorporate the teachings of Gharaibeh and wherein the physical environmental sensed data is also associated with environmental constraints and is derived from and comprises integrated Internet of Things (IOT) feeds, camera feeds and environment data feeds and training, by the machine learning based voice-response system, a machine learning model comprising bi-directional long short-term memory to detect patterns in information in the usage statistics and the environmental statistics comprising the context of the environment and stored in the corpus, wherein the training is to determine a relationship between the user and the computing device by determining correlations between commands of the user to one or more of the plurality of interconnected IOT devices and the context. One would have been motivated to do this modification because doing so would give the benefit of integrating the various embedded devices and systems in the environment enabling an Internet of Things (IoT) for a smart city as taught by Gharaibeh [Abstract].
Regarding claim 8
The system of Fratkina, Monir, and Ho teaches: The computer-implemented method of claim 1 (as shown above).
Fratkina further teaches: wherein the determined relationship includes an influence of the first set of conditions on the user that is correlated to inducing an interaction between the user and the computing device ([Column 6, Lines 15-23] Popular or parameterized queries (PQs): One obvious response to a user question is to determine whether it is, in fact, analogous/equivalent to a question for which there is a well-known answer. In human conversation, this is captured by: "So, are you really asking, X?". X is a restatement of the question in terms understood by the responder. The responder asks the query in this way to ensure that a possible known "answer" is really relevant (that is, the user is actually asking the question which is the predicate of the answer).
Regarding claim 9
Fratkina teaches: A computer program product comprising: one or more non-transitory computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising ([Column 40, Lines 42-50] Although aspects of the present invention are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or CD-ROMs; a carrier wave from the Internet; or other forms of RAM or ROM. Similarly, the method of the present invention may conveniently be implemented in program modules that are based upon the flow charts in FIG. 18);
program instructions to identify an interaction of a user with a computing device ([Column 4, Lines 16-19] The purpose of a dialog engine is to facilitate the following in an electronic interaction between a human being and a machine (computer or other device including for example a telephone or personal data assistant). Note: Human being corresponds to the user);
program instructions to determine a first set of conditions of an operating environment that includes the interaction of the user with the computing device ([Column 6, Lines 15-23] Popular or parameterized queries (PQs): One obvious response to a user question is to determine whether it is, in fact, analogous/equivalent to a question for which there is a well-known answer. In human conversation, this is captured by: "So, are you really asking, X?". X is a restatement of the question in terms understood by the responder. The responder asks the query in this way to ensure that a possible known "answer" is really relevant (that is, the user is actually asking the question which is the predicate of the answer). [Column 6, Lines 24-26] In the dialog engine, parameterized queries (PQs) are specific "pre-created" queries that are played to the user. Note: User question corresponds to the interaction of the user with the computing device and the restatement of the question (pre-created queries) corresponds to a first set of conditions of an operating environment that includes the interaction of the user); and data corresponding to the operating environment of the computing device from one or more interconnected devices ([Column 11, Lines 3-24] Referring now to the drawings, FIG. 1 illustrates a network 10 for implementing the subject invention. As shown in FIG. 1, network 10 is comprised of thin client computing devices 2 (PDAs and cellular telephones), analog or digital telephones 4, desktop or laptop client computing devices 12, facsimile machines 16, gateways 18, extranets 22 and servers 24 coupled to a public network 20. Digital telephones 4, client computing devices 12, facsimile machines 16, gateways 18, extranets 22 and servers 24 are coupled to public network 20 via a conventional interface 32. As shown in FIG. 1, thin client computing devices 2 are coupled to gateway 18 via a wireless interface 34. Each telephone 4 is a conventional analog or digital telephone that communicates with other analog and digital telephones over a public-switched telephone network (PSTN). Client computing devices 12 may be directly connected to public network 20, or they may be coupled to the public network 20 via gateway 18. Gateway 18 is a file server that may be connected to other computers on the public network 20. Company extranet 22 is a smaller private network that may be separated from other computers on public network by a firewall. Coupled to company extranet 22 are a plurality of server computers 24. [Column 29, Lines 1-4] The dialog engine can derive information from text that is typed in by the user. This process of deriving data is one of the many ways in which the dialog engine gathers information. [Column 13, Lines 15-24] In some embodiments, a similar type of interaction can be achieved vocally via an interactive voice response (IVR) type of system. In these embodiments, the user speaks their requests and responses into telephone 4 or other microphone and may also provide other input by pressing buttons (e.g. buttons on the telephone's keypad). The user's spoken responses are passed to a voice recognition system, which turns the responses into data that dialog engine 232 can process. The dialog engine 232 response is passed to a text-to-speech system that turns it into a vocal response to the user. Note: Other input by pressing buttons (e.g. buttons on the telephone's keypad corresponds to data corresponding to the operating environment of the computing device from one or more interconnected devices);
program instructions to train and apply a machine learning model to determine a relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device by applying the machine learning model to commands of the user and environmental statistics to determine one or more patterns in usage and environmental statistics correlations between commands of the user and the context, wherein the context is the environmental statistics ([Column 31, Lines 58-67] In some embodiments, the dialog engine can contain a module that performs various kinds of analysis of the logging data, in order to change the future behavior of the dialog engine and better customize dialog to a particular user based on prior usage. This feedback-based learning module may use a variety of statistical and machine-learning techniques to learn any of the following: Long-term user preferences for a particular user, Common dialog control information (triggers), including: [Column 32, Lines 1-8] Relationships between concept nodes in different regions of the knowledge map (triggers confirming a node); Relevance of taxonomies based on confirmed concepts (triggers creating goals or adding/removing taxonomies); and Commonly targeted information (parameterized queries (PQs)). [Column 32, Lines 24-37] In one embodiment of the subject invention, it is envisioned that the system could log the data, apply known machine learning techniques, and feed analysis back into the system to provide a mechanism by which the system "learns" from prior user query patterns. In another embodiment, the feedback-based learning module learns a mapping from selected user profile data (including meta-data and concept-node tags) and confirmed or preferred concept-nodes to other preferred concept-nodes. In other words, this map indicates, for users with given properties, at a given point or points in a dialog, what other concept-nodes may be considered as "preferred" by the dialog engine for its next response(s). This learning is based on a statistical analysis of previous usage that analyzes users' document selections);
program instructions to generate a knowledge base that includes the determined relationship, the first set of conditions of the operating environment, and the interaction of the user with the computing device ([Column 6, Lines 27-] For example, suppose that the parameterized query (PQ) (PQ: 1245 containing query: "Are you receiving an error message #101 when installing for the first time?", options: YES/NO and answer: KC EXTERNALID:001) is mapped within the knowledge map to the activity taxonomy: First time install and to the symptom taxonomy: error message. Note: knowledge map corresponds to knowledge base).
However, Fratkina does not explicitly disclose: program instructions to train and apply a reinforcement learning process to determine a context of the interaction between the user and the computing device; employing a bidirectional long-short term model, wherein the environmental statistics are determined via integrations with Internet of Things feeds, camera feeds, and environment data comprising weather; to a smart speaker
Gharaibeh teaches, in an analogous system: program instructions to train and apply a reinforcement learning process to determine a context of the interaction between the user and the computing device ([Page 2471, Column 1, Paragraph 2] Generally, machine learning is classified into reinforcement learning, supervised and unsupervised learning. Reinforcement learning algorithms are defined by a quintuple, the set of states (including a beginning and final states), actions, transitions, policies and rewards. Each transition, from a state-action pair to another either earns a rewards or a penalty. The objective is to choose transitions from the beginning to the final state that maximize the rewards in the long-term);
employing a bidirectional long-short term model ([Page 2464, Column 1, Last but one Paragraph] Information and Communication System: The system includes a control system to manage DER and ESS to ensure the efficient operation of the VPP through bidirectional communication. The control system is also responsible for load forecasting, monitoring and coordination between the VPP components. [Page 2469, Column 1, Paragraph 1] Through a study survey, Breetzke and Flowerday [132] conclude that using an Interactive Voice Response system in smart city crowdsourcing is beneficial [Page 2470, Column 2, Last Paragraph] In the following subsections, we discuss the role of machine learning, Deep learning and Real-time Analytics in the context of smart cities for knowledge discovery. [Page 2473, Column 2, Last Paragraph] Long Short Term Memory (LSTM): The main motivation behind the use of LSTM is to deal with the problems of vanishing and exploding gradients. Vanishing gradient can cause the deep learning algorithm to either learn at a very slow pace or stop learning altogether, while exploding gradients can cause the learning algorithm to diverge. LSTM allows a neuron cell to read, write, or erase the current state of the cell via gates),
wherein the environmental statistics are determined via integrations with Internet of Things feeds, camera feeds, and environment data comprising weather ([Page 2456, Abstract] Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. [Page 2461, Column 2, Paragraph 2] In this project, lamp posts are equipped with cameras, environmental sensors and WiFi connections, among other technologies. Note: Also see Figures 4, 5, and 6 etc. [Page 2470, Paragraph 3] Another example is weather monitoring, where the different sensors distributed throughout the city need to be energy-efficient);
to a smart speaker ([Page 2487, Figure 17] Note: Figure 17 shows smart speakers);
and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing the bidirectional long-short term model ([Abstract] Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. [Page 2464, Column 1, Last but one Paragraph] Information and Communication System: The system includes a control system to manage DER and ESS to ensure the efficient operation of the VPP through bidirectional communication. The control system is also responsible for load forecasting, monitoring and coordination between the VPP components. [Page 2469, Column 1, Paragraph 1] Through a study survey, Breetzke and Flowerday [132] conclude that using an Interactive Voice Response system in smart city crowdsourcing is beneficial [Page 2470, Column 2, Last Paragraph] In the following subsections, we discuss the role of machine learning, Deep learning and Real-time Analytics in the context of smart cities for knowledge discovery. [Page 2473, Column 2, Last Paragraph] Long Short Term Memory (LSTM): The main motivation behind the use of LSTM is to deal with the problems of vanishing and exploding gradients. Vanishing gradient can cause the deep learning algorithm to either learn at a very slow pace or stop learning altogether, while exploding gradients can cause the learning algorithm to diverge. LSTM allows a neuron cell to read, write, or erase the current state of the cell via gates. Note: Figure 1 also shows users in correlation to IOT).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer program product of Fratkina to incorporate the teachings of Gharaibeh to use program instructions to train and apply a reinforcement learning process to determine a context of the interaction between the user and the computing device; employing a bidirectional long-short term model, wherein the environmental statistics are determined via integrations with Internet of Things feeds, camera feeds, and environment data comprising weather; to a smart speaker, and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing the bidirectional long-short term model. One would have been motivated to do this modification because doing so would give the benefit of integrating the various embedded devices and systems in the environment enabling an Internet of Things (IoT) for a smart city as taught by Gharaibeh [Abstract].
Regarding claim 10
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 9 (as shown above).
Fratkina further teaches: further comprising program instructions, stored on the one or more computer readable storage media, to: identify a second set of conditions in the operating environment that includes the interaction of the user with the computing device ([Column 6, Lines 32-42] If the user asks the questions "I'm getting an error when installing the software" and this auto contextualizes to the activity taxonomy: first time install, symptom taxonomy: error message, object taxonomy: software, then the dialog engine will play the parameterized query (PQ) to the user. If the user answers Yes, the answer will be displayed. If the user answers no, the answer will not be displayed. The user's answer changes the session state by emphasizing the importance of the tags mapped to the parameterized query (PQ). Note: The session state where dialog engine will play the parameterized query (PQ) to the user corresponds to the second set of conditions in the operating environment. The user answering yes/no corresponds to the interaction of the user with the computing device);
determine that the second set of conditions in the operating environment matches the determined first set of conditions of the operating environment included in the knowledge base ([Column 6, Lines 24-38] In the dialog engine, parameterized queries (PQs) are specific "pre-created" queries that are played to the user when the session state matches the conditions necessary for the parameterized query (PQ) to be appropriate. For example, suppose that the parameterized query (PQ) (PQ: 1245 containing query: "Are you receiving an error message #101 when installing for the first time?", options: YES/NO and answer: KC EXTERNALID:001) is mapped within the knowledge map to the activity taxonomy: First time install and to the symptom taxonomy: error message. If the user asks the questions "I'm getting an error when installing the software" and this auto contextualizes to the activity taxonomy: first time install, symptom taxonomy: error message, object taxonomy: software, then the dialog engine will play the parameterized query (PQ) to the user. Note: The session state of the user being asked if receiving an error is the second set of conditions that matches with the pre-created query which is the first set of conditions);
and perform a defined action based at least in part on the user interaction of the knowledge base ([Column 6, Lines 24-38] If the user answers Yes, the answer will be displayed. Note: Answer being displayed corresponds to the defined action).
Regarding claim 11
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 9 (as shown above).
Fratkina further teaches: further comprising program instructions, stored on the one or more computer readable storage media, to: determine whether a count of a number of occurrences of the determined relationship exceeds a defined threshold of occurrences over a defined timeframe ([Column 30, Lines 65-67] In some embodiments, the dialog engine or a separate piece of software may provide a way to visually examine taxonomy and concept-node based breakdowns of analyzed or aggre- [Column 31, Lines 1-2] gated logging data over all time, or over particular selected time periods. [Column 25, Lines 6-15] In one embodiment of the present invention, the system is capable of using customer profile information described above to push content to interested users. More specifically, when new knowledge containers 20 enter the system, the system matches each against each customer's profile taxonomy tags 40 in the associated the application screen. Knowledge containers 20 that match customer profiles sufficiently closely--with a score over a predetermined threshold--are pushed to customers on their personal web pages, through email, or via email to other channels);
and in response to determining that the count of the number of occurrences of the determined relationship exceeds the defined threshold of occurrences over the defined timeframe, modify the knowledge base based on the number of occurrences of the determined relationship ([Column 30, Lines 65-67] In some embodiments, the dialog engine or a separate piece of software may provide a way to visually examine taxonomy and concept-node based breakdowns of analyzed or aggre- [Column 31, Lines 1-2] gated logging data over all time, or over particular selected time periods. [Column 24, Lines 22-34] The combination of taxonomies, taxonomy tags, taxonomic restrictions (filters), and knowledge containers provide a large collection of personalization capabilities to the present system. Certain of these taxonomies can be used to: capture the universe of information needs and interests of end-users; tag the knowledge containers representing these users with the appropriate concept nodes from these taxonomies; and use these concept nodes when retrieving information to personalize the delivery of knowledge containers to the user. Further, the system can use this tagging and other aspects of the knowledge containers in order to create a display format appropriate for the needs of the user receiving the knowledge container. [Column 25, Lines 6-15] In one embodiment of the present invention, the system is capable of using customer profile information described above to push content to interested users. More specifically, when new knowledge containers 20 enter the system, the system matches each against each customer's profile taxonomy tags 40 in the associated the application screen. Knowledge containers 20 that match customer profiles sufficiently closely--with a score over a predetermined threshold--are pushed to customers on their personal web pages, through email, or via email to other channels).
Regarding claim 13
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 9 (as shown above).
Fratkina further teaches: wherein program instructions to determine the first set of conditions of the operating environment that includes the interaction of the user with the computing device, further comprise program instructions to: aggregate the collected data that includes conditions of the operating environment, wherein the collected data corresponds to a defined time period that includes events prior to and subsequent the user interaction with the computing device ([Column 30, Lines 65-67] In some embodiments, the dialog engine or a separate piece of software may provide a way to visually examine taxonomy and concept-node based breakdowns of analyzed or aggregated [Column 31, Lines 1-20] logging data over all time, or over particular selected time periods. This visualization is performed by displaying the structure and concept-nodes of one or more taxonomies visually and with each concept-node, providing a visual cue that indicates the value of the analyzed or aggregated logging data point being reported, for that concept-node. The visual cue may be, for example, displaying the concept-nodes in different colors (e.g. red may mean a low value, green a high value, and the range of colors in between representing intermediate values); or by displaying a numeric value next to each concept-node; or by displaying one or more icons next to each concept node (e.g. a small, medium, or large stack of question-mark icons to represent query volume at each concept-node). This type of visualization can be very useful because it allows a dialog designer to get an overall view of the activity or aggregated/analyzed values across a whole taxonomy or set of taxonomies at once. The visual cues allow a person to very quickly identify the areas of taxonomies that may warrant further attention or analysis).
Regarding claim 15
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 10 (as shown above).
Fratkina further teaches: wherein program instructions to perform the defined action based at least in part on the user interaction of the knowledge base, further comprise program instructions to: identify an action of the user in the knowledge base, wherein the action corresponds to a determined relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device ([Column 6, Lines 15-23] Popular or parameterized queries (PQs): One obvious response to a user question is to determine whether it is, in fact, analogous/equivalent to a question for which there is a well-known answer. In human conversation, this is captured by: "So, are you really asking, X?". X is a restatement of the question in terms understood by the responder. The responder asks the query in this way to ensure that a possible known "answer" is really relevant (that is, the user is actually asking the question which is the predicate of the answer). Note: User question corresponds to the interaction of the user with the computing device and the restatement of the question (pre-created queries) corresponds to a first set of conditions of an operating environment that includes the interaction of the user. The user is actually asking the question which is the predicate of the answer corresponds to identifying an action of the user in the knowledge base);
and perform the identified action, wherein performance of the identified action is selected from a group consisting of: previously performed actions of a user and providing a performance confirmation request ([Column 6, Lines 20-23] The responder asks the query in this way to ensure that a possible known "answer" is really relevant (that is, the user is actually asking the question which is the predicate of the answer). [Column 15, Lines 1-15] Node 300, 310 may have one or more triggers associated with it. In the present invention, dialog control logic is expressed through the use of triggers which consists of a label, a condition, an event and an action. When an event occurs, triggers associated with the event are evaluated. If the trigger condition is satisfied, the action is taken. Below is an example of a simple trigger: If (under(a12, ts)) { confirm(a3); }. The left hand side of the trigger is a condition "If (under(a12, ts))" and the right hand side is an event or action "{ confirm(a3); }". As shown, the trigger's condition consists of the keyword "if" followed by a left parenthesis, a boolean expression of one or more predicates, and then a right parenthesis. A single predicate may have the form "Condition (concept, concept set)." Note: a possible known "answer" corresponds to previously performed actions of a user).
Regarding claim 16
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 9 (as shown above).
Fratkina further teaches: wherein the determined relationship includes an influence of the first set of conditions on the user that is correlated to inducing an interaction between the user and the computing device ([Column 6, Lines 15-23] Popular or parameterized queries (PQs): One obvious response to a user question is to determine whether it is, in fact, analogous/equivalent to a question for which there is a well-known answer. In human conversation, this is captured by: "So, are you really asking, X?". X is a restatement of the question in terms understood by the responder. The responder asks the query in this way to ensure that a possible known "answer" is really relevant (that is, the user is actually asking the question which is the predicate of the answer).
Regarding claim 17
Fratkina teaches: A computer system comprising: one or more computer processors ([Column 11, Lines 62-63] As shown in FIG. 2, client computing device (2 and 12) is further comprised of a central processor unit (CPU));
one or more Internet of Things (IOT) computing devices having sensors and coupled to the one or more computer processors ([Column 13, Lines 15-24] In some embodiments, a similar type of interaction can be achieved vocally via an interactive voice response (IVR) type of system. In these embodiments, the user speaks their requests and responses into telephone 4 or other microphone and may also provide other input by pressing buttons (e.g. buttons on the telephone's keypad). The user's spoken responses are passed to a voice recognition system, which turns the responses into data that dialog engine 232 can process. Note: Also see Figure 1 with several computers corresponding to one or more Internet of Things (IOT) computing devices having sensors and coupled to the one or more computer processors. Microphone corresponds to a type of sensor);
one or more computer readable storage media; and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: ([Column 40, Lines 42-50] Although aspects of the present invention are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or CD-ROMs; a carrier wave from the Internet; or other forms of RAM or ROM. Similarly, the method of the present invention may conveniently be implemented in program modules that are based upon the flow charts in FIG. 18);
program instructions to initiate data sensing via a sensor of an IOT computing device of the one or more IOT computing devices ([Column 13, Lines 15-24] In some embodiments, a similar type of interaction can be achieved vocally via an interactive voice response (IVR) type of system. In these embodiments, the user speaks their requests and responses into telephone 4 or other microphone and may also provide other input by pressing buttons (e.g. buttons on the telephone's keypad). The user's spoken responses are passed to a voice recognition system, which turns the responses into data that dialog engine 232 can process. Note: Also see Figure 1 with several computers corresponding to one or more Internet of Things (IOT) computing devices having sensors and coupled to the one or more computer processors. Microphone corresponds to a type of sensor);
program instructions to identify an interaction of the user with the IOT computing device ([Column 4, Lines 16-19] The purpose of a dialog engine is to facilitate the following in an electronic interaction between a human being and a machine (computer or other device including for example a telephone or personal data assistant). Note: Human being corresponds to the user);
program instructions to determine a first set of conditions of an operating environment that includes the interaction of the user with the IOT computing device ([Column 6, Lines 15-23] Popular or parameterized queries (PQs): One obvious response to a user question is to determine whether it is, in fact, analogous/equivalent to a question for which there is a well-known answer. In human conversation, this is captured by: "So, are you really asking, X?". X is a restatement of the question in terms understood by the responder. The responder asks the query in this way to ensure that a possible known "answer" is really relevant (that is, the user is actually asking the question which is the predicate of the answer). [Column 6, Lines 24-26] In the dialog engine, parameterized queries (PQs) are specific "pre-created" queries that are played to the user. Note: User question corresponds to the interaction of the user with the computing device and the restatement of the question (pre-created queries) corresponds to a first set of conditions of an operating environment that includes the interaction of the user);
program instructions to determine a relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device ([Column 6, Lines 24-27] In the dialog engine, parameterized queries (PQs) are specific "pre-created" queries that are played to the user when the session state matches the conditions necessary for the parameterized query (PQ) to be appropriate. Note: Checking for match corresponds to determining a relationship);
program instructions to generate a knowledge base that includes the determined relationship, the first set of conditions of the operating environment, and the interaction of the user with the IOT computing device ([Column 6, Lines 27-] For example, suppose that the parameterized query (PQ) (PQ: 1245 containing query: "Are you receiving an error message #101 when installing for the first time?", options: YES/NO and answer: KC EXTERNALID:001) is mapped within the knowledge map to the activity taxonomy: First time install and to the symptom taxonomy: error message. Note: knowledge map corresponds to knowledge base).
However, Fratkina does not explicitly disclose: program instructions to collect audio and physical environmental sensed data regarding an operating environment in which a user is located and wherein the physical environmental sensed data is derived from integrated IOT feeds from the IOT computing device, camera feeds and environment data comprising weather data; and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing a machine learning model comprising a bidirectional long-short term model that determines the relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device.
Gharaibeh teaches, in an analogous system: program instructions to collect audio and physical environmental sensed data regarding an operating environment in which a user is located and wherein the physical environmental sensed data is derived from integrated IOT feeds from the IOT computing device, camera feeds and environment data comprising weather data ([Page 2456, Abstract] Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. [Page 2461, Column 2, Paragraph 2] In this project, lamp posts are equipped with cameras, environmental sensors and WiFi connections, among other technologies. Note: Also see Figures 4, 5, and 6 etc. [Page 2470, Paragraph 3] Another example is weather monitoring, where the different sensors distributed throughout the city need to be energy-efficient);
and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing a machine learning model comprising a bidirectional long-short term model that determines the relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device ([Abstract] Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. [Page 2464, Column 1, Last but one Paragraph] Information and Communication System: The system includes a control system to manage DER and ESS to ensure the efficient operation of the VPP through bidirectional communication. The control system is also responsible for load forecasting, monitoring and coordination between the VPP components. [Page 2469, Column 1, Paragraph 1] Through a study survey, Breetzke and Flowerday [132] conclude that using an Interactive Voice Response system in smart city crowdsourcing is beneficial [Page 2470, Column 2, Last Paragraph] In the following subsections, we discuss the role of machine learning, Deep learning and Real-time Analytics in the context of smart cities for knowledge discovery. [Page 2473, Column 2, Last Paragraph] Long Short Term Memory (LSTM): The main motivation behind the use of LSTM is to deal with the problems of vanishing and exploding gradients. Vanishing gradient can cause the deep learning algorithm to either learn at a very slow pace or stop learning altogether, while exploding gradients can cause the learning algorithm to diverge. LSTM allows a neuron cell to read, write, or erase the current state of the cell via gates. Note: Figure 1 also shows users in correlation to IOT).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Fratkina to incorporate the teachings of Gharaibeh to use program instructions to collect audio and physical environmental sensed data regarding an operating environment in which a user is located and wherein the physical environmental sensed data is derived from integrated IOT feeds from the IOT computing device, camera feeds and environment data comprising weather data; and program instructions to output, using natural language understanding (NLU) and natural language generation (NLG) machine processing for human-computer communication to create and output a notification message to provide to the user from the computing device, wherein the notification message is also determined based on the employing a machine learning model comprising a bidirectional long-short term model that determines the relationship between the first set of conditions of the operating environment and the interaction of the user with the IOT computing device. One would have been motivated to do this modification because doing so would give the benefit of Integrating the various embedded devices and systems in the environment enabling an Internet of Things (IoT) for a smart city as taught by Gharaibeh [Abstract].
Regarding claim 18
The system of Fratkina and Gharaibeh teaches: The computer system of claim 17, and the computer system further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, to (as shown above).
Fratkina further teaches: identify a second set of conditions in the operating environment that includes the interaction of the user ([Column 6, Lines 32-42] If the user asks the questions "I'm getting an error when installing the software" and this auto contextualizes to the activity taxonomy: first time install, symptom taxonomy: error message, object taxonomy: software, then the dialog engine will play the parameterized query (PQ) to the user. If the user answers Yes, the answer will be displayed. If the user answers no, the answer will not be displayed. The user's answer changes the session state by emphasizing the importance of the tags mapped to the parameterized query (PQ). Note: The session state where dialog engine will play the parameterized query (PQ) to the user corresponds to the second set of conditions in the operating environment. The user answering yes/no corresponds to the interaction of the user with the computing device);
determine that the second set of conditions in the operating environment matches the determined first set of conditions of the operating environment included in the knowledge base ([Column 6, Lines 24-38] In the dialog engine, parameterized queries (PQs) are specific "pre-created" queries that are played to the user when the session state matches the conditions necessary for the parameterized query (PQ) to be appropriate. For example, suppose that the parameterized query (PQ) (PQ: 1245 containing query: "Are you receiving an error message #101 when installing for the first time?", options: YES/NO and answer: KC EXTERNALID:001) is mapped within the knowledge map to the activity taxonomy: First time install and to the symptom taxonomy: error message. If the user asks the questions "I'm getting an error when installing the software" and this auto contextualizes to the activity taxonomy: first time install, symptom taxonomy: error message, object taxonomy: software, then the dialog engine will play the parameterized query (PQ) to the user. Note: The session state of the user being asked if receiving an error is the second set of conditions that matches with the pre-created query which is the first set of conditions);
and perform a defined action based at least in part on the user interaction of the knowledge base ([Column 6, Lines 24-38] If the user answers Yes, the answer will be displayed. Note: Answer being displayed corresponds to the defined action).
However, the system of Fratkina does not explicitly disclose: the computer system further comprising a camera having a sensor that records a video or photographs an image of a visual aspect of the operating environment and that is coupled to the one or more processors, and the computer system; with the camera having the sensor that records a video or photographs an image of the visual aspect of the operating environment.
Gharaibeh further teaches, in an analogous system: the computer system further comprising a camera having a sensor that records a video or photographs an image of a visual aspect of the operating environment and that is coupled to the one or more processors, and the computer system ([Page 2461, Column 2, Last Paragraph] using security Closed-Circuit Television Cameras (CCTV) and motion sensors [Page 2462] Note: Figure 6 shows camera having a sensor that can record a video and coupled with one or more processors);
with the camera having the sensor that records a video or photographs an image of the visual aspect of the operating environment ([Page 2461, Column 2, Last Paragraph] using security Closed-Circuit Television Cameras (CCTV) and motion sensors [Page 2462] Note: Figure 6 shows camera having a sensor that can record a video).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fratkina to incorporate the teachings of Gharaibeh to use the computer system further comprising a camera having a sensor that records a video or photographs an image of a visual aspect of the operating environment and that is coupled to the one or more processors, and the computer system; with the camera having the sensor that records a video or photographs an image of the visual aspect of the operating environment. One would have been motivated to do this modification because doing so would give the benefit of empowering users with data in order to be better informed, which leads to safer and smarter decisions as taught by Gharaibeh [Page 2461, Column 2, Last Paragraph].
Regarding claim 19
The system of Fratkina and Gharaibeh teaches: The computer system of claim 17 (as shown above).
Fratkina further teaches: further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, to: determine whether a count of a number of occurrences of the determined relationship exceeds a defined threshold of occurrences over a defined timeframe ([Column 30, Lines 65-67] In some embodiments, the dialog engine or a separate piece of software may provide a way to visually examine taxonomy and concept-node based breakdowns of analyzed or aggre- [Column 31, Lines 1-2] gated logging data over all time, or over particular selected time periods. [Column 25, Lines 6-15] In one embodiment of the present invention, the system is capable of using customer profile information described above to push content to interested users. More specifically, when new knowledge containers 20 enter the system, the system matches each against each customer's profile taxonomy tags 40 in the associated the application screen. Knowledge containers 20 that match customer profiles sufficiently closely--with a score over a predetermined threshold--are pushed to customers on their personal web pages, through email, or via email to other channels);
and in response to determining that the count of the number of occurrences of the determined relationship exceeds the defined threshold of occurrences, over the defined timeframe, modify the knowledge base based on the number of occurrences of the determined relationship ([Column 30, Lines 65-67] In some embodiments, the dialog engine or a separate piece of software may provide a way to visually examine taxonomy and concept-node based breakdowns of analyzed or aggre- [Column 31, Lines 1-2] gated logging data over all time, or over particular selected time periods. [Column 24, Lines 22-34] The combination of taxonomies, taxonomy tags, taxonomic restrictions (filters), and knowledge containers provide a large collection of personalization capabilities to the present system. Certain of these taxonomies can be used to: capture the universe of information needs and interests of end-users; tag the knowledge containers representing these users with the appropriate concept nodes from these taxonomies; and use these concept nodes when retrieving information to personalize the delivery of knowledge containers to the user. Further, the system can use this tagging and other aspects of the knowledge containers in order to create a display format appropriate for the needs of the user receiving the knowledge container. [Column 25, Lines 6-15] In one embodiment of the present invention, the system is capable of using customer profile information described above to push content to interested users. More specifically, when new knowledge containers 20 enter the system, the system matches each against each customer's profile taxonomy tags 40 in the associated the application screen. Knowledge containers 20 that match customer profiles sufficiently closely--with a score over a predetermined threshold--are pushed to customers on their personal web pages, through email, or via email to other channels).
Regarding claim 21
The system of Fratkina and Gharaibeh teaches: The computer-implemented method of claim 1 (as shown above).
Fratkina further teaches: further comprising: adding, by the machine learning based voice-response system, to the corpus, an importance level of the context and a reaction of the user to the context, wherein the context is an environmental parameter, the reaction is a voice command and an importance level is a captured audio phrase; performing, by the machine learning based voice-response system, machine learning processing to update data of the corpus to determine another relationship between the context and the interaction of the user ([Column 2, Lines 24-39] The present invention supports a model of interaction between a machine and a human being that closely models the way people interact with each other. It allows the user to begin with an incomplete problem description and elicits the unstated elements of the description--which the user may not know at the beginning of the interaction, or may not know are important--asking only questions that are relevant to the problem description stated so far, given the system's knowledge of the problem domain; without requiring the user to answer questions one at a time, or to answer all of the questions posed; and without imposing unnecessary restrictions on the order in which questions are posed to the user. The present invention allows the dialog designer to model the way an expert elicits information, giving a human feel to the dialog and a better customer experience. [Column 32, Lines 24-28] In one embodiment of the subject invention, it is envisioned that the system could log the data, apply known machine learning techniques, and feed analysis back into the system to provide a mechanism by which the system "learns" from prior user query patterns).
Regarding claim 22
The system of Fratkina and Gharaibeh teaches: The computer-implemented method of claim 21 (as shown above).
Fratkina further teaches: wherein the environmental parameter comprises one or more indicators that provides information about or describes the state of the environment such as the operating environment of the computing device ([Column 10, Lines 55-62] The operating environment in which the present invention is used encompasses general distributed computing systems wherein general purpose computers, work stations, or personal computers are connected via communication links of various types. In a client server arrangement, programs and data, many in the form of objects, are made available by various members of the system).
Regarding claim 23
The system of Fratkina and Gharaibeh teaches: The computer-implemented method of claim 1 (as shown above).
However, the system of Fratkina does not explicitly disclose: further comprising: training, by the machine learning based voice-response system, the bidirectional long- short term memory model employing historical answers of the user to pre-defined questions for the one or more IOT devices to determine types of the questions to generate for the one or more IOT devices and to determine the type of notification message to output to the user; and performing, by the machine learning based voice-response system, NLP techniques such as natural language understanding to process verbal responses of the user to questions of the type determined by the training of the bidirectional long-short term memory.
Gharaibeh further teaches: further comprising: training, by the machine learning based voice-response system, the bidirectional long- short term memory model employing historical answers of the user to pre-defined questions for the one or more IOT devices to determine types of the questions to generate for the one or more IOT devices and to determine the type of notification message to output to the user; and performing, by the machine learning based voice-response system, NLP techniques such as natural language understanding to process verbal responses of the user to questions of the type determined by the training of the bidirectional long-short term memory ([Page 2472, Column 1, Paragraph 2] IBM Watson [164] is a Question Answering (QA) computing system aiming at providing the best answer to a question posed by users through the use of different natural language processing and machine learning algorithms. Watson supports different types of questions from different data sources and provides advanced data cleaning functionality. It returns the best answer after comparing the results of different algorithms. [Page 2473, Column 2, Last Paragraph] Long Short Term Memory (LSTM)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fratkina to incorporate the teachings of Gharaibeh to train, by the machine learning based voice-response system, the bidirectional long- short term memory model employing historical answers of the user to pre-defined questions for the one or more IOT devices to determine types of the questions to generate for the one or more IOT devices and to determine the type of notification message to output to the user; and performing, by the machine learning based voice-response system, NLP techniques such as natural language understanding to process verbal responses of the user to questions of the type determined by the training of the bidirectional long-short term memory. One would have been motivated to do this modification because doing so would give the benefit of empowering users with data in order to be better informed, which leads to safer and smarter decisions as taught by Gharaibeh [Page 2461, Column 2, Last Paragraph].
Regarding claim 24
The system of Fratkina and Gharaibeh teaches: The computer-implemented method of claim 23 (as shown above).
However, the system of Fratkina does not explicitly disclose: further comprising: aggregating, by the machine learning based voice-response system, data of the one or more IOT devices that is a camera to identify that the user is present in an environment and employing, by the system, data of another one of the one or more IOT devices to identify that the user is not currently engaged in any activity with another IOT device and is therefore available to receive a question of the types of questions generated by the bidirectional long-short term memory model; and training, by the machine learning based voice-response system, the bidirectional long- short term memory model, to identify an appropriate time frame to transmit a question to the user through the computing device using data of the IOT device feeds or camera feeds based on the aggregating the data of the one or more IOT devices that is the camera and the employing the data of another one of the IOT devices to identify that the user is available to receive the question.
Gharaibeh further teaches: further comprising: aggregating, by the machine learning based voice-response system, data of the one or more IOT devices that is a camera to identify that the user is present in an environment and employing, by the system, data of another one of the one or more IOT devices to identify that the user is not currently engaged in any activity with another IOT device and is therefore available to receive a question of the types of questions generated by the bidirectional long-short term memory model; and training, by the machine learning based voice-response system, the bidirectional long- short term memory model, to identify an appropriate time frame to transmit a question to the user through the computing device using data of the IOT device feeds or camera feeds based on the aggregating the data of the one or more IOT devices that is the camera and the employing the data of another one of the IOT devices to identify that the user is available to receive the question ([Page 2467, Column 2, Last Paragraph] Systems integrating WSN and cameras are proposed in [91] and [92] for human identification and tracking. [Page 2472, Column 1, Paragraph 2] IBM Watson [164] is a Question Answering (QA) computing system aiming at providing the best answer to a question posed by users through the use of different natural language processing and machine learning algorithms. Watson supports different types of questions from different data sources and provides advanced data cleaning functionality. It returns the best answer after comparing the results of different algorithms. [Page 2473, Column 2, Last Paragraph] Long Short Term Memory (LSTM)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fratkina to incorporate the teachings of Gharaibeh to aggregate, by the machine learning based voice-response system, data of the one or more IOT devices that is a camera to identify that the user is present in an environment and employing, by the system, data of another one of the one or more IOT devices to identify that the user is not currently engaged in any activity with another IOT device and is therefore available to receive a question of the types of questions generated by the bidirectional long-short term memory model; and training, by the machine learning based voice-response system, the bidirectional long- short term memory model, to identify an appropriate time frame to transmit a question to the user through the computing device using data of the IOT device feeds or camera feeds based on the aggregating the data of the one or more IOT devices that is the camera and the employing the data of another one of the IOT devices to identify that the user is available to receive the question. One would have been motivated to do this modification because doing so would give the benefit of empowering users with data in order to be better informed, which leads to safer and smarter decisions as taught by Gharaibeh [Page 2461, Column 2, Last Paragraph].
Claims 4, 6, 12, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fratkina et al (US 7539656 B2) in view of Gharaibeh, et al (Smart Cities: A Survey on Data Management, Security, and Enabling Technologies, 2017) and further in view of Ho et al (Nurture: Notifying Users at the Right Time Using Reinforcement Learning, 2018).
Regarding claim 4
The system of Fratkina and Gharaibeh teaches: The computer-implemented method of claim 1 (as shown above).
Fratkina further teaches: further comprising: identifying, by the machine learning based voice-response system, a reaction of the user to the context of the environment, wherein the reaction is determined based on the commands from the user in view of the environment ([Abstract] A method and system are disclosed for retrieving information through the use of a multi-stage interaction with a client to identify particular knowledge content associated with a knowledge map. The present invention is an application program running on a server accessed via the world-wide web or other data network using standard Internet protocols, a web browser and web server software. In addition to an automated portion, the present invention allows a human dialog designer to model the way the system elicits information, giving a human feel to the dialog and a better customer experience. In operation, users start a dialog by directing their web browser to a designated web page. This web page asks the user some initial questions that are then passed to a dialog engine. The dialog engine then applies its methods and algorithms to a knowledge map, using dialog control information\ and the user's responses to provide feedback to the user. The feedback may include follow-up questions, relevant documents, and instructions to the user (e.g., instructions to contact a human customer service representative). This dialog engine response is rendered as a web page and returned to the user's web browser. The user can then respond further to the follow-up questions he or she is presented, and the cycle repeats. The invention can be implemented so that it can interact with customers through a wide variety of communication channels including the Internet, wireless devices (e.g., telephone, pager, etc.), handheld devices such as a Personal Data Assistant (PDA), email, and via a telephone where the automated system is delivered using an interactive voice response (IVR) and/or speech-recognition system).
However, Fratkina and Gharaibeh does not explicitly disclose: and updating, by the machine learning based voice-response system, a reward function of a reinforcement learning model of the corpus based on whether the machine learning based voice-response system determines that the reaction of the user denotes a negative connotation or response thereby resulting in the reinforcement learning model reducing the reward function.
Ho teaches, in an analogous system: and updating, by the machine learning based voice-response system, a reward function of a reinforcement learning model of the corpus based on whether the machine learning based voice-response system determines that the reaction of the user denotes a negative connotation or response thereby resulting in the reinforcement learning model reducing the reward function ([Page 1195, Column 2, Paragraph 3] Figure 1 illustrates the learning flow. Nurture obtains the user state via sensors, decides if it should notify the user, and observes user reaction after sending the notification. By considering accepted notifications as positive signals and dismissed notifications as negative signals, the agent learns the appropriate times to notify the user. We evaluated Nurture with both a synthetic and an online interactive crowdsourcing-based simulation. Our simulation results show that reinforcement learning improves user response rate compared to supervised learning methods. [Page 1197, Column 1] Reinforcement Learning [Page 1197, Column 1, Section: Experimental Setup] Nurture then obtains the reward according to the user’s reaction and adjusts the strategy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined method of Fratkina and Gharaibeh to incorporate the teachings of Ho to update by the machine learning based voice-response system, a reward function of a reinforcement learning model of the corpus based on whether the machine learning based voice-response system determines that the reaction of the user denotes a negative connotation or response thereby resulting in the reinforcement learning model reducing the reward function. One would have been motivated to do this modification because doing so would give the benefit of the reward being based on the reaction of the user: a positive reward is received if the user responds to the notification, a negative reward is received if the notification is dismissed, and zero reward is received if the user ignores it as taught by Ho [Page 1196, Column 2, Paragraph 2].
Regarding claim 6
The system of Fratkina, Gharaibeh, and Ho teaches: The computer-implemented method of claim 4 (as shown above).
However, Fratkina and Gharaibeh does not explicitly disclose: 4, further comprising: based on the updating the reward function by the reinforcement learning model by reducing the reward function: determining, by the machine learning based voice-response system, a different relationship between the user and the computing device; or determining, by the machine learning based voice-response system, a different combination of conditions of the context based on the reaction of the user; and updating the corpus with the different relationship or the different combination of conditions of the context.
Ho further teaches: 4, further comprising: based on the updating the reward function by the reinforcement learning model by reducing the reward function: determining, by the machine learning based voice-response system, a different relationship between the user and the computing device; or determining, by the machine learning based voice-response system, a different combination of conditions of the context based on the reaction of the user; and updating the corpus with the different relationship or the different combination of conditions of the context ([Page 1195] Note: see Figure 1 showing observations corresponding to the first set of conditions of the operating environment for the interaction of the user with the computing device and Nurture (learning unit) corresponds to a reinforcement learning model);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined method of Fratkina and Gharaibeh to incorporate the teachings of 4, further comprising: based on the updating the reward function by the reinforcement learning model by reducing the reward function: determining, by the machine learning based voice-response system, a different relationship between the user and the computing device; or determining, by the machine learning based voice-response system, a different combination of conditions of the context based on the reaction of the user; and updating the corpus with the different relationship or the different combination of conditions of the context. One would have been motivated to do this modification because doing so would give the benefit of the reward being based on the reaction of the user: a positive reward is received if the user responds to the notification, a negative reward is received if the notification is dismissed, and zero reward is received if the user ignores it as taught by Ho [Page 1196, Column 2, Paragraph 2].
Regarding claim 12
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 10 (as shown above).
Fratkina further teaches: further comprising program instructions, stored on the one or more computer readable storage media, to: detect a reaction of the user to performing the defined action, wherein the reaction of the user is selected from a group consisting of: affirmative actions and negation actions ([Column 6, Line 30] options: YES/NO. [Column 6, Lines 38-40] If the user answers Yes, the answer will be displayed. If the user answers no, the answer will not be displayed. Note: Yes corresponds to affirmative and no corresponds to negation);
However, Fratkina and Gharaibeh does not explicitly disclose: and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user.
Ho teaches, in an analogous system: and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user ([Page 1197, Column 1] Reinforcement Learning [Page 1197, Column 1, Section: Experimental Setup] Nurture then obtains the reward according to the user’s reaction and adjusts the strategy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined teachings of Fratkina and Gharaibeh to incorporate the teachings of Ho to update, by one or more processors, a reward function of a reinforced learning model of the knowledge base based on the reaction of the user. One would have been motivated to do this modification because doing so would give the benefit of the reward being based on the reaction of the user: a positive reward is received if the user responds to the notification, a negative reward is received if the notification is dismissed, and zero reward is received if the user ignores it as taught by Ho [Page 1196, Column 2, Paragraph 2].
Regarding claim 14
The system of Fratkina and Gharaibeh teaches: The computer program product of claim 9 (as shown above).
However, Fratkina and Gharaibeh does not explicitly disclose: wherein program instructions to determine the relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device, further comprise program instructions to: input the first set of conditions of the operating environment for the interaction of the user with the computing device into a reinforcement learning model; and select an output state of the reinforcement learning model, wherein the output state includes a determined relationship with a maximum reward value.
Ho further teaches: wherein program instructions to determine the relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device, further comprise program instructions to: input the first set of conditions of the operating environment for the interaction of the user with the computing device into a reinforcement learning model ([Page 1195] Note: see Figure 1 showing observations corresponding to the first set of conditions of the operating environment for the interaction of the user with the computing device and Nurture (learning unit) corresponds to a reinforcement learning model);
and select an output state of the reinforcement learning model, wherein the output state includes a determined relationship with a maximum reward value ([Page 1195, Column 2, Paragraph 2] RL implicitly tracks the state changes of the user to maximize long term rewards).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined teachings of Fratkina and Gharaibeh to incorporate the teachings of Ho wherein program instructions to determine the relationship between the first set of conditions of the operating environment and the interaction of the user with the computing device, further comprise program instructions to: input the first set of conditions of the operating environment for the interaction of the user with the computing device into a reinforcement learning model; and select an output state of the reinforcement learning model, wherein the output state includes a determined relationship with a maximum reward value. One would have been motivated to do this modification because doing so would give the benefit of the reward being based on the reaction of the user: a positive reward is received if the user responds to the notification, a negative reward is received if the notification is dismissed, and zero reward is received if the user ignores it as taught by Ho [Page 1196, Column 2, Paragraph 2].
Regarding claim 20
The system of Fratkina, Monir, Ho, and Gharaibeh teaches: The computer system of claim 18 (as shown above).
Fratkina further teaches: further comprising program instructions, stored on the one or more computer readable storage media for execution by at least one of the one or more processors, to: detect a reaction of the user to performing the defined action, wherein the reaction of the user is selected from a group consisting of: affirmative actions and negation actions ([Column 6, Line 30] options: YES/NO. [Column 6, Lines 38-40] If the user answers Yes, the answer will be displayed. If the user answers no, the answer will not be displayed. Note: Yes corresponds to affirmative and no corresponds to negation);
However, Fratkina, Monir, and Gharaibeh do not explicitly disclose: and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user.
Ho teaches, in an analogous system: and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user ([Page 1197, Column 1] Reinforcement Learning [Page 1197, Column 1, Section: Experimental Setup] Nurture then obtains the reward according to the user’s reaction and adjusts the strategy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined teachings of Fratkina, Monir, and Gharaibeh to incorporate the teachings of Ho to and update a reward function of a reinforced learning model of the knowledge base based on the reaction of the user. One would have been motivated to do this modification because doing so would give the benefit of the reward being based on the reaction of the user: a positive reward is received if the user responds to the notification, a negative reward is received if the notification is dismissed, and zero reward is received if the user ignores it as taught by Ho [Page 1196, Column 2, Paragraph 2].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Harish et al (VFF- A Framework for linking Virtual Assistants with IoT, 2016) discloses a framework for improving and extending the functionality of Virtual Assistants by incorporating them with a network of smart devices by means of the Internet Of Things.
Kim et al (US 10148807 B2) discloses an electronic device and method of voice command processing therefor. The electronic device may include: a housing having a surface; a display disposed in the housing and exposed through the surface; an audio input interface comprising audio input circuitry disposed in the housing; an audio output interface comprising audio output circuitry disposed in the housing; at least one wireless communication circuit disposed in the housing and configured to select one of plural communication protocols for call setup; a processor disposed in the housing and electrically connected with the display, the audio input interface, the audio output interface, the at least one wireless communication circuit, and a codec; and a memory electrically connected with the processor. The memory may store at least one codec supporting multiple modes associated with different frequency bands. The memory may store instructions that, when executed, cause the processor to perform operations comprising: setting up a call using the wireless communication circuit, selecting one of the multiple modes based on the selected communication protocol, selecting a speech recognition model based on the selected mode, receiving a voice command from the outside of the electronic device using the wireless communication circuit, and processing the voice command using the selected speech recognition model.
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/C.R.J./Examiner, Art Unit 2128 /KYLE R STORK/Primary Examiner, Art Unit 2128
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