DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because the term “Disclosed” is used. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Drawings
The drawings filed 5/1/2024 were accepted.
Claim Objections
Claim 20 is objected to because of the following informalities: "the graphical user interface comprises a accept function" should be "the graphical user interface comprises an accept function." Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 9 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 9 depends on claim 9, which is not a “claim previously set forth”. For the purposes of the following rejections, examiner is considering it as depending on claim 7. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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, 6-9, and 11-13 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to an abstract idea without significantly more. The claims recite the abstract idea of determining an engagement driver identification and generating notification content data.
Step 2A, Prong 1
The limitations that describe the determining an engagement driver identification and generating notification content data are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claims also include elements of a system with a processor and memory, capturing data over a network, and packaging and outputting the notification content data, however nothing in the claims precludes the steps from practically being performed in the mind.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application because the additional elements regarding a system with a processor and memory, capturing data over a network, and packaging and outputting the notification content data are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the extrasolutionary elements are not considered significantly more than just applying the steps of determining an engagement driver identification and generating notification content data.
Step 2B
In addition to the abstract idea, the claims have the system with a processor and memory, capturing data over a network, and packaging and outputting the notification content data, but they represent only well-understood, routine, conventional activity that can be performed on generic computers. The system with a processor and memory are considered mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The receiving (capturing) of data has been recognized by the courts as being well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) and MPEP 2106.05(d), subsection II. Sethi et al (US20220116470A1; filed 10/8/2020) discloses how well-understood, routine, and conventional the packaging and outputting a notification is: paragraph 3: “Currently, systems are in place which treat all users equally with respect to sending notifications…” The claims are not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 2, this claim recites the same determining step as claim 1, and is rejected similarly. This claim also recites an additional element of a neural network.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding a neural network are considered mere instructions to apply an exception using generic computer components. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the neural network are not considered significantly more than the judicial exception. The neural network is considered mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 3, this claim recites an additional element of a support vector machine network.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding a support vector machine network are considered mere instructions to apply an exception using generic computer components. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the support vector machine network are not considered significantly more than the judicial exception. The support vector machine network is considered mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 4, this claim recites an additional element of a ConvNet architecture.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding a ConvNet architecture are considered mere instructions to apply an exception using generic computer components. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the ConvNet architecture are not considered significantly more than the judicial exception. The ConvNet architecture is considered mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 6, this claim recites an additional element of displaying a notification via a user interface.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding a displaying a notification via a user interface e are considered mere instructions to apply an exception using generic computer components. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the displaying a notification via a user interface are not considered significantly more than the judicial exception. Sethi et al (US20220116470A1; filed 10/8/2020) discloses how well-understood, routine, and conventional the packaging and outputting a notification via a user interface is: paragraph 29: “As used herein, a “click” is to be broadly construed to refer to any affirmative action by a user on a user interface to open or close a window to view or prevent viewing of a notification or other message displayed on a user interface of a user device 102.” The claims are not patent eligible.
As per claim 7, this claim has similar neural network as claim 2, and generation step as claim 1 and is rejected similarly to claims 2 and 1 respectively.
As per claim 8, this claim has similar support vector machine as claim 3 and is rejected similarly to claim 3.
As per claim 9, this claim has similar ConvNet architecture as claim 4 and is rejected similarly to claim 4.
As per claim 11, this claim has similar determining an engagement driver identification, generating notification content data, system with a processor and memory, capturing data over a network, and packaging and outputting the notification content data (this includes both the alert transmission in (b) and step (e)) as claim 1 and is rejected similarly to claim 1. Claim 11 also recites an additional abstract idea of comparing data (comparing the received system configuration data and user security data against stored system configuration data and stored user security data). The comparing of data is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 12, this claim has similar capturing of data and determining as claim 1 and is rejected similarly to claim 1. This claim recites an additional element of an interactive voice response system.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding the interactive voice response system are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the interactive voice response system are not considered significantly more than the judicial exception. The additional elements represent only well-understood, routine, conventional activity that can be performed on generic computer systems. Trim et al (US20200304636A1; filed 3/19/2019) teaches how well understood, routine, and conventional the interactive voice response system is: Paragraph 3: “A proxy virtual agent receives a set of user interactions with a mobile device. The proxy virtual agent automatically routes through an interactive voice response system based on the received set of user interactions with the mobile device.” The claims are not patent eligible.
As per claim 13, this claim has similar displaying of notifications as claim 6 and is rejected similarly to claim 6.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 6 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sethi et al (hereinafter Sethi; US20220116470A1; filed 10/8/2020).
With regards to claim 1, Sethi discloses 1. A system for building a customized conditional interface comprising a first computing device that comprises a first processor and a first memory device storing data and executable code (Sethi, Fig. 1: the notification prediction and generation platform 110; paragraph 21: “Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.””) that, when executed, causes the first processor to:
(a) capture system configuration data and network activity data from a remote user computing device (Sethi, paragraph 29: “the data collection component 121 of the data processing engine 120 collects data pertaining to user actions in connection with application changes (e.g., installations and/or upgrades) and user responses to notifications received in connection with the application changes.” Fig. 1: the users are considered remote because they are on the other side of the network from the notification prediction and generation platform 110);
(b) determine an engagement driver identification (Sethi, paragraph 6: “predict whether a user should receive a given notification;” the “engagement driver identification” is being interpreted as whether the user should be shown the associated content) using the system configuration data and the network activity data as well as end user data loaded from an end user database (Sethi, paragraph 6: “extracting data pertaining to a plurality of user actions in connection with one or more changes to one or more of a plurality of applications;” also see data collection component 121 as described in paragraphs 29-30);
(c) generate notification content data based on the engagement driver identification and the end user data (Sethi, abstract: “In response to predicting that the user should receive the given notification, content of the given notification is determined;” paragraph 11: “determining notifications to send to particular users based on historical datasets”); and
(d) package the notification content data with interface assembly instruction software code that, when executed by a user computing device, outputs the notification content data to a display device integrated with the end user computing device (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user”).
With regards to claim 6, which depends on claim 1, Sethi discloses wherein executing the interface assembly instruction software code by the user computing device causes the device to display the notification content data on a graphical user interface that is output to the display device (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user;” paragraph 29: “a notification or other message displayed on a user interface of a user device 102”).
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.
Claim(s) 2-4 and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sethi in view of Kumar et al (US20190250891A1; filed 6/1/2018).
With regards to claim 2, which depends on claim 1, Sethi discloses (a) the first computing device comprises at least one…; and (b) the at least one… is used to determine the engagement driver identification (Sethi, abstract: “The one more machine learning models are used to predict whether a user should receive a given notification”).
However, Sethi does not disclose at least one neural network.
Kumar et al teaches at least one neural network (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 3, which depends on claim 2, Sethi does not disclose wherein the at least one neural network is configured with a support vector machine network architecture.
Kumar et al teaches wherein the at least one neural network is configured with a support vector machine network architecture (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a support vector machine. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 4, which depends on claim 2, Sethi does not disclose wherein the at least one neural network comprises a ConvNet architecture.
Kumar et al teaches wherein the at least one neural network comprises a ConvNet architecture (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a convolutional neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 5, which depends on claim 2, Sethi discloses wherein running the executable code stored to a second memory device causes a second processor (Sethi, paragraph 22: “At least a portion of the available services and functionalities provided by the notification prediction and generation platform 110 in some embodiments may be provided under Function-as-a-Service (“FaaS”) and/or Platform-as-a-Service (“PaaS”) models, including cloud-based FaaS and PaaS environments;” in the cloud based system, the code can be stored and run on a number of different devices with processors) to:
(a) capture notification content data transmitted to the user computing device (Sethi, paragraph 45: “the machine learning engine 230, like the machine learning engine 130 in FIG. 1, periodically receives new training data 228 based on recent user activity and recent responses to received notifications”);
(b) create a notification database record comprising the network activity data, the system configuration data, end user account data, and the notification content data (Sethi, paragraph 30: “The data collection component 121 also collects data from technical support devices 105 and/or from user clickstreams about whether and when technical support tickets have been opened and/or technical support has been contacted in response to problems with installations and/or upgrades and/or in response to notifications about problems with installations and/or upgrades;” paragraph 31: “The data pre-processing component 122 also compiles the data from the user devices 102, IT administrative devices 103 and/or technical support devices 105 to generate combined datasets that can be processed by the feature engineering and similarity calculation components 123 and 124, and the machine learning engine 130.”);
(c) generate known labeling data using the notification database record (Sethi, paragraph 44: “Similar to the data pre-processing component 122 in FIG. 1, a data pre-processing component 222 prepares the collected data for processing by the features engineering component 223, which determines the characteristics of the various applications and tags and/or classifies the characteristics into different categories representing the functionalities of the applications”);
… comparing the notification content data to the known labeling data; and (e) training the… by adjusting one or more… parameters (Sethi, paragraph 45: “the machine learning engine 230, like the machine learning engine 130 in FIG. 1, periodically receives new training data 228 based on recent user activity and recent responses to received notifications. For example, the new training data 228 comprises user behavior data similar to that described in connection with FIG. 3, and is provided to the machine learning engine 230 to periodically retrain the machine learning engine 230 over short periods of time (e.g., every couple of weeks) so that the machine learning engine 230 (or 130) is equipped to generate predictions in real-time from a pre-trained model that has been consistently retrained”).
However, Sethi does not disclose (d) generate an error rate by comparing the… data; and (e) training the at least one neural network by adjusting one or more neural network parameters to reduce the error rate.
Kumar et al teaches (d) generate an error rate by comparing the… data; and (e) training the at least one neural network by adjusting one or more neural network parameters to reduce the error rate (Kumar et al, paragraph 114: “A loss function value (e.g., an error) may be determined based on the known type(s) of UI component(s) in the training image and the classification result(s). The parameters of the classifier may be adjusted to reduce the loss function value”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a neural network, and that the parameters are adjusted to reduce the error rate. This would have made the neural network more accurate (Kumar et al, paragraph 114: “reduce the loss function value”).
With regards to claim 7, which depends on claim 1, Sethi discloses (a) the first computing device comprises at least one…; and (b) the at least one… is used to generate the notification content data (Sethi, abstract: “The one more machine learning models are used to predict whether a user should receive a given notification”).
However, Sethi does not disclose at least one neural network.
Kumar et al teaches at least one neural network (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 8, which depends on claim 7, Sethi does not disclose wherein the at least one neural network is configured with a support vector machine network architecture.
Kumar et al teaches wherein the at least one neural network is configured with a support vector machine network architecture (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a support vector machine. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 9, which depends on claim 7 (Note: the claim states it depends on claim 9, however based on the context, examiner is treating it as depending on claim 7 for the sake of this rejection), Sethi does not disclose wherein the at least one neural network comprises a ConvNet architecture.
Kumar et al teaches wherein the at least one neural network comprises a ConvNet architecture (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Kumar et al such that the machine learning model was a convolutional neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
Claim(s) 10, 14, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sethi in view of Trim et al (US20200304636A1; filed 3/19/2019).
With regards to claim 10, which depends on claim 1, Sethi discloses (a) the user computing device processes the notification content data and the interface assembly instructions to generate the graphical user interface (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user;” paragraph 29: “a notification or other message displayed on a user interface of a user device 102”);
(b) the graphical user interface comprises an accept function (Sethi, paragraph 29: “As used herein, a “click” is to be broadly construed to refer to any affirmative action by a user on a user interface to open or close a window to view or prevent viewing of a notification or other message displayed on a user interface of a user device 102.”).
However, Sethi does not disclose wherein (c) when the accept function is selected by an end user, the first computing device transmits an alert to an agent computing device permitting an engagement between the end user and an agent to proceed.
Trim et al teaches (c) when the accept function is selected by an end user, the first computing device transmits an alert to an agent computing device permitting an engagement between the end user and an agent to proceed (Trim et al, abstract: “An interactive voice response system is automatically routed through based on the received set of user interactions with the mobile device.” Paragraph 3: “A proxy virtual agent receives a set of user interactions with a mobile device. The proxy virtual agent automatically routes through an interactive voice response system based on the received set of user interactions with the mobile device.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Trim et al such that Sethi’s IT support (described in at least paragraph 24) included virtual agents. This would have enabled the IT support to be performed automatically (Trim et al, paragraph 25: “clients 110, 112, and 114 may include a proxy virtual agent that automatically composes and structures communications with a virtual assistant or IVR system of the customer support center to resolve issues experienced by users of clients 110, 112, and 114 based on recorded user interactions with clients 110, 112, and 114 and identified characteristics of the recorded user interactions generated by the proxy virtual agent.”).
With regards to claim 14, Sethi discloses a system for building a customized conditional input interface comprising a first computing device that comprises a first processor and a first memory device storing data and executable code that, when executed, causes the first processor to (Sethi, Fig. 1: the notification prediction and generation platform 110; paragraph 21: “Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.””):…
(c) generate an engagement driver identification using the … (Sethi, paragraph 6: “predict whether a user should receive a given notification;” the “engagement driver identification” is being interpreted as whether the user should be shown the associated content);
(d) capture system configuration data and network activity data from a remote user computing device (Sethi, paragraph 29: “the data collection component 121 of the data processing engine 120 collects data pertaining to user actions in connection with application changes (e.g., installations and/or upgrades) and user responses to notifications received in connection with the application changes.” Fig. 1: the users are considered remote because they are on the other side of the network from the notification prediction and generation platform 110);
(e) generate notification content data based on the engagement driver identification and end user data loaded from the first memory device (Sethi, abstract: “In response to predicting that the user should receive the given notification, content of the given notification is determined;” paragraph 11: “determining notifications to send to particular users based on historical datasets”); and
(f) package the notification content data with interface assembly instruction software code that, when executed by a user computing device, outputs the notification content data and an accept input function to a display device integrated with the end user computing device (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user;” paragraph 29: “As used herein, a “click” is to be broadly construed to refer to any affirmative action by a user on a user interface to open or close a window to view or prevent viewing of a notification or other message displayed on a user interface of a user device 102”).
However, Sethi does not disclose (a) activate a digital recorder that stores audio data from an engagement between an agent and an end user; (b) convert the audio data to machine encoded communication elements… machine encoded communication elements.
Trim et al teaches (a) activate a digital recorder that stores audio data from an engagement between an agent and an end user (Trim et al, abstract: “An interactive voice response system is automatically routed through based on the received set of user interactions with the mobile device.” Paragraph 3: “A proxy virtual agent receives a set of user interactions with a mobile device. The proxy virtual agent automatically routes through an interactive voice response system based on the received set of user interactions with the mobile device.”); (b) convert the audio data to machine encoded communication elements… machine encoded communication elements (Trim et al, paragraph 58: “The proxy virtual agent sends converted speech to text information, active application information, and other related information to a hierarchical or parallel classifier, along with an in-scope boundary definition (e.g., timelines, information privacy definitions, and the like)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Trim et al such that Sethi’s IT support (described in at least paragraph 24) included virtual agents. This would have enabled the IT support to be performed automatically (Trim et al, paragraph 25: “clients 110, 112, and 114 may include a proxy virtual agent that automatically composes and structures communications with a virtual assistant or IVR system of the customer support center to resolve issues experienced by users of clients 110, 112, and 114 based on recorded user interactions with clients 110, 112, and 114 and identified characteristics of the recorded user interactions generated by the proxy virtual agent.”).
With regards to claim 18, which depends on claim 14, Sethi discloses wherein executing the interface assembly instruction software code by the user computing device causes the device to display the notification content data on a graphical user interface that is output to the display device (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user;” paragraph 29: “a notification or other message displayed on a user interface of a user device 102”).
With regards to claim 20, which depends on claim 14, Sethi discloses (a) the user computing device processes the notification content data and the interface assembly instructions to generate a graphical user interface (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user;” paragraph 29: “a notification or other message displayed on a user interface of a user device 102”);
(b) the graphical user interface comprises a accept function (Sethi, paragraph 29: “As used herein, a “click” is to be broadly construed to refer to any affirmative action by a user on a user interface to open or close a window to view or prevent viewing of a notification or other message displayed on a user interface of a user device 102.”).
However, Sethi does not disclose wherein (c) when the accept function is selected by an end user, the first computing device transmits an alert to an agent computing device permitting an engagement between the end user and an agent to proceed.
Trim et al teaches (c) when the accept function is selected by an end user, the first computing device transmits an alert to an agent computing device permitting an engagement between the end user and an agent to proceed (Trim et al, abstract: “An interactive voice response system is automatically routed through based on the received set of user interactions with the mobile device.” Paragraph 3: “A proxy virtual agent receives a set of user interactions with a mobile device. The proxy virtual agent automatically routes through an interactive voice response system based on the received set of user interactions with the mobile device.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Trim et al such that Sethi’s IT support (described in at least paragraph 24) included virtual agents. This would have enabled the IT support to be performed automatically (Trim et al, paragraph 25: “clients 110, 112, and 114 may include a proxy virtual agent that automatically composes and structures communications with a virtual assistant or IVR system of the customer support center to resolve issues experienced by users of clients 110, 112, and 114 based on recorded user interactions with clients 110, 112, and 114 and identified characteristics of the recorded user interactions generated by the proxy virtual agent.”).
Claim(s) 11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sethi in view of Sardinas (US20230319028A1; filed 3/31/2022).
With regards to claim 11, Sethi discloses a system for building a customized conditional input interface comprising a first computing device that comprises a first processor and a first memory device storing data and executable code that, when executed, causes the first processor to (Sethi, Fig. 1: the notification prediction and generation platform 110; paragraph 21: “Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.””):
(a) capture from a user computing device, (i) a user interface transmit command for user interface data utilized by the user computing device to generate a graphical user interface, (ii) system configuration data, (iii) network activity data (Sethi, paragraph 29: “the data collection component 121 of the data processing engine 120 collects data pertaining to user actions in connection with application changes (e.g., installations and/or upgrades) and user responses to notifications received in connection with the application changes.”)…
(c) determine an engagement driver identification (Sethi, paragraph 6: “predict whether a user should receive a given notification;” the “engagement driver identification” is being interpreted as whether the user should be shown the associated content) using the system configuration data, the network activity data, and the end user data (Sethi, paragraph 6: “extracting data pertaining to a plurality of user actions in connection with one or more changes to one or more of a plurality of applications;” also see data collection component 121 as described in paragraphs 29-30);
(d) generate notification content data based on the engagement driver identification and the end user data (Sethi, abstract: “In response to predicting that the user should receive the given notification, content of the given notification is determined;” paragraph 11: “determining notifications to send to particular users based on historical datasets”); and
(e) package the notification content data with interface assembly instruction software code that, when executed by a user computing device, outputs the notification content data to a display device integrated with the end user computing device (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user”).
However, Sethi does not disclose capture… (iv) user security data; (b) process the system configuration data by comparing the received system configuration data and user security data against stored system configuration data and stored user security data, wherein (i) if the captured data does not match the stored data, an alert is transmitted to the user computing device, and (ii) if the captured data does match the stored data, the processor loads end user data.
Sardinas teaches capture… (iv) user security data (Sardinas, paragraph 21: “A user at the station 130 can provide credentials through a keyboard or an app can fill in credential automatically at a log in screen. For example, username and password may be requested”); (b) process the system configuration data by comparing the received system configuration data and user security data against stored system configuration data and stored user security data (Sardinas, paragraph 21: “If the user correctly verifies, access is granted to the station and/or user”), wherein (i) if the captured data does not match the stored data, an alert is transmitted to the user computing device (Sardinas, paragraph 21: “For example, username and password may be requested”), and (ii) if the captured data does match the stored data, the processor loads end user data (Sardinas, paragraph 21: “If the user correctly verifies, access is granted to the station and/or user”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi and Sardinas such that the user is asked for correct security information to access their end user data. This would have protected the user and their data from malicious actors (Sardinas, paragraphs 3-4: “Once a password and username have been compromised, malicious actors can access and damage a user's assets protected by the password. For example, one person may eavesdrop to find out another person's credentials. Automated hack robots can use blunt force to repetitively attempt different username and password combinations. Therefore, what is needed is a robust technique for verification level of credentials based on risk when accessing a resource.”).
With regards to claim 13, which depends on claim 1, Sethi discloses wherein executing the interface assembly instruction software code by the user computing device causes the device to display the notification content data on a graphical user interface that is output to the display device (Sethi, paragraph 6: “The method further includes generating the given notification for the user, and transmitting the given notification to the user;” paragraph 29: “a notification or other message displayed on a user interface of a user device 102”).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sethi in view of Sardinas, and further in view of Trim et al.
With regards to claim 12, which depends on claim 11, Sethi discloses (a) the processor captures end user inputs to… system; and (b) the end user inputs are also used to determine the engagement driver identification (Sethi, paragraph 45: “the machine learning engine 230, like the machine learning engine 130 in FIG. 1, periodically receives new training data 228 based on recent user activity and recent responses to received notifications. For example, the new training data 228 comprises user behavior data similar to that described in connection with FIG. 3, and is provided to the machine learning engine 230 to periodically retrain the machine learning engine 230 over short periods of time;” since the user inputs are used to train the ML model that determines the engagement driver identification (the “engagement driver identification” is still being interpreted as whether the user should be shown the associated content), they are indirectly used to determine the engagement driver identification).
However, Sethi and Sardinas do not disclose user inputs to an interactive voice response system.
Trim et al teaches user inputs to an interactive voice response system (Trim et al, abstract: “An interactive voice response system is automatically routed through based on the received set of user interactions with the mobile device.” Paragraph 3: “A proxy virtual agent receives a set of user interactions with a mobile device. The proxy virtual agent automatically routes through an interactive voice response system based on the received set of user interactions with the mobile device.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi, Sardinas, and Trim et al such that Sethi’s IT support (described in at least paragraph 24) included virtual agents. This would have enabled the IT support to be performed automatically (Trim et al, paragraph 25: “clients 110, 112, and 114 may include a proxy virtual agent that automatically composes and structures communications with a virtual assistant or IVR system of the customer support center to resolve issues experienced by users of clients 110, 112, and 114 based on recorded user interactions with clients 110, 112, and 114 and identified characteristics of the recorded user interactions generated by the proxy virtual agent.”).
Claim(s) 15-17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sethi in view of Trim et al, and further in view of Kumar et al.
With regards to claim 15, which depends on claim 14, Sethi discloses wherein the first computing device comprises a first… generate the engagement driver identification (Sethi, abstract: “The one more machine learning models are used to predict whether a user should receive a given notification”).
However, Sethi and Trim et al do not disclose at least one neural network.
Kumar et al teaches at least one neural network (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi, Trim et al, and Kumar et al such that the machine learning model was a neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 16, which depends on claim 15, Sethi and Trim et al do not disclose wherein the neural network is implemented with a ConvNet architecture.
Kumar et al teaches wherein the neural network is implemented with a ConvNet architecture (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi, Trim et al, and Kumar et al such that the machine learning model was a convolutional neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 17, which depends on claim 15, Sethi and Trim do not disclose wherein the neural network is implemented with a recurrent neural network having at least three intermediate layers.
However, Kumar teaches wherein the neural network is implemented with a recurrent neural network having at least three intermediate layers (Kumar et al, paragraph 127: “a neural network based classifier may include some layers for feature extraction and some layers (e.g., fully-connected layers) for classification”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi, Trim et al, and Kumar et al such that the machine learning model was a convolutional neural network with multiple layers. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
With regards to claim 19, which depends on claim 14, Sethi discloses (a) the first computing device comprises at least one…; and (b) the at least one… is used to generate the notification content data (Sethi, abstract: “The one more machine learning models are used to predict whether a user should receive a given notification”).
However, Sethi and Trim et al do not disclose at least one neural network.
Kumar et al teaches at least one neural network (Kumar et al, paragraph 7: “the UI components may be detected and classified by a machine learning-based classifier (e.g., a support vector machine classifier or a convolutional neural network-based classifier)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Sethi, Trim et al, and Kumar et al such that the machine learning model was a neural network. This would have enabled the invention to identify UI interactions using screen images (Kumar et al, paragraph 7: “identify UI components present in the screen image and the locations of the detected UI components in the image. Additionally, the learning-based classifier may be configured to identify a type for each detected UI component and/or a function associated with each detected UI component. Text content items in the GUI screen image and their corresponding locations may also be detected and recognized.”).
Conclusion
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/B.C.A/Examiner, Art Unit 2178 /STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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