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 .
Status of Claims
The following is a final office action.
Claims [25-44] are currently pending and have been examined on their merits.
Claims 25, 32, and 39 are newly amended see REMARKS March 09, 2026.
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 25-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claim 25-31 recites a system, claims 32-38 recite a method (i.e. a process such as an act or series of steps) and claims 39-44 recite a non-transitory computer readable medium and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 25, 32, and 39: A method, comprising: receiving, first profile data associated with a first profile of a plurality of profiles, each profile associated with an electronic account, wherein the first profile data comprises a plurality of data categories including transitions, prior positions, certifications, skills, and past responsibilities; providing, the first profile data as input, wherein providing causes generate one or more weighted inputs; generating, at least one output associated with the first profile by apply an activation function to a net input determined based on the one or more weighted inputs; generating, based on the at least one output, a data graph comprising a plurality of graph nodes representing a plurality of tasks and a plurality of edges connecting the plurality of graph nodes; generating, a first graphical representation of the first profile based on a first subset of the plurality of graph nodes corresponding to the first profile data; receiving, second profile data associated with a second profile of the plurality of profiles, wherein the second profile data comprises the plurality of data categories; generating, a second graphical representation of the second profile based on a second subset of the plurality of graph nodes corresponding to the second profile data; determine, based on a comparison of the first graphical representation and the second graphical representation, profile to determine similarities between the transitions, prior positions, certifications, skills, and past responsibilities of the first profile and the second profile; identify, based on the comparison of the first graphical representation and the second graphical representation, one or more gaps in at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile indicated by the first subset of the plurality of graph nodes in relation to one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with a position of the second profile indicated by the second subset of the plurality of graph nodes, present one or more graphical elements comprising a plurality of programs configured to address the one or more gaps in the at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile in relation to the one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with the position of the second profile: and based on an execution of one of the plurality of programs configured to address the one or more gaps, present at least one graphical element comprising a filled portion based on a respective fit score of the first profile with the position of the second profile.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure recites managing personal behavior or relationships or interactions between people. The Examiner finds the claims to simply recite a method of receiving user information, receiving second user information, comparing the first and second profile information to determine similarities and generating content based on the comparison. The claims recite a series of steps to determine a potential career path for a user by receiving and analyzing user information and comparing the information to other or previous users. Therefore, the claims recite a method of managing personal behavior.
Alternatively, the Examiner find the recited claims to recite a mental process. The examiner finds the claims to be similar to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis." The claims merely recite a method for receiving and analyzing first and second user profile data such as transitions, prior positions, skills, certification, and past responsibilities to determine content such as potential career advice for a user. Therefore, the claims recite a mental process as a person such as a career counselor would be capable of mentally or with simple tools such as pen and paper perform the actions of determining career advice for a person based on their personal information and comparing it to other individuals such as previously successful employees. The claims are found to recite concepts the courts have identified as reciting mental processes such as observations, evaluations, and judgements. Therefore, the examiner finds the claims to recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 25: A system, comprising: one or more processors, coupled with memory, that execute a machine learning-based modeling program, access a neural network comprising a visible node layer and a hidden node layer, wherein the visible node layer comprises a plurality of visible nodes connected to a plurality of hidden nodes of the hidden node layer, provide the first profile data as input to the plurality of visible nodes of the visible node layer of the neural network, wherein the one or more processors cause the plurality of hidden nodes of the hidden node layer of the neural network to generate one or more weighted inputs, updating, by the one or more processors, the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected training algorithm to an output of the comparison of the first graphical representation profile and the second graphical representation profile to improve performance of the neural network in generation of subsequent outputs from the neural network; transmit, to a client device, data to cause the client device to display, on an output device, a graphical user interface with profile data generated based on the subsequent outputs from the neural network that is updated with the adjusted one or more weights that improve the performance of the neural network, construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network; transmit, to the client device, data to cause the client device to update the graphical user interface, wherein the system is implemented using at least one of a graphical processing unit, an application-specific integrated circuit, or a programmable logic device configured for machine learning operations.
Claim 32: one or more processors coupled with memory, access a neural network comprising a visible node layer and a hidden node layer, wherein the visible node layer comprises a plurality of visible nodes connected to a plurality of hidden nodes of the hidden node layer, provide the first profile data as input to the plurality of visible nodes of the visible node layer of the neural network, wherein the one or more processors cause the plurality of hidden nodes of the hidden node layer of the neural network to generate one or more weighted inputs, updating, by the one or more processors, the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected training algorithm to an output of the comparison of the first graphical representation profile and the second graphical representation profile to improve performance of the neural network in generation of subsequent outputs from the neural network; transmit, to a client device, data to cause the client device to display, on an output device, a graphical user interface with profile data generated based on the subsequent outputs from the neural network that is updated with the adjusted one or more weights that improve the performance of the neural network, construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network; transmit, to the client device, data to cause the client device to update the graphical user interface, wherein the system is implemented using at least one of a graphical processing unit, an application-specific integrated circuit, or a programmable logic device configured for machine learning operations.
Claim 39: A non-transitory computer-readable medium storing processor executable instructions, that upon execution by one or more processors, access a neural network comprising a visible node layer and a hidden node layer, wherein the visible node layer comprises a plurality of visible nodes connected to a plurality of hidden nodes of the hidden node layer, provide the first profile data as input to the plurality of visible nodes of the visible node layer of the neural network, wherein the one or more processors cause the plurality of hidden nodes of the hidden node layer of the neural network to generate one or more weighted inputs, updating, by the one or more processors, the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected training algorithm to an output of the comparison of the first graphical representation profile and the second graphical representation profile to improve performance of the neural network in generation of subsequent outputs from the neural network; transmit, to a client device, data to cause the client device to display, on an output device, a graphical user interface with profile data generated based on the subsequent outputs from the neural network that is updated with the adjusted one or more weights that improve the performance of the neural network, construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network; transmit, to the client device, data to cause the client device to update the graphical user interface, wherein the system is implemented using at least one of a graphical processing unit, an application-specific integrated circuit, or a programmable logic device configured for machine learning operations.
The additional element of using generic computer elements and generic neural network elements to perform the abstract idea are directed to merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a method for transmitting, receiving, and processing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As the claims are merely directed to utilizing a computer to perform a method of gathering and analyzing information to present recommendations based on the analysis are not significant improvements to the functionality of a generic computer and are directed to merely “apply it” or applying the abstract idea on a computer. Additionally, the claims merely recite a generic application of a neural network to perform the abstract idea of receiving and processing data and performing the generic function of updating a neural network to generate subsequent outputs. As a neural network is understood to routinely be updated and continuously generate updates outputs the additional elements do not amount to significantly more.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Therefore, the additional element do not amount to significantly more as they do not recite an improvement in a technology or technical field.
The dependent claims 26-31, 33-38, and 40-44 further narrow the abstract idea of determining content based on comparing the representation of a first and second profile recited in the independent claims 25, 32, and 39 and are therefore directed towards the same abstract idea.
Claims 29, 36, and 43 further recites the additional elements of at least one of a restricted Boltzmann machine, a deep Boltzmann machine, a deep belief network, a convolutional neural network, a spiking neural network, or a recurrent neural network, however the examiner finds these elements to be directed to merely “apply it” or utilizing a generic computer to perform a generic function and not reciting an improvement to a technology.
Therefore, claims 25-44 are rejected under 35 U.S.C. 101.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 25-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Siddiqui (US 2019/0228288) in view of Xie (US 2019/0303798) further in view of Sallee (2020/0134376) even further in view of Jarrett (US 2016/0196534).
Claim 25, 32, and 39: Siddiqui discloses (Claim 25) A system, comprising: one or more processors, coupled with memory, that execute a machine learning-based modeling program to: (Claim 32) A method, comprising: (Claim 39) A non-transitory computer-readable medium storing processor executable instructions, that upon execution by one or more processors, cause the one or more processors to: receive first profile data associated with a first profile of a plurality of profiles, each profile associated with an electronic account, wherein the first profile data comprises a plurality of data categories including transitions, prior positions, certifications, skills, and past responsibilities (Paragraph [0005-0006]; [0008-0010]; [0053]; [0100]; Fig. 4, as part of any journey mapping or use case mapping, customers can perform “what-if” analysis against a certain set of parameters, which help them identify various possible outcomes. An example embodiment is a platform that uses designs and algorithms which allows students and employers the ability to segment, model, educate, and employ based on the various changing parameters. An example embodiment of a system for decision modeling of a personal temporal path comprises an interface system for receiving external data. Receiving personal data of a user from the interface system includes at least one user temporal event which corresponds to at least one of the network layers. In an example, personal data comprises certifications, or credential information and/or trade skills. The personal data can be entered into the data lake and pulled through to the temporal path neural network. The system determines using the temporal path neural network that at least one memory contains a respective user profile of a user having a respective temporal path that matches one of the optimal temporal paths of the user);
access a neural network comprising a visible node layer and a hidden node layer, wherein the visible node layer comprises a plurality of visible nodes connected to a plurality of hidden nodes of the hidden node layer (Paragraph [0008-0010]; [0025-0026]; Fig. 1C, an example embodiment of a system for decision modelling of a personal temporal path comprises an interface system for receiving external data, at least one memory, and a processing system. The processing system is in communication with the interface system and at least one memory configured to define more than one network layers of a temporal path neural network, at least two of the neural network layers representing different temporal events at different times in the personal temporal path, receive the external data from the interface system that includes at least one event corresponding to the at least one network layer, train the temporal path neural network with the received external data, receive personal data of a user, and determine a respective optimal temporal path within the temporal neural network. A neural network consists of several layers, but typical neural networks contain; an input layer, an output layer, the typical neural network usually includes one or more in-between layers (sometimes referred to as hidden layers) between the input and output layers. In some existing neural networks, the system itself assigns and adjusts the weights in order to correctly correlate input and outputs using a training algorithm and a training data set);
provide the first profile data as input to the plurality of visible nodes of the visible node layer of the neural network, wherein the one or more processors cause the plurality of hidden nodes of the hidden node layer of the neural network to generate one or more weighted inputs (Paragraph [0035-0037]; [0042-0043]; Fig. 1B, interfaces are developed to plug-in to the data repositories on one side and data lake on the other side (collectively referred to as the interface system or integration layer). Personal data of a user may be received from the interface system. Using the interfaces, external data is pushed to a data lake, wherein the processing system may be configured to sort the external data and position data points into the temporal path neural network. Reports are generated by the processing system based on weights of the temporal neural network. The weights within the temporal path neural network can be determined by the temporal path neural network itself using an algorithm applied to a set of training data. In an example, the temporal neural network is trained with the stored external data. The temporal path neural network can also be trained with at least some of the data stored in the data lake, which data was received by the data lake via interfaces from the external data sources);
generate, using the neural network, at least one output associated with the first profile by causing the plurality of hidden nodes to apply an activation function to a net input determined based on the one or more weighted inputs (Paragraph [0035-0037]; [0042-0043]; [0054-0055]; Fig. 1B, interfaces are developed to plug-in to the data repositories on one side and data lake on the other side (collectively referred to as the interface system or integration layer). Personal data of a user may be received from the interface system. Using the interfaces, external data is pushed to a data lake, wherein the processing system may be configured to sort the external data and position data points into the temporal path neural network. Reports are generated by the processing system based on weights of the temporal neural network. The weights within the temporal path neural network can be determined by the temporal path neural network itself using an algorithm applied to a set of training data. In an example, the temporal neural network is trained with the stored external data. The temporal path neural network can also be trained with at least some of the data stored in the data lake, which data was received by the data lake via interfaces from the external data sources. A list of temporal events that may follow the temporal events in the personal data received is provided to the interface by the system, which is received by the user. For example, in a career environment, the interface system may provide a list of programs that are capable of acceptance based on personal data. The user will receive data that is output from the system via the interface system. The outputs can be based on particular weightings scores determine within the temporal path neural network, by the processing system, from the personal data of the user entered);
generate, based on the at least one output, a data graph comprising a plurality of graph nodes representing a plurality of tasks and a plurality of edges connecting the plurality of graph nodes (Paragraph [0005-0006]; [0008-0010]; [0031]; Fig. 1B, as part of any journey mapping or use case mapping, customers can perform “what-if” analysis against a certain set of parameters, which help them identify various possible outcomes. An example embodiment is a platform that uses designs and algorithms which allows students and employers the ability to segment, model, educate, and employ based on the various changing parameters. An example embodiment of a system for decision modeling of a personal temporal path comprises an interface system for receiving external data. Receiving personal data of a user from the interface system includes at least one user temporal event which corresponds to at least one of the network layers. In an example, personal data comprises certifications, or credential information and/or trade skills. The personal data can be entered into the data lake and pulled through to the temporal path neural network. The computer receives personal data of a user from the interface system which corresponds to at least one network layer and trains the temporal path neural network with the received external data. The computer then provides to the interface system at least one of the journey outcomes, the respective optimal temporal path, and information based on probabilistic attributes of the respective optimal temporal path. A temporal path neural network is defined by the system. The temporal path which includes a journey outcome, for example for a career journey outcome. A temporal path can be defined using neural network layers, each layer representing temporal events or facts. At least some of the temporal events can be temporally separate in time. In other works the various inputs, which may include temporal facts/events such as personality, desired journey outcome, etc. and/or outputs of at least some events, which may include optimal temporal path may take place at different times. In an example the layers of the neural network are in a sequential order. (The examiner notes that the broadest reasonable interpretation of task nodes would include a temporal path which defines events in a chronological order to lead a user to a desired outcome));
generate, using the neural network, a first graphical representation of the first profile based on a first subset of the plurality of graph nodes corresponding to the first profile data (Paragraph [0005-0006]; [0008-0010]; [0031]; Fig. 1B, as part of any journey mapping or use case mapping, customers can perform “what-if” analysis against a certain set of parameters, which help them identify various possible outcomes. An example embodiment is a platform that uses designs and algorithms which allows students and employers the ability to segment, model, educate, and employ based on the various changing parameters. An example embodiment of a system for decision modeling of a personal temporal path comprises an interface system for receiving external data. Receiving personal data of a user from the interface system includes at least one user temporal event which corresponds to at least one of the network layers. In an example, personal data comprises certifications, or credential information and/or trade skills. The personal data can be entered into the data lake and pulled through to the temporal path neural network. The computer receives personal data of a user from the interface system which corresponds to at least one network layer and trains the temporal path neural network with the received external data);
receive second profile data associated with a second profile of the plurality of profiles, wherein the second profile data comprises the plurality of data categories (Paragraph [0005-0006]; [0008-0010]; [0031]; [0100-0101]; Fig. 1B, as part of any journey mapping or use case mapping, customers can perform “what-if” analysis against a certain set of parameters, which help them identify various possible outcomes. An example embodiment is a platform that uses designs and algorithms which allows students and employers the ability to segment, model, educate, and employ based on the various changing parameters. An example embodiment of a system for decision modeling of a personal temporal path comprises an interface system for receiving external data. Receiving personal data of a user from the interface system includes at least one user temporal event which corresponds to at least one of the network layers. In an example, personal data comprises certifications, or credential information and/or trade skills. The personal data can be entered into the data lake and pulled through to the temporal path neural network. In some embodiments, the system has the ability to match a user with a further user who have an optimal temporal path or selected temporal path in a similar path. The system determines using the temporal path neural network that contains a perspective user profile of at least one further user having a respective further temporal path that matches one of the optimal temporal paths of the user. Upon finding a matching further user may be configured to provide to the interface system the respective further temporal path of the at least one further users);
generate, using the neural network, a second graphical representation of the second profile based on a second subset of the plurality of graph nodes corresponding to the second profile data (Paragraph [0005-0006]; [0008-0010]; [0031]; [0100-0101]; Fig. 1B, as part of any journey mapping or use case mapping, customers can perform “what-if” analysis against a certain set of parameters, which help them identify various possible outcomes. An example embodiment is a platform that uses designs and algorithms which allows students and employers the ability to segment, model, educate, and employ based on the various changing parameters. An example embodiment of a system for decision modeling of a personal temporal path comprises an interface system for receiving external data. Receiving personal data of a user from the interface system includes at least one user temporal event which corresponds to at least one of the network layers. In an example, personal data comprises certifications, or credential information and/or trade skills. The personal data can be entered into the data lake and pulled through to the temporal path neural network. In some embodiments, the system has the ability to match a user with a further user who have an optimal temporal path or selected temporal path in a similar path. The system determines using the temporal path neural network that contains a perspective user profile of at least one further user having a respective further temporal path that matches one of the optimal temporal paths of the user. Upon finding a matching further user may be configured to provide to the interface system the respective further temporal path of the at least one further users);
determine, based on a comparison of the first graphical representation and the second graphical representation, similarities between the transitions, prior positions, certifications, skills, and past responsibilities of the first profile and the second profile (Paragraph [0100-0101]; [0128-0129]; Fig. 1B, the system determines using the temporal path neural network that contains a perspective user profile of at least one further user having a respective further temporal path that matches one of the optimal temporal paths of the user. Upon finding a matching further user may be configured to provide to the interface system the respective further temporal path of the at least one further users. Once users input their key parameters the system can perform a comparative analysis against professional players in the field. The matching can search for similar users in a similar temporal zone for the user, and display the similar users’ temporal path allowing the user to set goals. The system provides the comparative analysis to the user);
transmit, to a client device, data to cause the client device to display, on an output device, a graphical user interface with profile data generated based on the subsequent outputs from the neural network that is updated with the adjusted one or more weights that improve the performance of the neural network (Paragraph [0005-0006]; [0008-0010]; [0031]; [0100-0101]; Fig. 1B, as part of any journey mapping or use case mapping, customers can perform “what-if” analysis against a certain set of parameters, which help them identify various possible outcomes. An example embodiment is a platform that uses designs and algorithms which allows students and employers the ability to segment, model, educate, and employ based on the various changing parameters. An example embodiment of a system for decision modeling of a personal temporal path comprises an interface system for receiving external data. Receiving personal data of a user from the interface system includes at least one user temporal event which corresponds to at least one of the network layers. In an example, personal data comprises certifications, or credential information and/or trade skills. The personal data can be entered into the data lake and pulled through to the temporal path neural network. In some embodiments, the system has the ability to match a user with a further user who have an optimal temporal path or selected temporal path in a similar path. The system determines using the temporal path neural network that contains a perspective user profile of at least one further user having a respective further temporal path that matches one of the optimal temporal paths of the user. Upon finding a matching further user may be configured to provide to the interface system the respective further temporal path of the at least one further users);
construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network (Paragraph [0008-0010]; [0025-0026]; [0050]; [0071]; [0080]; Fig. 1C, an example embodiment of a system for decision modelling of a personal temporal path comprises an interface system for receiving external data, at least one memory, and a processing system. The processing system is in communication with the interface system and at least one memory configured to define more than one network layers of a temporal path neural network, at least two of the neural network layers representing different temporal events at different times in the personal temporal path, receive the external data from the interface system that includes at least one event corresponding to the at least one network layer, train the temporal path neural network with the received external data, receive personal data of a user, and determine a respective optimal temporal path within the temporal neural network. A neural network consists of several layers, but typical neural networks contain; an input layer, an output layer, the typical neural network usually includes one or more in-between layers (sometimes referred to as hidden layers) between the input and output layers. In some existing neural networks, the system itself assigns and adjusts the weights in order to correctly correlate input and outputs using a training algorithm and a training data set. The processors assigns and adjusts the weights and subsequently uses a training algorithm or training data set to correctly correlate input and output. The system may be configured to self-optimize any temporal path based on updating the temporal path neural network by either re-training the network or re-determining the temporal paths);
wherein the system is implemented using at least one of a graphical processing unit, an application-specific integrated circuit, or a programmable logic device configured for machine learning operations (Paragraph [0008-0010]; [0025-0026]; [0050]; [0178]; Fig. 1C, an example embodiment of a system for decision modelling of a personal temporal path comprises an interface system for receiving external data, at least one memory, and a processing system. The processing system is in communication with the interface system and at least one memory configured to define more than one network layers of a temporal path neural network, at least two of the neural network layers representing different temporal events at different times in the personal temporal path, receive the external data from the interface system that includes at least one event corresponding to the at least one network layer, train the temporal path neural network with the received external data, receive personal data of a user, and determine a respective optimal temporal path within the temporal neural network. A neural network consists of several layers, but typical neural networks contain; an input layer, an output layer, the typical neural network usually includes one or more in-between layers (sometimes referred to as hidden layers) between the input and output layers. In some existing neural networks, the system itself assigns and adjusts the weights in order to correctly correlate input and outputs using a training algorithm and a training data set. In an example embodiment, the user interface may connect to the internet and connect the user to the interface via a browser application that allows a user device to input personal data, which data is sent to the system via the interface).
Siddiqui discloses a system of generating a decision model for a user modelling a temporal path based on user data. However, Siddiqui does not specifically disclose the following claim limitations: update the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected training algorithm to an output of the comparison of the first graphical representation and the second graphical representation to improve performance of the neural network in generation of subsequent outputs from the neural network; construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network; identify, based on the comparison of the first graphical representation and the second graphical representation, one or more gaps in at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile indicated by the first subset of the plurality of graph nodes in relation to one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with a position of the second profile indicated by the second subset of the plurality of graph nodes; transmit, to the client device, data to cause the client device to update the graphical user interface to present one or more graphical elements comprising a plurality of programs configured to address the one or more gaps in the at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile in relation to the one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with the position of the second profile; and transmit, to the client device, based on an execution of one of the plurality of programs configured to address the one or more gaps, data to cause the client device to update the graphical user interface to present at least one graphical element comprising a filled portion based on a respective fit score of the first profile with the position of the second profile.
In the same field of endeavor of using a machine learning model to generate a career path recommendation Xie teaches update the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected training algorithm to an output of the comparison of the first graphical representation and the second graphical representation to improve performance of the neural network in generation of subsequent outputs from the neural network. (Paragraph [0018-0021]; [0024]; Fig. 1, first profile and/or usage data is examined to locate features relevant to career path determination. In an example embodiment, a first machine learning algorithm may be used to assign weights to features in the profile and usage data in a career path model. The career path model is therefore trained by the first machine learning algorithm to receive an input candidate’s profile and/or usage data and output a score for one or more potential career paths. This training may be performed by feeding training data, including sample profile and/or usage data into the first machine learning algorithm. Prior to training the career path model the training data may be analyzed to determine the set of potential career paths taken by users in the training data, which can be found by examining the user profiles in the training data. Each of these career paths may be input into the first machine learning algorithm along with the training data in order to learn the weights for the particular career paths).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a potential career path representation model using a neural network as disclosed by Siddiqui (Siddiqui [0008]) with the system of update the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected training algorithm to an output of the comparison of the first graphical representation and the second graphical representation to improve performance of the neural network in generation of subsequent outputs from the neural network as taught by Xie (Xie [0018]). With the motivation of helping to use a trained machine learning model to determine a career path for an individual (Xie [0004]).
In the same field of endeavor of using a neural network to make predictions from data sets Sallee teaches construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network (Paragraph [0016]; [0030-0032]; [0041-0042] neural networks generally receive, as input, a numerical input vector. The numerical input vector is provided to a sequence of layers, and processed at each layer, to generate an output. Machine learning techniques train models to accurately make predictions on data fed into the models. Models may be run against a training dataset for several iterations to refine its results. Once an iteration is run the models are evaluated and the values of their variables are adjusted to attempt to better refine the model in an iterative fashion. A deep neural network is a stacked neural network, which is composed of multiple layers. The layers are composed of nodes. A node combines input from the data with a set of coefficients or weights that either amplify or dampen that input, which assigns significance to inputs for the task the algorithm is trying to learn. A deep neural network uses a cascade of many layers of non-linear processing units for features extraction and transformation).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a potential career path representation model using a neural network as disclosed by Siddiqui (Siddiqui [0008]) with the system of construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network as taught by Sallee (Sallee [0041]). With the motivation of being a simple substitution as Siddiqui discloses a system of using a neural network trained to make predictions for a potential career path for a user based on a plurality of training data and adjusting weights, which can be substituted for a specific and known type of neural network such as a deep learning neural network which uses a stacked neural network to further enhance the model’s ability to make predictions as is taught by Sallee (Sallee [0041]). Additionally, the combination can be used to help improve the models ability to be trained and produce accurate and desired outputs based on specific training techniques (Sallee [0002]).
In the same field of endeavor of guiding a user’s progress towards a career goal Jarrett teaches identify, based on the comparison of the first graphical representation and the second graphical representation, one or more gaps in at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile indicated by the first subset of the plurality of graph nodes in relation to one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with a position of the second profile indicated by the second subset of the plurality of graph nodes (Paragraph [0034]; [0075]; [0082]; [0100]; [0130]; Fig. 17, a system for training, tracking, and placing job seekers in employment opportunities based on determining a competence score for a job seeker with respect to a skill set. The techniques described herein provide various examples of generated personalized user interfaces that provide various functionalities to summarizes information. Based on the competence score associated with a job seeker, the recommendation module may determine and recommend to the job seeker one or more training activities for strengthening one or more of the skills. The recommendation module may determine training activities based at least in part on a gap analysis. The recommendation module may compare threshold scores determine by employers with competence scores associated with individual job seekers to identify gaps. The user information may include a graphical comparison between a job seeker’s competence score and scores with respect to each of the skill in the skill set and a threshold score of a target role. The user interface may present the job seekers information relevant such as how well the job seeker’s scores with respect to each of the skills matches the employer’s predetermined criteria (e.g. %fit). The competence score may be updated during or after the job seeker completes training activities);
transmit, to the client device, data to cause the client device to update the graphical user interface to present one or more graphical elements comprising a plurality of programs configured to address the one or more gaps in the at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile in relation to the one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with the position of the second profile (Paragraph [0034]; [0075]; [0082]; [0100]; [0130]; Fig. 17, a system for training, tracking, and placing job seekers in employment opportunities based on determining a competence score for a job seeker with respect to a skill set. The techniques described herein provide various examples of generated personalized user interfaces that provide various functionalities to summarizes information. Based on the competence score associated with a job seeker, the recommendation module may determine and recommend to the job seeker one or more training activities for strengthening one or more of the skills. The recommendation module may determine training activities based at least in part on a gap analysis. The recommendation module may compare threshold scores determine by employers with competence scores associated with individual job seekers to identify gaps. The user information may include a graphical comparison between a job seeker’s competence score and scores with respect to each of the skill in the skill set and a threshold score of a target role. The user interface may present the job seekers information relevant such as how well the job seeker’s scores with respect to each of the skills matches the employer’s predetermined criteria (e.g. %fit). The competence score may be updated during or after the job seeker completes training activities);
and transmit, to the client device, based on an execution of one of the plurality of programs configured to address the one or more gaps, data to cause the client device to update the graphical user interface to present at least one graphical element comprising a filled portion based on a respective fit score of the first profile with the position of the second profile (Paragraph [0034]; [0075]; [0082]; [0100]; [0130]; Fig. 17, a system for training, tracking, and placing job seekers in employment opportunities based on determining a competence score for a job seeker with respect to a skill set. The techniques described herein provide various examples of generated personalized user interfaces that provide various functionalities to summarizes information. Based on the competence score associated with a job seeker, the recommendation module may determine and recommend to the job seeker one or more training activities for strengthening one or more of the skills. The recommendation module may determine training activities based at least in part on a gap analysis. The recommendation module may compare threshold scores determine by employers with competence scores associated with individual job seekers to identify gaps. The user information may include a graphical comparison between a job seeker’s competence score and scores with respect to each of the skill in the skill set and a threshold score of a target role. The user interface may present the job seekers information relevant such as how well the job seeker’s scores with respect to each of the skills matches the employer’s predetermined criteria (e.g. %fit). The competence score may be updated during or after the job seeker completes training activities).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a potential career path representation model using a neural network as disclosed by Siddiqui (Siddiqui [0008]) with the system of identify, based on the comparison of the first graphical representation and the second graphical representation, one or more gaps in at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile indicated by the first subset of the plurality of graph nodes in relation to one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with a position of the second profile indicated by the second subset of the plurality of graph nodes; transmit, to the client device, data to cause the client device to update the graphical user interface to present one or more graphical elements comprising a plurality of programs configured to address the one or more gaps in the at least one of the transitions, prior positions, certifications, skills, and past responsibilities of the first profile in relation to the one or more expected transitions, prior positions, certifications, skills, and past responsibilities associated with the position of the second profile; and transmit, to the client device, based on an execution of one of the plurality of programs configured to address the one or more gaps, data to cause the client device to update the graphical user interface to present at least one graphical element comprising a filled portion based on a respective fit score of the first profile with the position of the second profile as taught by Jarrett (Jarrett [0075]). With the motivation of helping to provide users with a guide to developing skills for a job opportunity (Jarrett [0002]).
Claim 26, 33, and 40: Modified Siddiqui discloses the system as per claim 25, the method as per claim 32, and the non-transitory computer readable medium as per claim 39. Siddiqui further discloses wherein the one or more processors are further configured to: cause the plurality of hidden nodes to multiply the inputs associated with the first profile data with the one or more weights to create the one or more weighted inputs (Paragraph [0025]; [0054]; [0065] conventional neural network processing is simply based on taking the inner product of a weight vector and the input vector and texting this value against some threshold. In some existing neural network the system itself assignees and adjusts the weights in order to correctly correlate input and output using a training algorithm. The outputs can be based on particular weighting scores determined within the temporal path neural network, by the processing system from the personal data of the user).
Claim 27, 34, and 41: Modified Siddiqui discloses the system as per claim 26, the method as per claim 33, and the non-transitory computer readable medium as per claim 40. Siddiqui further discloses wherein the one or more weights increase or decrease significance of the input, (claim 27 and 41) and the one or more weights are configured to change responsive to an update to the neural network. (Claims 34) and wherein the one or more weights are dynamic and change as the neural network is updated (Paragraph [0054]; [0065]; [0071] the outputs can be based on particular weighting scores determined within the temporal path neural network, by the processing system from the personal data of the user. Probabilistic attributes may comprise the calculated weights of the temporal path neural network from the at least one journey outcomes to the respective temporal paths. Weights of the neural network can be determined by the server using their process either in real time or offline. The processors assigns and adjusts weights and subsequently uses a training algorithm or training data to set to correctly correlate input and output).
Claim 28, 35 and 42: Modified Siddiqui discloses the system as per claim 27, the method as per claim 33, and the non-transitory computer readable medium as per claim 41. Siddiqui further discloses wherein the one or more weighted inputs are passed through a net input function to determine the net input, and wherein the net input is passed through the activation function to create an output of a corresponding node (Paragraph [0043]; [0054]; [0065]; [0071] the outputs can be based on particular weighting scores determined within the temporal path neural network, by the processing system from the personal data of the user. Probabilistic attributes may comprise the calculated weights of the temporal path neural network from the at least one journey outcomes to the respective temporal paths. Weights of the neural network can be determined by the server using their process either in real time or offline. The processors assigns and adjusts weights and subsequently uses a training algorithm or training data to set to correctly correlate input and output).
Claim 29, 36, and 43: Modified Siddiqui discloses the system as per claim 25, the method as per claim 32, and the non-transitory computer readable medium as per claim 39. Siddiqui further discloses wherein the neural network is at least one of a restricted Boltzmann machine, a deep Boltzmann machine, a deep belief network, a convolutional neural network, a spiking neural network, or a recurrent neural network (Paragraph [0024-0025]; [0204] a neural network consists of a multitude of interconnected processing elements. The processing elements can be organized into layers. The system further comprises using the temporal path neural network to learn, recommend, or augment the at least one optimal temporal path).
Claim 30, 37, and 44: Modified Siddiqui discloses the system as per claim 25, the method as per claim 32, and the non-transitory computer readable medium as per claim 39. Siddiqui further discloses wherein the one or more processors are further configured to: define a number of positions, wherein each position comprises a number of required skills; generate, using the neural network, a model of relationships between the positions, wherein the model maps potential transitions between positions according to similarities of required skills (Paragraph [0112-0114]; [0160]; Fig. 2B, the appropriate journey for the user can be determined using decision tree algorithms described herein to determine either a best single journey or alternatively all of the valid applicable journeys and an associated total score or total weightage above a certain threshold for those journeys are presented. In an example all of the applicable temporal paths are presented. The user can select which temporal path is desired. A list of educational avenues (certifications, trade skills) for the selected career choices are listed, if applicable. In example embodiments, the likelihood of a certain probability occurring in the decision tree can be calculated);
generate, using the neural network, a model of a number of persons who have occupied the positions according to skills, employment history, and job performance; receive user data from a user device, wherein the user data comprises skills, employment history, and job performance; compare the user data to the model of the number of persons; match a number of potential employment opportunities from among the number of positions to the user data based on similarities between the user data and the number of persons (Paragraph [0100-0101]; [0128-0129]; Fig. 1B, the system determines using the temporal path neural network that contains a perspective user profile of at least one further user having a respective further temporal path that matches one of the optimal temporal paths of the user. Upon finding a matching further user may be configured to provide to the interface system the respective further temporal path of the at least one further users. Once users input their key parameters the system can perform a comparative analysis against professional players in the field. The matching can search for similar users in a similar temporal zone for the user, and display the similar users’ temporal path allowing the user to set goals. The system provides the comparative analysis to the user);
Claim 31 and 38: Modified Siddiqui discloses the system as per claim 25, and the method as per claim 37. Siddiqui further discloses wherein the potential career path comprises a number of job transitions between different jobs starting from an initial job, wherein each transition is displayed with a predicted likelihood of a transition, an indication of the transition directly to a plurality of alternate jobs or branching parallel transitions to the plurality of alternate jobs, and wherein each of the plurality of alternate jobs is displayed with a respective fit between a user and a job based on a match between information about the user and prior occupants of the job (Paragraph [0112-0114]; [0160]; Fig. 2B, the appropriate journey for the user can be determined using decision tree algorithms described herein to determine either a best single journey or alternatively all of the valid applicable journeys and an associated total score or total weightage above a certain threshold for those journeys are presented. In an example all of the applicable temporal paths are presented. The user can select which temporal path is desired. A list of educational avenues (certifications, trade skills) for the selected career choices are listed, if applicable. In example embodiments, the likelihood of a certain probability occurring in the decision tree can be calculated).
Therefore, claims 25-44 are rejected under 35 U.S.C. 103.
Response to arguments
Applicant’s arguments, see REMARKS, filed March 09, 2026, with respect to the rejections of Claim(s) 25-44 is/are rejected under 35 U.S.C. 101 are considered and not persuasive.
Claims 25, 32, and 39: Representative argues that the amended claims do not recite an abstract idea as they cannot be practically performed in the human mind. The representative argues that the claims recite the claim limitations of “access a neural network comprising a visible node layer and a hidden node layer, wherein the visible node layer comprises a plurality of visible nodes connected to a plurality of hidden nodes of the hidden node layer; cause a plurality of hidden nodes of the neural network to generate one or more weighted inputs, cause the plurality of hidden nodes to apply an activation function to a net input determined based on the one or more weighted inputs, generate a data graph comprising a plurality of graph nodes, generate using the neural network a first graphical representation of the first profile based on a first subset of the plurality of graph nodes, generate using the neural network a second graphical representation of the second profile based on a second subset of the plurality of graph nodes, update the neural network by adjusting one or more weights based on a comparison of one or more model performance criteria of a selected trained algorithm to an output of the comparison of the first graphical representation and the second graphical representation to improve performance of the neural network in generation of subsequent output from the neural network, transmit to a client device data to cause the client device to display on an output device a graphical user interface with profile data generated based on the subsequent outputs from the neural network that is updated with the adjusted one or more weights that improve the performance of the neural network, construct, responsive at least in part to updating of the neural network by adjusting the one or more weights, a stacked neural network comprising the neural network and at least one additional neural network, wherein the hidden nodes of the neural network having the adjusted one or more weights are used as training data for training of the at least one additional neural network, and wherein the stacked neural network forms a deep learning network configured to generate improved subsequent outputs relative to the subsequent outputs generated by the neural network; transmit to the client device data to cause the client device to update the graphical user interface to present one or more graphical elements comprising a plurality of training programs configured to address the one or more gaps in the transition prior positions certifications skills and past responsibilities of the first profile in relation to the expected transition prior positions certifications skills and past responsibilities associated with the position of the second profile, and transmit to the client device data to cause the client device to update the graphical user interface to present at least one graphical element comprising a filled portion sized according to a respective fit score of the first profile with the position of the second profile; and wherein the system is implemented using at least one of a graphical processing unit, an application-specific integrated circuit, or a programmable logic device configured for machine learning operations.” However, the examiner respectfully disagrees as the claims recite a method for determining and presenting a user with a plurality of programs to address one or more gaps in their transitions, prior positions, certifications, skills, and past responsibilities. The method comprising receiving first profile data associated with a first profile, providing the first profile data as input to generate one or more weighted inputs, generating at least one output associated with the first profile by applying an activation functions to a net input based on the one or more weighted inputs, generating a data graph comprising a plurality of graph nodes representing a plurality of tasks and a plurality of edges connecting the plurality of graph nodes, receiving second profile data, generating as second graphical representation of the second profile based on a second subset of the plurality of graph nodes, determining based on a comparison of the first graphical representation and the second graphical representation similarities between the transitions, prior positions, certifications, skills, and past responsibilities of the first and second profile, identifying based on the comparison one or more gaps, and presenting a plurality of programs configured to address the one or more gaps. The method recites merely a series of steps for receiving and analyzing user information to determining similarities and gaps between a first user profile such as a job seeker’s profile or a student’s profile and a second user’s profile such as a professional’s profile and recommending programs to help address the gaps. A person such as a guidance counselor or a career manager would be capable of mentally, or by using simple tools such as pen and paper, be able to receive and analyze user profile data by comparing the data of a first and a second user to determine gaps between them and subsequently recommending programs to help address the gaps. The claims are similar to subject matter the courts have identified as reciting a mental process such as observation, evaluation, judgment, and opinion. As a person is capable of mentally observing and evaluating profile data to determine gaps and recommendations to help alleviate the gaps.
Alternatively, the claims recite a certain method of organizing human activity as they recite managing personal behavior or relationships or interactions between people. The claims merely recite a series of steps to receive and evaluate user profile information by comparing a first user to a second user and determining a potential career path as well as recommending programs to help address gaps between a current user’s skill set and that of another user who has been successful in a similar career path. Analyzing a user’s characteristics to recommend programs to help address gaps in their transitions, prior positions, certifications, skills, and past responsibilities in a career path is a certain method of organizing human activity. Therefore, the claims recite an abstract idea.
The representative further argues that the additional elements are directed to a practical application as they recite an improvement to the functioning of a computer. The representative argues that the claims recite a specific combination of features that amount to a technical solution that improves computer technology as humans do not have the cognitive capacity to comprehend potential career paths and opportunities and therefore the claims recite an improved modeling system that uses a neural network to generate and analyze a data graph, updates the neural network to improve accuracy, and updates a graphical user interface to display graphical elements. However, the examiner respectfully disagrees as the additional elements of using a neural network to analyze information and transmitting data to a client device to update a graphical user interface to present graphical elements are directed to merely “apply it.” The additional elements are directed to merely apply it as the claims use generic neural network elements to perform the abstract idea of receiving and analyzing information and generic computer elements to receive, transmit, and present information on a graphical user interface. Training and using a neural network to evaluate data by receiving an input, applying a weight, and determining an output is not an improvement to a neural network but merely applying a neural network to perform a standard function. Additionally constructing a stacked neural network, such as a deep neural network, by adjusting one or more weights and updating a neural network based on training data, is not an improvement to a neural network. But a standard and generic processes of training a neural network to be able to make a prediction or generate an output based on an input. While merely using generic computer elements to receive, process, transmit, and display information is not an improvement to a computer but applying a generic computer to perform the abstract idea.
Therefore, the examiner maintains the current 101 rejection.
Claims 26-31, 33-38, and 40-44 were dependent on claims 25, 32, and 39 Therefore, they are also rejected under the same rejection as above.
Applicant’s arguments, see REMARKS, filed March 09, 2026, with respect to the rejections of 25-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Siddiqui (US 2019/0228288) in view of Xie (US 2019/0303798) further in view of Sallee (2020/0134376) even further in view of Jarrett (US 2016/0196534) are not persuasive as the claims were amended which required further search and consideration and new art was applied.
Claims 25, 32, and 39: Applicant argues that the current prior art does not disclose the newly amended claim limitations. However, upon further search and consideration the examiner finds that the combination of Siddiqui and Sallee teach the newly amended claim limitations. Siddiqui discloses a system of receiving input information pertaining to a user such as a student or employee and use a neural network algorithm to model “what-if” scenarios. Siddiqui discloses a system of allowing a user to input personal information via an interface and generate a report by using a neural network to process the input information by performing actions such as assigning weights to information (Siddiqui [0042]). Siddiqui further discloses a system where a neural network used for making predictions comprises a plurality of layers including visible layers, i.e. input and output layers, and hidden layers, i.e. the layers in-between the input and output layers. The neural network disclosed by Siddiqui is further trained using a plurality of training techniques and training data sets to adjust node weights to help improve the outcome of the neural network when predicting various career options. Siddiqui can be further used in combination with Sallee which teaches a neural network architecture to predict an outcome based on input data. Salle further teaches using a deep learning neural network such as stacked neural network which can be trained using a training set to help improve the ability of the model to make predictions and generate a desired output based on the received inputs (Sallee [0041]). Therefore, the current combination of prior art teaches the newly amended claim limitations.
Therefore, the examiner maintains the current rejects of independent claim 25, 32, and 39 over U.S.C. 103.
Claims 26-31, 33-38, and 40-44 were dependent on claims 25, 32, and 39. Therefore, they are also rejected under the same rejection as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Bolte (US 2018/0039946) Career data analysis systems and methods.
Pande (US 2022/0101265) Career analytics platform.
Alkan (US 2020/0143498) Intelligent career planning in a computing environment.
Marino (US 2015/0140526) Systems and methods for career preferences assessment.
Terhark (US 2018/0232751) Internet system and method with predictive modeling.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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/COREY RUSS/Primary Examiner, Art Unit 3629