DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Regarding claim 1, in Step 1 of the 101 analysis set forth in the MPEP 2106, the claim recites a method for conducting a strategy of a digital assistant. A process is one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101 analysis set forth in the MPEP 2106, the examiner
has determined that the following limitations recite a process that under broadest
reasonable interpretation, covers a mental process but for recitation of generic
computer components:
“A method for conducting a strategy of a digital assistant, comprising: identifying a plurality of potential plans for a user based on input data, wherein the plurality of potential plans includes an optimal plan having a highest expected reward value and at least one suboptimal plan having an expected reward value less than the highest expected reward value, wherein the input data includes historical data and a current state of the user;” (a person can mentally come to the conclusion of a set of potential plans to give based on a given set of information about a person as a process of simply evaluating the information, and making a judgement on an optimal set of plans that would fit the individual based on the evaluated data and past data (MPEP 2106)).
“determining an exploration score based on the first dataset, the second dataset, the third dataset, and the input data;” (a person can come to the conclusion of determining an exploration score based on a first, second, and third dataset, including inputted data as a process of simply evaluating the data of the first, second and third data sets, including the input data, and making a judgement based on information provided to determine an exploration score based on the evaluated data.)
“determining a strategy based on the determined exploration score;” (a person can mentally come to the conclusion of determining a strategy based on a pre-determined score to explore other strategies and/or plans as a process of simply making a judgement based on the outcome of the determined exploration score that would necessitate changing the plan to using a suboptimal strategy.)
If claim limitations, under their broadest reasonable interpretation, covers
performance of the limitations as a mental process but for the recitation of generic
computer components, then it falls within the mental process grouping of abstract ideas.
According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has
determined that the following additional elements do not integrate this judicial exception
into a practical application:
“and causing the digital assistant to perform at least one of the plurality of potential plans based on the determined strategy.” (using a computer to perform an abstract idea (mere instructions to apply an abstract idea on a generic computer) (MPEP 2106.05(f)))
“extracting a first dataset, a second dataset, and a third dataset from the input data, wherein the first dataset provides a rejection history for the plurality of potential plans, wherein the second dataset indicates a receptiveness level of the user, wherein the third dataset includes confidence levels of expected reward values for each of the plurality of potential plans;” (This limitation is an insignificant extra solution activity of mere data gathering. (MPEP 2106.05(g)))
Since the claim does not contain any other additional elements, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application, the claim is “directed” to an abstract idea.
In step 2b Prong 2 of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, Additional element (iv) recite mere instructions to perform an abstract idea on a generic computer (artificial intelligence model), which is not indicative of significantly more. Additional element (v) recite insignificant extra-solution activity routine and conventional in the form of mere data gathering and mere data output respectively, which additionally is well understood, routine, and conventional activity (the limitation recites receiving or transmitting data over a network, e.g., using the Internet to gather data, (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (MPEP 2106.05(d)(II))), which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent on claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 2 recites “wherein the strategy is an exploitation strategy when the exploration score is less than a predetermined threshold score,” (a person can mentally determine when it is appropriate to use a specific strategy by using a scoring threshold as a process of simply evaluating the score, and making a judgement if the score is below the threshold and utilize the correct strategy according to the scoring data. (MPEP 2106). If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for recitation of generic computer components then it falls within the mental process grouping of abstract ideas. According, the claim recites an abstract idea.)
“and wherein the exploitation strategy causes the digital assistant to perform the optimal plan.” (In step 2a, Prong 2, this recites using mere instructions to apply the abstract idea on a generic computer (MPEP 2106.05(f)), which is not indicative of integration into a practical application. In step 2b, the limitation recites mere instructions to apply the abstract idea on a generic computer, which is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 3, it is dependent on claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 3 recites “wherein the strategy is an exploration strategy when the exploration score is equal or greater than a predetermined threshold score,” (a person can mentally determine when it is appropriate to use a specific strategy by using a scoring threshold as a process of simply evaluating the score, and making a judgement if the score is above or meets the threshold and utilize the correct strategy according to the scoring data. (MPEP 2106). If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for recitation of generic computer components then it falls within the mental process grouping of abstract ideas. According, the claim recites an abstract idea.)
“and wherein the exploration strategy causes the digital assistant to perform the at least one suboptimal plan.” (In step 2a, Prong 2, this recites using mere instructions to apply the abstract idea on a generic computer (MPEP 2106.05(f)), which is not indicative of integration into a practical application. In step 2b, the limitation recites mere instructions to apply an abstract idea on a generic computer, which is not indicative of significantly more). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent on claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further claim 4 recites “determining a specific suboptimal plan for the at least one suboptimal plan based on the first dataset, the second dataset, a third dataset, and the input data;” ((a person can mentally come to a conclusion of determining what plan would be suboptimal based on specific sets of data as a process of simply evaluating the given data and making a judgement on what planned outcome for the data would be considered suboptimal to be chosen. If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for recitation of generic computer components then it falls within the mental process grouping of abstract ideas. According, the claim recites an abstract idea.)
“and causing the digital assistant to perform the determined specific suboptimal plan.” (In step 2a, Prong 2, this recites using mere instructions to apply the abstract idea on a generic computer (MPEP 2106.05(f)), which is not indicative of integration into a practical application. In step 2b, the limitation recites using mere instructions to apply the abstract idea on a generic computer, which is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent on claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 5 recites “applying a machine learning model trained to determine the current state based on real-time data of the user and real-time data of an environment in a predetermined proximity to the user in real time.” (In step 2a, Prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))), which is not indicative of integration into a practical application. In step 2b, the limitation recites generally linking the invention to a particular technological environment or field of use, which is not indicative of significantly more.). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent on claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 6 recites “wherein the real-time data of the user and a realtime data of an environment is captured by at least one sensor of an I/0 device.” (In step 2a, Prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))), which is not indicative of integration into a practical application. In step 2b, the limitation recites generally linking the invention to a particular technological environment or field of use, which is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent on claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 7 recites “wherein the at least one of the plurality of potential plans are presented by at least one resource of an I/0 device.” (In step 2a, Prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))), which is not indicative of integration into a practical application. In step 2b, the limitation recites generally linking the invention to a particular technological environment or field of use, which is not indicative of significantly more.). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8, it is dependent on claim 5, and thereby incorporates the limitations of, and corresponding analysis applied to claim 5. Further claim 8 recites “generating the expected reward values for each of the plurality of potential plans based on the current state and the historical data of the user, wherein the expected reward value is a numerical value to indicate probability of user to accept the respective performed potential plan” (a person can mentally calculate a value to represent likelihood as a process of simply evaluating the numerical data to calculate probability, and make a judgement based on current and past occurrences if the likelihood an occurrence is probable while providing a value to such idea. (MPEP 2106) If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for recitation of generic computer components then it falls within the mental process grouping of abstract ideas. According, the claim recites an abstract idea.)
Regarding claim 9, it is dependent on claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 9 recites “generating feedback data, based on user data for the performed potential plan, wherein the user data includes at least one of: a user reply, a user reaction, and a sensory data of user;” (a person can mentally come to the conclusion of generating feedback for a plan as a process of simply receiving the input of an individual, and evaluating the feedback for relevant portions, and making a judgement on what would be proper to present as feedback for learning. If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for recitation of generic computer components then it falls within the mental process grouping of abstract ideas. According, the claim recites an abstract idea.)
“storing the feedback data of the user for the performed potential plan;” (In step 2a, Prong 2, This limitation is an insignificant extra solution activity of mere data gathering. (MPEP 2106.05(g)), which is not indicative to integration into a practical application. In step 2b, the limitation recites storing and retrieving information in memory (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)), which is not indicative of significantly more.) “and updating the input data.” (In step 2a, Prong 2, This limitation is an insignificant extra solution activity of mere data gathering. (MPEP 2106.05(g)), which is not indicative to integration into a practical application. In step 2b, the limitation recites storing and retrieving information in memory (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)), which is not indicative of significantly more.). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 10, it recites a process, which falls under one of the four statutory categories of invention. Claim 10 recites, “A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:”. In step 2a, Prong 2 and step 2B, this recites using a computer to perform an abstract idea (applying the computer use to the abstract idea) (MPEP 2106.05(f)), which is not indicative of integration into a practical application or indicative of significantly more. The remaining further limitations of claim 10 comprises similar limitations as claim 1, and therefore are rejected for similar rationale. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 11, it recites a system, which falls under one of the four statutory categories of invention. Claim 11 recites, “A system for conducting a strategy of a digital assistant, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:”. In step 2a, Prong 2 and step 2B, this recites using a computer to perform an abstract idea (applying the computer use to the abstract idea) (MPEP 2106.05(f)), which is not indicative of integration into a practical application or indicative of significantly more. The remaining limitations in claim 11 comprises similar limitations as claim 1, and therefore are rejected for similar rationale. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 12, it is dependent on claim 11, and comprises similar limitations as claim 2, therefore it is rejected on similar rationale.
Regarding claim 13, it is dependent on claim 11, and comprises similar limitations as claim 3, therefore it is rejected on similar rationale.
Regarding claim 14, it is dependent on claim 13, and comprises similar limitations as claim 4, therefore it is rejected on similar rationale.
Regarding claim 15, it is dependent on claim 11, and comprises similar limitations as claim 5, therefore it is rejected on similar rationale.
Regarding claim 16, it is dependent on claim 11, and comprises similar limitations as claim 6, therefore it is rejected on similar rationale.
Regarding claim 17, it is dependent on claim 11, and comprises similar limitations as claim 7, therefore it is rejected on similar rationale.
Regarding claim 18, it is dependent on claim 15, and comprises similar limitations as claim 8, therefore it is rejected on similar rationale.
Regarding claim 19, it is dependent on claim 11, and comprises similar limitations as claim 9, therefore it is rejected on similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 and 10-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter Pub No.: US 20210073287 A1, in view of Lugt et al. (hereinafter “Lugt”) Pub No.: US 20210142118 A1.
Regarding claim 1 Hunter teaches:
A method for conducting a strategy of a digital assistant, comprising: identifying a plurality of potential plans for a user based on input data, ([0207] In some embodiments, the action value may be determined based on future reward values associated with possible future program state transitions (ie. program states represent the model’s potential plan for the user) … In addition, the reward values may be based on additional factors, such as other program state values, specific directed graph configurations, types of vertex statuses, or the like. (ie. these different program states create the framework for the potential plan to be given to the user.) [0204] In some embodiments, the action value associated with a set of possible state-changing events causable by an entity may be determined based on a reward value associated with a change from a first program state to a second program state caused by the state-changing event. (ie. entities have a possibility to make changes to the output of the model in the form of program states) [0259] In some embodiments, the process 1600 may include obtaining a first entity and second entity for transaction scoring, as indicated by block 1628. In some embodiments, the first entity and second entity may be explicitly provided by a user as inputs (ie. potential plans are dictated by user input data) or selected by default.)
wherein the plurality of potential plans includes an optimal plan having a highest expected reward value and at least one suboptimal plan having an expected reward value less than the highest expected reward value, ([0244] The intelligent agent may begin at a first vertex of a directed graph and traverse the directed graph by proceeding to an adjacent vertex determined by the set of directed edges connected to the first vertex. Each configuration of the directed graph may be of a different program state or be otherwise associated with a different program state (ie. each pathing configuration leads to a different plan) … the intelligent agent may traverse to a first child vertex of an initial directed graph from a starting vertex. The intelligent agent may proceed to expand the directed graph to simulate evolution of its associated program state until arriving at a terminal child vertex and ending at a terminal program state. In some embodiments, performing an MCTS operation may include determining a maximum upper confidence bound (UCB) score, where the UCB score may be based on an exploration weight and a total weight score, where the exploration weight is correlated with(ie. The UCB score determines what strategy is being employed [exploitation/exploration] such that the weight of the proposed plan selects from the optimal or sub-optimal planned outcome.) exploring less-visited nodes (ie. Sub-optimal plan where nodes are representative of possible action paths in a neural network.) and the total weight score is correlated with following the vertices (ie path through the nodes) associated with the greatest total reward values (ie. Optimal plan represented by greatest plan.))
extracting a first dataset, a second dataset, and a third dataset from the input data, … ([0252] In some embodiments, an outcome program state may be associated with an outcome score, where the outcome score may be based on a total reward value, a total reward range, a risk of failure value. (ie. reward value, reward range, and risk of failure representing the first, second, and third datasets.))
… wherein the second dataset indicates a receptiveness level of the user, ([0250] As described above, when implementing a set of MCTS operations, the system may determine a next possible state s+1 for each state s based on a set of heuristic values associated with the set of events causable by an entity (ie. where in this case the entity can be a user’s input as stated earlier in [0259]). A heuristic value may be determined using a function based on a ratio of an aggregate score associated with an event caused by an entity … For example, the heuristic value may be determined as the sum of a first value and a second value. The first value may be a ratio of the aggregate score value to the number of times that an action has been taken (ie. A correlation with the amount of times an operation was taken during decision making [creating an idea of receptiveness from the user] while performing the set of MCTS operations.)
wherein the third dataset includes confidence levels of expected reward values for each of the plurality of potential plans; ([0236] In some embodiments, the counterfactual regret value may reduce a corresponding action value and may represent the regret of not following a strategy profile. For example, a counterfactual regret value for an action may be quantitatively defined as a difference between a total expected loss of an algorithm using a combination of strategies and a minimum total loss when following a single strategy. (ie. the regret value within the action value determines the model’s confidence in sticking to a determined plan) [0246] A third neural network may then be trained to predict a network-predicted reward value based on the policy weights and internal predicted value, where the action value (ie. The action value includes a regret value that determines failure chance) may be equal to or otherwise based on the network-predicted reward value).
determining an exploration score based on the first dataset, the second dataset, the third dataset, and the input data; ([0252] In some embodiments, the process 1500 may include determining a set of outcome program states or set of outcome scores or other parameters of the intelligent agent based on the set of action values or other set of parameters of the intelligent agent … In some embodiments, an outcome program state may be associated with an outcome score, where the outcome score may be based on a total reward value, a total reward range, a risk of failure value, or the like. (ie. total reward value representing receptiveness level wherein a higher reward value is determined as a higher chance for a planned final program state, total reward range representing confidence value wherein the reward value is placed in a range or a threshold to determine the minimum reward value in relation to the action state that includes a regret value of a determined plan to dictate a range/threshold, and risk of failure representing a rejection history wherein a learning model as described in the art holds in memory prior rejected or failed plans) In some embodiments, a smart contract program may receive an event associated with an action performed by a counter entity that causes a change to a program state into a subsequent actual outcome program state that is not in the set of predicted outcome program states)
determining a strategy based on the determined exploration score; ([0252] In some embodiments, an outcome program state may be associated with an outcome score, where the outcome score may be based on a total reward value, a total reward range, a risk of failure value, or the like. In some embodiments, a smart contract program may receive an event associated with an action performed by a counter entity that causes a change to a program state into a subsequent actual outcome program state that is not in the set of predicted outcome program states)
and causing the digital assistant to perform at least one of the plurality of potential plans based on the determined strategy. ([0252] In some embodiments, the process 1500 may include determining a set of outcome program states or set of outcome scores or other parameters of the intelligent agent based on the set of action values or other set of parameters of the intelligent agent (ie. the outcome or output of a program state is the planned path chosen by the AI model for the user), as indicated by block 1528. In some embodiments, the intelligent agent may determine an outcome program state by providing a probability distribution associated with one or more possible outcome program states (ie. there is a probability of multiple planned outcomes being used as the output to the user, furthermore the program states represent the potential plans in this description).
Hunter does not explicitly teach “wherein the input data includes historical data and a current state of the user and wherein the first dataset provides a rejection history for the plurality of potential plans”.
Lugt further teaches:
… wherein the input data includes historical data and a current state of the user; … ([0111] A threshold database 310 can store one or several threshold values. These one or several threshold values can delineate between states or conditions. In one exemplary embodiment, for example, a threshold value can delineate between an acceptable user performance (ie. user performance being the current state of the user) and an unacceptable user performance, between content appropriate for a user and content that is inappropriate for a user (ie. A threshold’s state is determined by the input data placed in the machine learning model) … [0128] As mentioned above, the CC interface 338 allows the CDN 110 to query historical messaging queue 412 information. An archive data agent 336 listens to the messaging queue 412 to store data streams in a historical database 334. (ie. Query data of historic database is taken as part of the input for a user))
wherein the first dataset provides a rejection history for the plurality of potential plans, … ([0006] In some embodiments, selecting the next piece of content includes receiving the correlation matrices relevant to the user context, multiplying the received correlation matrices to generate a set of scalar weights, each of which scalar weights is associated with a context, identifying success and failure data for each potential next piece of content ((ie. content being the potential plan in this configuration.)) in each potential context (ie. Depending on the context of the data provided, if a user would utilize the recommendation of content provided, wherein the context of potential content serves as a history of the user to reject or not complete recommended planned content)) [0066] For example, in content distribution networks 100 used for professional training and educational purposes, content server 112 may include data stores of training materials, presentations, plans, syllabi, reviews, evaluations, interactive programs and simulations, course models, course outlines, and various training interfaces that correspond to different materials and/or different types of user devices (ie. above content includes plans to be distributed to the user through their devices) 106.
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references both deal with recommendation systems and methods for users. Consequently, one of ordinary skill in the art would be motivated to further modify the system as in Hunter to receive the benefits of Hunter’s recommendation and strategy decision making utilizing an exploration/exploitation decision making scheme following a reward system to determine the use of either strategy, and the structure of Lugt’s recommendation system with peripheral components for user interaction and a user centric focus in its design and application, such that the two systems modified to each other can provide a versatile decision process for the machine learning model to assist the user while receiving multiple types of user input.
Regarding claim 10 it recites, “A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process…” in the art provided Hunter teaches, “[0463] … The functionality described herein may be provided by one or more processors of one or more computers executing code stored on a tangible, non-transitory, machine readable medium. ”, claim 10 also recites similar further limitations to that in claim 1, therefore it is rejected for similar rationale.
Regarding claim 11, it recites “a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry…” in the art provided Hunter teaches, “[0007] Some aspects include a system, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process.”, claim 11 also recites similar further limitations to that in claim 1, therefore it is rejected for similar rationale.
Regarding claim 2 Hunter teaches:
wherein the strategy is an exploitation strategy when the exploration score is less than a predetermined threshold score, ([0243] A self-play operation may include using an initial set of random decisions representing actions or events caused by each entity, such as via a Monte Carlo Search Tree operation (MCTS) described further below, until a terminal program state is reached (ie. model is making decisions utilizing MCTS operations mentioned below). The intelligent agent may then store the terminal program state, an associated terminal state outcome score, or other outcome program states or their associated outcome scores in the outcome program state store. [0244] In some embodiments, performing an MCTS operation may include determining a maximum upper confidence bound (UCB) score, where the UCB score may be based on an exploration weight and a total weight score (ie. Exploitation occurs when exploration score is below total weight score [a numerical threshold] in the MCTS configuration as part of the outcome score discussed by Hunter in [0243])) and wherein the exploitation strategy causes the digital assistant to perform the optimal plan. ([0244] The intelligent agent may proceed to expand the directed graph to simulate evolution of its associated program state until arriving at a terminal child vertex and ending at a terminal program state … where the exploration weight is correlated with exploring less-visited nodes and the total weight score is correlated with following the vertices associated with the greatest total reward values (ie. Optimal plan where the vertices represent the greatest total reward points and thus the optimal plan following MCTS operations as described in claim, found in paragraphs [0243] and [0244])).
Regarding claim 12, it recites similar limitations to that in claim 2, therefore it is rejected for similar rationale.
Regarding claim 3 Hunter teaches:
wherein the strategy is an exploration strategy when the exploration score is equal or greater than a predetermined threshold score, ([0244] In some embodiments, performing an MCTS operation may include determining a maximum upper confidence bound (UCB) score, where the UCB score may be based on an exploration weight and a total weight score (ie. the total weight score serving as the value to be compared to the exploration weight to create the threshold for performing exploration wherein said threshold is upper bound to require compared score be at or above the upper bound to start exploration strategy in model, following outcome score in MCTS operations as discussed in claim 2 in paragraphs [0243] and [0244])) and wherein the exploration strategy causes the digital assistant to perform the at least one suboptimal plan. ([0244] where the exploration weight (ie. this weight dictates when the suboptimal plan is used in MCTS operations based on regret values that tell the system to perform sub-optimal plans) is correlated with exploring less-visited nodes (ie. Sub-optimal plans are provided with the less visited nodes as they are not chosen frequently as program state paths) and the total weight score is correlated with following the vertices associated with the greatest total reward values).
Regarding claim 13, it recites similar limitations to that in claim 3, therefore it is rejected for similar rationale.
Regarding claim 4, Hunter teaches:
determining a specific suboptimal plan for the at least one suboptimal plan based on the first dataset, the second dataset, a third dataset, and the input data; ([0241] In some embodiments, additional strategic behavior may be considered by using neural networks to consider the viability of additional events causable by an entity that would be ignored (ie. These events being potential planned actions of the model that are being considered suboptimal in the model utilizing the first second and third claimed datasets as provided by Hunter in [0250] (receptiveness level as discussed in 103 rejection for claim 1), [0252] (reward value as discussed in 103 rejection of claim 1), and [0236] (confidence level of expected reward value as discussed in 103 rejection of claim 1)) or otherwise grouped in a category.)
and causing the digital assistant to perform the determined specific suboptimal plan. ([0240] Using the regret matching routine may also include updating a set of strategy values at the time point based on a regret value for an action and a total regret value across all actions. For example, the regret matching routine may include updating a strategy value based on a ratio of a regret value acquired of performing a first action with a first information set and a sum of regret values, where each regret value of the sum regret values is based on a different action and the first information set. [0241] In some embodiments, additional strategic behavior may be considered by using neural networks to consider the viability of additional events causable by an entity that would be ignored or otherwise grouped in a category. [0252] In some embodiments, a smart contract program may receive an event associated with an action performed by a counter entity that causes a change to a program state into a subsequent actual outcome program state that is not in the set of predicted outcome program states (ie. when the models regret value meets a threshold, the system provides an exploration type solution over an exploitation type of solution to make recommendation to the user, wherein the regret values may cause the model to consider plans that would be otherwise ignored (suboptimal) under normal circumstances.)).
Regarding claim 14, it recites similar limitations to that in claim 4, therefore it is rejected for similar rationale.
Regarding claim 5, Hunter does not explicitly teach “applying a machine learning model trained to determine the current state based on real-time data of the user and real-time data of an environment in a predetermined proximity to the user in real time.”, however in analogous art Lugt teaches:
applying a machine learning model trained to determine the current state based on real-time data of the user and real-time data of an environment in a predetermined proximity to the user in real time. ([0143]The I/O subsystem 526 may provide one or several outputs to a user by converting one or several electrical signals to user perceptible and/or interpretable form, and may receive one or several inputs from the user by generating one or several electrical signals based on one or several user-caused interactions with the I/O subsystem such as the depressing of a key or button, the moving of a mouse, the interaction with a touchscreen or trackpad, the interaction of a sound wave with a microphone [0144] Input devices 530 may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. Input devices 530 may also include three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices (ie. the I/O devices determine the current state of the user and observe/listen to the environment surrounding the user in real-time, wherein the proximity of the environment being observed/listened is the area directly in front of the system being used so the system can observe and read elements like eye movement and gestures). Additional input devices 530 may include, for example, motion sensing and/or gesture recognition devices that enable users to control and interact with an input device through a natural user interface using gestures and spoken commands, eye gesture recognition devices that detect eye activity from users and transform the eye gestures as input into an input device).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references all deal with machine learning models for user content. Consequently, one of ordinary skill in the art would be motivated to further modify the system as in Lugt to receive the benefits of Lugt teaching of a machine learning system utilizing sensors to capture user input and environment data to assist in providing feedback to the system for training, and the structure of Lugt’s and Hunter’s recommendation system with strong decision making for neural network pathing and peripheral I/O components for user interaction and a user centric focus in its design and application, such that the two systems modified to each other can provide a versatile decision process for the machine learning model to assist the user while receiving multiple types of user input.
Regarding claim 15, it recites similar limitations to that in claim 5, therefore it is rejected for similar rationale.
Regarding claim 18, it is dependent on claim 15 and recites similar limitations to that in claim 8, therefore it is rejected for similar rationale.
Regarding claim 6, Hunter does not explicitly teach “wherein the real-time data of the user and a real-time data of an environment is captured by at least one sensor of an I/O device”, however in analogous art Lugt teaches:
wherein the real-time data of the user and a real-time data of an environment is captured by at least one sensor of an I/O device. ([0143] The I/O subsystem 526 may provide one or several outputs to a user by converting one or several electrical signals to user perceptible and/or interpretable form, and may receive one or several inputs from the user by generating one or several electrical signals based on one or several user-caused interactions with the I/O subsystem such as the depressing of a key or button, the moving of a mouse, the interaction with a touchscreen or trackpad, the interaction of a sound wave with a microphone (ie. the use of a touchpad or microphone extend the environment outside the system wherein this environment that is being used is captured by the system.) [0144] Input devices 530 may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. Input devices 530 may also include three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices (ie. these other input devices (cameras and microphones) can provide real-time data of a user in the proximity to be observed and heard by them as well they further extend the environment that the system is capturing.). Additional input devices 530 may include, for example, motion sensing and/or gesture recognition devices that enable users to control and interact with an input device through a natural user interface using gestures and spoken commands, eye gesture recognition devices that detect eye activity from users and transform the eye gestures as input into an input device).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references all deal with machine learning models for user content. Consequently, one of ordinary skill in the art would be motivated to further modify the system as modification with reasonable chance of success, to receive the benefits of Hunter’s decision making methods, Lugt’s use of sensor based I/O devices in generating user content and Dalal’s preexisting methodology such that the systems modified to each other can provide a versatile decision process for the machine learning model to assist the user while receiving multiple types of user input including that of the environment the user is in to properly add additional levels of context to the decision making process.
Regarding claim 7, Hunter does not explicitly teach “wherein the at least one of the plurality of potential plans are presented by at least one resource of an I/O device”. However, in analogous art Lugt teaches:
wherein the at least one of the plurality of potential plans are presented by at least one resource of an I/O device. ([0066] For example, in content distribution networks 100 used for professional training and educational purposes, content server 112 may include data stores of training materials, presentations, plans, syllabi, reviews, evaluations, interactive programs and simulations, course models, course outlines, and various training interfaces (ie. multiple plans for the system to present to the user) [0143] I/O subsystem 526 may include device controllers 528 for one or more user interface input devices and/or user interface output devices 530. User interface input and output devices 530 may be integral with the computer system 500 (e.g., integrated audio/video systems, and/or touchscreen displays), (ie. the devices 530 within system 500 are used in the presentation of content to a user as output.) or may be separate peripheral devices which are attachable/detachable from the computer system 500. The I/O subsystem 526 may provide one or several outputs to a user by converting one or several electrical signals to user perceptible and/or interpretable form, and may receive one or several inputs from the user by generating one or several electrical signals based on one or several user-caused interactions with the I/O subsystem such as the depressing of a key or button, the moving of a mouse, the interaction with a touchscreen or trackpad, the interaction of a sound wave with a microphone, or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references both deal with recommendation systems and methods for users. Consequently, one of ordinary skill in the art would be motivated to further modify the system as in Hunter to receive the benefits of Hunter’s recommendation and strategy decision making utilizing an exploration/exploitation decision making scheme following a reward system to determine the use of either strategy, and the structure of Lugt’s recommendation system with peripheral components for user interaction and a user centric focus in its design and application, such that the two systems modified to each other can provide a versatile decision process for the machine learning model to assist the user while receiving multiple types of user input from sensors to collect useful information.
Regarding claim 17, it recites similar limitations to that in claim 7, therefore it is rejected for similar rationale.
Regarding claim 8, Hunter does not explicitly teach, “generating the expected reward values for each of the plurality of potential plans based on the current state and the historical data of the user, wherein the expected reward value is a numerical value to indicate probability of user to accept the respective performed potential plan.”,
However, Lugt teaches:
generating the expected reward values for each of the plurality of potential plans based on the current state and the historical data of the user, (([0111] A threshold database 310 can store one or several threshold values (ie. the database acting as a historical repository for threshold data). These one or several threshold values can delineate between states or conditions. In one exemplary embodiment, for example, a threshold value can delineate between an acceptable user performance (ie. user performance being the current state of the user, moreover this value can also be from the historical database where prior threshold values are stored) and an unacceptable user performance, between content appropriate for a user and content that is inappropriate for a user … [0128] As mentioned above, the CC interface 338 allows the CDN 110 to query historical messaging queue 412 information. An archive data agent 336 listens to the messaging queue 412 to store data streams in a historical database 334 (ie. this historical database storing user information on past content). [0006] In some embodiments, selecting the next piece of content includes receiving the correlation matrices relevant to the user context, multiplying the received correlation matrices to generate a set of scalar weights, each of which scalar weights is associated with a context, identifying success and failure data for each potential next piece of content ((ie. content being the potential plan in this configuration, wherein the scalar weights represent the reward value based on their determination of success or failure of content.)) in each potential context (ie. Depending on the context of the data provided, if a user would utilize the recommendation of content provided)))
wherein the expected reward value is a numerical value to indicate probability of user to accept the respective performed potential plan. ([0291] After receipt of the user action data, the process 940 proceeds to block 960 when recommendation success is determined. In some embodiments the determination of the recommendation success is based on the received user action data. In some embodiments, this can include the calculation of value (ie. wherein the potential plan consists of the recommendation of a potential plan to the user, such potential plan is graded upon a numerical calculation) characterizing degree to which the user interact with the content, a value characterizing the degree to which the content inspired the user to take further action (ie. Accept the planned content, wherein the content represents a potential plan for a user), and a value characterizing the degree to which the content affected the user skill level.)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references both deal with recommendation systems and methods for users. Consequently, one of ordinary skill in the art would be motivated to further modify the system as in Hunter to receive the benefits of Hunter’s recommendation and strategy decision making utilizing an exploration/exploitation decision making scheme following a reward system to determine the use of either strategy, and the structure of Lugt’s recommendation system with clarified reward systems based on numerical values that can be utilized by a model properly, such that the modified systems would make for a better recommendation engine utilizing the strengths both teaching provide to a smoother computation.
Regarding claim 16, Hunter does not explicitly teach “wherein the real-time data of the user and a real-time data of an environment is captured by at least one sensor of an I/0 device.” However in analogous art Lugt teaches:
wherein the real-time data of the user and a real-time data of an environment is captured by at least one sensor of an I/0 device. (([0143] The I/O subsystem 526 may provide one or several outputs to a user by converting one or several electrical signals to user perceptible and/or interpretable form, and may receive one or several inputs from the user by generating one or several electrical signals based on one or several user-caused interactions with the I/O subsystem such as the depressing of a key or button, the moving of a mouse, the interaction with a touchscreen or trackpad, the interaction of a sound wave with a microphone (ie. the use of a touchpad or microphone extend the environment outside the system for example a user can speak and make sound waves that the I/O device then can receive from the environment around the user it receives input from.) [0144] Input devices 530 may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. Input devices 530 may also include three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additional input devices 530 may include, for example, motion sensing and/or gesture recognition devices that enable users to control and interact with an input device through a natural user interface using gestures and spoken commands, eye gesture recognition devices that detect eye activity from users and transform the eye gestures as input into an input device).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references all deal with machine learning models for user content. Consequently, one of ordinary skill in the art would be motivated to further modify the system as modification with reasonable chance of success, to receive the benefits of Hunter’s decision making methods, Lugt’s use of sensor based I/O devices in generating user content and Dalal’s preexisting methodology such that the systems modified to each other can provide a versatile decision process for the machine learning model to assist the user while receiving multiple types of user input including that of the environment the user is in to properly add additional levels of context to the decision making process.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter and Luget as applied to claim 1 above, and further in view of Dalal et al. (hereinafter “Dalal”) Pub No.: US 20200245913 A1.
Regarding claim 9, Hunter does not explicitly teach “generating feedback data, based on user data for the performed potential plan, wherein the user data includes at least one of: a user reply, a user reaction, and a sensory data of user; storing the feedback data of the user for the performed potential plan;”, however in analogous art Lugt teaches:
generating feedback data, based on user data for the performed potential plan, ([0053] The use of such a math engine including two components can be facilitated by improvements in OCR technology also disclosed herein. Specifically, disclose OCR technology is able to efficiently and effectively generate computer readable character strings from image data. This is accomplished through a multistep process including the generation of a plurality of: areas; tokens; and confidence scores for the image data. These areas, tokens, and confidence scores can be ingested into a decoder which can generate a computer readable character string, and specifically can generate, a LaTeX character string. This character string can be presented to the user and feedback received from the user can be used to improve this multistep process. (ie. character strings are derived from OCR image data gathered from user input, wherein the string is a planned correct output to present to the user for feedback.) [0256]...The user feedback can identify one or several portions of the computer readable character string as correct or incorrect. (ie. wherein the model utilizes the OCR capability to output the planned string to the user then user provides feedback on the actions of the machine in context to the planned output string, the machine then uses such feedback to make future informed decisions.))
wherein the user data includes at least one of: a user reply, a user reaction, and a sensory data of user; ([0256] ...which user device can display the computer readable character string to the user via the I/O subsystem 526. At block 814, user feedback is received at the processor 102 from the user device 106, and specifically from the I/O subsystem 526. The user feedback can identify one or several portions of the computer readable character string as correct or incorrect.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter because the references both deal with recommendation systems and methods for users. Consequently, one of ordinary skill in the art would be motivated to further modify the system as in Hunter to receive the benefits of Hunter’s recommendation and strategy decision making utilizing an exploration/exploitation decision making scheme following a reward system to determine the use of either strategy, and the structure of Lugt’s recommendation system with peripheral components for user interaction and a user centric focus in its design and application, such that the two systems modified to each other can provide a versatile decision process for the machine learning model to assist the user while receiving multiple types of user input.
Hunter and Lugt do not explicitly teach the storing of feedback data and its usage to update input data.
In analogous art Dalal further teaches:
storing the feedback data of the user for the performed potential plan; ([0114] In some embodiments, the ML servers 102 can include reinforcement learning (RL) agents 114 that can operate in response to inputs from data sources 108-0 to -2 to generate suggested actions based on a desired reward function. Such suggested actions can be provided to a user device 110 by operation of a ML or application server (102, 104). Subject responses and behavior as recorded by data sources (108-0 to -2) can be encoded into a latent space (ie. user supplied feedback data is stored in memory) with custom variational encoding 116 to model and predict subject responses (ie. the predications providing a plan for the model).) and updating the input data. ([0008] In some embodiments, the system further comprises: a parameter estimator configured to update at least the second subject model in response to a reference biophysical response, and the first and second data values from the first time period (ie. updates in response to input data to the model)).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the teaching of Lugt into the teaching of Hunter and Dalal because the references all deal with machine learning models for user content. Consequently, one of ordinary skill in the art would be motivated to further modify the system as modification with reasonable chance of success, to receive the benefits of Hunter’s decision making methods, Lugt’s use of feedback storage and input into Lugt’s taught model and Dalal’s preexisting methodology, as well as Hunter’s exploration/exploitation strategy such that the systems modified to perform this modification to provide the model with future training data and knowledge of a user, such that preferences and habits of the user are usable in future decision making and taken into consideration by the model during future epochs.
Regarding claim 19, it recites similar limitations to that in claim 9, therefore it is rejected for similar rationale.
Response to Arguments
Applicant's arguments filed on 9/29/2025 have been fully considered and they are not persuasive.
In Remarks applicant Agues:
Argument Regarding 101 rejection: Applicant argues on page 1-2 in remarks the claim limitations as recited do not disclose any abstract idea rather discloses improvement to computer technology as disclosed in specification par. 17 and explained in Enfish LLC V Microsoft Corp held that improvement that is implemented in computer technology which may be implemented in hardware and software may not be abstract. Also argued that the memorandum issued from office regarding Enfish also supports that, therefore applicant claims similar to Enfish the application is directed to a specific implementation of a solution in the software art which improves digital assistance this claims do not disclose an abstract idea. Also applicant mentioned that the citation of Enfish was to disclose proper methodology of 101 analysis and not for comparison of the claimed invention.
Response to argument: Applicant argument is not persuasive. Applicant’s argument of claimed improvement as recited in paragraph 17 is a generic allegation improving computer technology. However the actual steps or functions performed to in the claim language which appears to be the claimed improvement is an abstract idea as disclosed in the 101 rejection. The claim as recited performs functions or steps of identifying, extracting and determining in a high level of generality which appears to be mental process of observation, evaluation, judgement or opinion with generic recitation of implementation or usage of generic computer as a tool to perform the abstract idea. The limitation of causing the digital assistance to perform an action or implement an action is recited in a high level of generality which is nothing more than a generic implementation of a tool or apply instructions to perform the claimed function on a generic device/software. Claim do not disclose any specific technical improvement in the claims rather claims the improvement as an abstract idea. Regarding applicant’s analysis of the Enfish, it is unclear how that is applicable to the claimed invention of the instant applicantion when the argument discloses the recitation is for proper analysis of subject matter eligibility and not for comparison. The recited 101 analysis did not disclose any statement that software or hardware improvement are not patent eligible rather the claimed improvement as recited in the claims are not patent eligible as disclosed in the 101 analysis which was conducted according to the 2019 PEG guidance for performing appropriate 101 analysis. Therefore applicant’s argument is not persuasive.
Argument regarding 103 Rejection:
In remarks page 5, first paragraph applicant argues that the potential plan in the claim is different than the potential plan in the cited reference.
In remarks page 5, paragraph 2, applicant argues the cited reference fails to teach the optimal plan and the suboptimal plan as recited in the claim.
In remarks page 5, paragraph 3 and additionally in page 6 applicant argues that the first, second and third data set are not extracted from input data, cited reference do disclose requiring all there dataset and are not related to user data. And also the cited reference par. 250 do not disclose any teaching related to receptiveness off the user as claimed. Also on page 7 first paragraph applicant argues that the cited paragraph 236 and 246 of Hunter fail to disclose confidence of the reward value as claimed.
Applicant argues that since Hunter fails to disclose the three datasets as claimed Hunter fails to disclose rest of the claimed limitations.
Applicant argues Lugt fails to disclose claimed input data including historical data and current state of the user as the data stored includes information for plurality of user and are not identifiable for performing any operation for the user.
Applicant argues since the combination of prior art fails to disclose the independent claims the dependent claims are also not taught by the combination of prior art or any further combination of prior art.
Response to argument:
Regarding argument (a) and (b) examiner respectfully disagrees with the applicant. The argument related to potential plan in the claims being different that the potential plan and the expected rewards related to the potential plan as cited in prior art being different is not persuasive as the claimed limitations are broad as recited and do not disclose any details about the specificities of the potential plan or how to calculate the rewards related to the potential plan rather than simple recitation of a plan and rewards for the plan. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., potential plan for executing a digital assistance that is making certain suggestion to a user or specific rewards calculation) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Regarding argument (c), examiner respectfully disagree with the applicant. The claim limitation broadly recites extracting three different dataset without specifically disclosing how these datasets are extracted from the input data which includes the historical data and the current state of the user. The claim do not disclose which portion of the dataset correspond to the historical data or current state of the user data or what information is included in the historical data and what information is specifically disclosed in the current state of the user data. Claim also do not disclose specifically any receptiveness of the user with detail or clarifies what entails the receptiveness of the user. In support of applicant’s argument applicant links different element of the claims in view of the instant application specification which is clearly not claimed or linked in the actual claim language. Applicant is reminded although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). As cited in the Hunter reference the three dataset are extracted during the training process of the model (par. 252 and figure 15) where the training is performed with training data which constitutes historical data (par. 225) including past score and rewards along with user input that related to the possible outcome or score. Accordingly Hunter paragraph 250 and 259 as cited performing the calculation score along with number of time action have been taking would be inherently extracted from the historical information which was implemented for training of the model which specifically discloses user receptiveness of the plan as claimed. Applicant’s argument regarding confidence of the reward value is not persuasive as the claim language is broad and does not disclose what constitutes the confidence of the reward value, also applicant in the presented argument clearly fails to disclose the difference between the cited reference counterfactual regret value and the claimed confidence value. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Additionally the rejection is a 103 rejection in combination of Lugt reference which was specifically cited to disclose the input data being historical data and current state data of the user. Applicant’s argument fails to disclose how the combination of Hunter and Lugt fails to disclose the claimed elements. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Regarding argument (d), applicant’s argument is not persuasive. As indicated in the response above Hunter specifically discloses claimed elements of the three datasets as claimed under broadest reasonable interpretation. Therefore Hunter teaches rest of the claimed limitations as claimed.
Regarding argument (d), applicant’s argument is not persuasive. Lugt clearly discloses the claimed input data as cited in the reference par. 111 and 128 and additionally Lugt also disclose having specific user related information along with other user and device related information which enables identification of specific user and would allow to perform operation on user specific data (par. 103).
Regarding argument (f), similarly applicant’s argument the cited references fails to teach the limitations of the dependent claims are not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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 ABDULLAH AL KAWSAR whose telephone number is (571)270-3169. The examiner can normally be reached M-F 7:30am-4:30pm.
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/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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