Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office action has been issued in response to amendment filed on 05/15/2026, Claims (1, 3-11), (12, 14) and (15-20) are pending. Applicants' arguments have been carefully and respectfully considered and addressed. Accordingly, this action has been made FINAL necessitated by amendment.
Claims (1, 3-11), (12, 14) and (15-20) are presented for examination.
Response to Arguments
Applicants' arguments have been carefully and respectfully considered and addressed. The arguments presented are moot based on amendment.
With regards to 101 rejection, the applicant’s arguments have been fully considered and they are persuasive, therefore; the rejection is withdrawn.
With regards to applicant’s argument stating that the prior arts or record do not teach the amended limitation of claim 1, Examiner respectfully disagrees, with regards to “next separate command”, Zheng provides command recommendations based on receiving recommendations from unified models, and aggregating the recommendations and transmitting them to a client device for display to the user ([0021]. Aggarwal teaches providing additional parameters to more effectively identify a next command ([0026], [0032]), and providing the goal information at the fully connected layer aids the model to keep track of the goal while predicting the next data command ([0030]. Aggarwal also incorporates the analytics system that can filter out irrelevant commands, leaving primarily commands that are relevant to the user goal, and the analytics system can also provide guidance to a novice user to allow efficient data analysis ([0039]). Aggarwal clearly describes the generation of “next command” where a command engine selects next command recommendation from the probable commands, wherein the additional command recommendation data can include a goal orientation score that quantifies a degree to which a command (e.g., next command recommendation) aligns with the analysis-goal. The next command recommendation and additional command recommendation output data are communicated and caused to be displayed on the analytics interface ([0040-0050], [0061]). The terms “next” or “separate” are taught by Aggarwal as described above.
With regards to applicant’s argument that none of the references teach by clustering final hidden states from a last hidden layer of a trained recurrent neural network model, associating each cluster with a value of one or more user characteristics, and having an output layer of the trained recurrent neural network model produce the separate next command predictions based on the final hidden states from each cluster, Examiner respectfully disagrees, Yao combined with Zheng and Aggarwal teaches the clustering aspect as described previously ([0044], [0053], [0061-0062], [0071-0073], [0077], [0083], [0085], [0093] wherein Yao communicates commands based on predicting the surrounding environment of a vehicle, and wherein GRU is the gated recurrent units of the motion encoder with hidden state vector and an encoding module that merges and aggregates the final hidden states, wherein the final fused hidden state is an output as hidden state vector of GRU. Wherein the commands are updated for specific participant).
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 of this title, 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, 3-12, 14-20 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Zheng et al. US Patent Application Publication US 20220335043 A1 (hereinafter Zheng) in view of Aggarwal et al. US Patent Application Publication US 20220019909 A1 (hereinafter Aggarwal) and further in view of Yao et al. US Patent Application Publication US 20200086858 A1 (hereinafter Yao).
Regarding claim 1, Zheng teaches A method for recommending one or more next commands to a user of an application, comprising: gathering, by a command prediction module of the application executing on a computing device, command data and user characteristic data for the user ([0002], [0023-0023], [0041-0043] wherein Zheng suggests recommendation of commands for achieving a desired outcome, and wherein Zheng receives command data in the form of queries and contextual data that includes user data and information).
Zheng does not teach cleaning the command data to produce an input dataset; applying, by the command prediction module, the input dataset to a trained recurrent neural network model, the trained recurrent neural network model configured to produce a Separate next command prediction for each of a plurality of different values of one or more user characteristics; selecting, by the command prediction module, one or more recommended next commands from within the next command prediction produced for a value of the one or more user characteristic that corresponds to the user characteristic data for the user; and displaying the one or more recommended next commands in a user interface of the application.
However in analogous art of recommending next commands using recurrent neural networks, Aggarwal teaches cleaning the command data to produce an input dataset ([0021], [0039], [0041], [0058], [0068] wherein Aggarwal describes the steps of filtering out irrelevant commands and producing a representation of the command sequence) applying, by the command prediction module, the input dataset to a trained recurrent neural network model, the trained recurrent neural network model configured to produce a Separate next command prediction for each of a plurality of different values of one or more user characteristics ([0014], [0016], [0021], [0028], [0039-0041], [0058], [0068] wherein Aggarwal describes an analytics system that provide a command recommendation, such as “add device type identifier” as a next command recommendation, wherein the analytics system is based on one or more model architectures (e.g., recurrent neural network (RNN), convolutional neural networks (CNN), frequency models, Markov models, etc.). The analytics system is trained using log data of an application (e.g., log data of a particular user or a group of users) selecting, by the command prediction module, one or more recommended next commands from within the next command prediction produced for a value of the one or more user characteristic that corresponds to the user characteristic data for the user (Abstract, [0021], [0039-0040], [0048], [0050] wherein Aggarwal describes the steps of command engine selecting a next command recommendation from commands based on analysis that includes the level of user’s skills) displaying the one or more recommended next commands in a user interface of the application (Abstract, [0003], [0021], [0039-0042], [0050-0052], [0058], [0068] wherein Aggarwal displays the command recommendation on the interface).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Aggarwal with Zheng by incorporating the method of cleaning the command data to produce an input dataset of Aggarwal; applying, by the command prediction module, the input dataset to a trained recurrent neural network model, the trained recurrent neural network model configured to produce a Separate next command prediction for each of a plurality of different values of one or more user characteristics; selecting, by the command prediction module, one or more recommended next commands from within the next command prediction produced for a value of the one or more user characteristic that corresponds to the user characteristic data for the user of Aggarwal into the method of gathering, by a command prediction module of the application executing on a computing device, command data and user characteristic data for the user of Zheng for the purpose of filtering out irrelevant options that are provided to users, this leaves the user with a choice among a relatively smaller number of options (Aggarwal: [0021]).
Zheng does not teach generating by the trained recurrent neural network model separate next command prediction for each of a plurality of different values of one or more characteristics by clustering final hidden states from a last hidden layer of the trained recurrent neural network model, associating each cluster with a value of one or more user characteristics, and having the output layer of the trained recurrent neural network model produce the separate next command predictions based on the final hidden states from each cluster.
However in analogous art of recommending next commands using recurrent neural networks, Yao teaches generating by the trained recurrent neural network model separate next command prediction for each of a plurality of different values of one or more characteristics by clustering final hidden states from a last hidden layer of the trained recurrent neural network model, associating each cluster with a value of one or more user characteristics, and having the output layer of the trained recurrent neural network model produce the separate next command predictions based on the final hidden states from each cluster ([0044], [0053], [0061-0062], [0071-0073], [0077], [0083], [0085], [0093] wherein Yao communicates commands based on predicting the surrounding environment of a vehicle, and wherein GRU is the gated recurrent units of the motion encoder with hidden state vector and an encoding module that merges and aggregates the final hidden states, wherein the final fused hidden state is an output as hidden state vector of GRU. Wherein the commands are updated for specific participant).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Yao with Zheng by incorporating the method of generating by the trained recurrent neural network model separate next command prediction for each of a plurality of different values of one or more characteristics by clustering final hidden states from a last hidden layer of the trained recurrent neural network model, associating each cluster with a value of one or more user characteristics, and having the output layer of the trained recurrent neural network model produce the separate next command predictions based on the final hidden states from each cluster of Yao into the method of gathering, by a command prediction module of the application executing on a computing device, command data and user characteristic data for the user of Zheng for the purpose of providing commands to the neural network to convert sampled data into plurality of images frames that may include one or more past data points (Yao: [0056]).
Regarding claim 3, Zheng as modified by Aggarwal and Yao teach wherein the trained neural network model is a trained gated recurrent unit (GRU) neural network model and the last hidden layer is a last GRU layer ([0044], [0053], [0061-0062], [0071-0073], [0077], [0083], [0085], [0093] wherein Yao teaches a neural network model that is trained gated recurrent unit (GRU) with hidden layer that is last GRU layer).
Regarding claim 4, Zheng as modified by Aggarwal teaches wherein the applying further comprises: extracting a past command sequence from the input dataset; encoding the past command sequence as a plurality of vectors; and providing the plurality of vectors to an input layer of the trained recurrent neural network model ([0014], [0058], [0065] wherein Aggarwal describes to a machine-learning model that is trained using a machine-learning system to perform an assessment on log data that includes user behaviors, wherein the log data is extracted and wherein a multi-layered Long Short-Term Memory (LSTM) is used to encode the input sequences of commands into vectors of fixed dimensionality).
Regarding claim 5, Zheng as modified by Aggarwal teaches wherein the trained recurrent neural network model is configured to produce an associated confidence level for each command within each separate next command prediction, and the selecting further comprises selecting a command having a greatest confidence level or selecting each command having an associated confidence level above a given threshold ([0043], [0062], [0087] wherein Zheng categorizes application command recommendations based a confidence score, wherein the command recommendations may also be categorized into a highly likely category labeled as “best action” and another category labeled as “actions.” Best action may identify commands that are highly likely to correspond with the user's desired intent. This may be determined based on a confidence score provided by the multilingual command recommendation model. The remaining commands may also be displayed based on their ranking which may be determined based on a probability or confidence score. This reduces cognitive load and may enable the user to quickly and efficiently identify a desired command).
Regarding claim 6, Zheng as modified by Aggarwal teaches wherein the gathering further comprises: processing the command data to determine the user characteristics data, the processing to include comparing aspects of the command data to one or more thresholds ([0043] wherein Zheng calculating the probability selection for multiple commands, wherein the commands have a probability that is higher than a predetermined threshold may be selected as the recommended commands. The probability calculations may be used to rank the recommended commands. For example, commands having higher probabilities may be ranked higher. The selected commands along with their probability scores and/or their rankings may be provided as the output. Thus, the multilingual command recommendation model may receive a search query as an input and may provide one or more recommendations in a desired language as an output).
Regarding claim 7, Zheng as modified by Aggarwal teaches wherein the gathering further comprises soliciting the user to provide the user characteristics data in the user interface of the application ([0021-0025], [0039], [0045], [0051] wherein Aggarwal supports training user on the different functions that are available on a particular application that the recommendation system supports. When a novice user lacks the skills and knowledge to choose the different options provided, recommender systems act as a guide for the user in making a selection. Specifically, a user may make a click selection (e.g., select command or actions that registered in log data when a user interacts with the interface of the analytics system).
Regarding claim 8, Zheng as modified by Aggarwal teaches wherein the cleaning further comprises: removing commands on a predetermined list of commands from the command data; removing instances of sequential commands that occur more than a threshold number of times from the command data; or removing commands that occur less frequently than a threshold from the command data ([0039], [0058], [0060] wherein Aggarwal identifies total number of unique commands wherein certain commands can be dropped and discarded, and wherein the analytics system can filter out irrelevant commands, leaving primarily commands that are relevant to the user goal, and the analytics system can also provide guidance to a novice user to allow efficient data analysis).
Regarding claim 9, Zheng as modified by Aggarwal teaches comparing an actual next command selected by the user to the one or more recommended next commands; and in response to the actual next command not matching any of the one or more recommended next commands, there being less than a threshold number of previous incorrect predictions and one or more recommended next commands having a confidence level of above a threshold level, determining the user switched tasks ([0043], [0062], [0087] wherein Zheng categorizes application command recommendations based a confidence score, wherein the command recommendations may also be categorized into a highly likely category labeled as “best action” and another category labeled as “actions.” Best action may identify commands that are highly likely to correspond with the user's desired intent. This may be determined based on a confidence score provided by the multilingual command recommendation model. The remaining commands may also be displayed based on their ranking which may be determined based on a probability or confidence score. This reduces cognitive load and may enable the user to quickly and efficiently identify a desired command).
Regarding claim 10, Zheng as modified by Aggarwal teaches comparing an actual next command selected by the user to the one or more recommended next commands; and in response to the actual next command not matching any of the one or more recommended next commands, there being greater than a threshold number of previous incorrect predictions and one or more recommended next commands having a confidence level of above a threshold level, determining the user is having difficulty operations the application ([0043], [0062], [0087] wherein Zheng categorizes application command recommendations based a confidence score, wherein the command recommendations may also be categorized into a highly likely category labeled as “best action” and another category labeled as “actions.” Best action may identify commands that are highly likely to correspond with the user's desired intent. This may be determined based on a confidence score provided by the multilingual command recommendation model. The remaining commands may also be displayed based on their ranking which may be determined based on a probability or confidence score. This reduces cognitive load and may enable the user to quickly and efficiently identify a desired command), ([0021-0025], [0039], [0045], [0051] wherein Aggarwal supports training user on the different functions that are available on a particular application that the recommendation system supports. When a novice user lacks the skills and knowledge to choose the different options provided, recommender systems act as a guide for the user in making a selection. Specifically, a user may make a click selection (e.g., select command or actions that registered in log data when a user interacts with the interface of the analytics system).
Regarding claim 11, Zheng as modified by Aggarwal teaches comparing an actual next command selected by the user to the one or more recommended next commands; and in response to the actual next command matching one of the one or more recommended next commands, there being greater than a threshold number of previous correct predictions, and one or more recommended next commands having a confidence level of above a threshold level, determining the user well-understands how to operate the application ([0043], [0062], [0087] wherein Zheng categorizes application command recommendations based a confidence score, wherein the command recommendations may also be categorized into a highly likely category labeled as “best action” and another category labeled as “actions.” Best action may identify commands that are highly likely to correspond with the user's desired intent. This may be determined based on a confidence score provided by the multilingual command recommendation model. The remaining commands may also be displayed based on their ranking which may be determined based on a probability or confidence score. This reduces cognitive load and may enable the user to quickly and efficiently identify a desired command), ([0021-0025], [0039], [0045], [0051] wherein Aggarwal supports training user on the different functions that are available on a particular application that the recommendation system supports. When a novice user lacks the skills and knowledge to choose the different options provided, recommender systems act as a guide for the user in making a selection. Specifically, a user may make a click selection (e.g., select command or actions that registered in log data when a user interacts with the interface of the analytics system).
Regarding claim 12, Zheng teaches a computing device configured to recommend one or more next commands to a user of an application, the computing device comprising: a processor; and a memory coupled to the processor, the memory configured to maintain a command prediction module of the application that when executed on the processor is operable to ([0005], [0055], [0070]). The claim is similar in scope to claim 1 therefore the claim is rejected under similar rationale.
Regarding claim 14, Zheng as modified by Aggarwal teaches wherein the one or more user characteristics comprise a user skill level or a user industry sector (Abstract, [0021-0022], [0025], [0039-0040], [0045], [0051] wherein Aggarwal describes the steps of command engine selecting a next command recommendation from commands based on analysis that includes the level of user’s skills).
Regarding claim 15, Zheng teaches a computing device configured to recommend one or more next commands to a user of an application, the computing device comprising: a processor; and a memory coupled to the processor, the memory configured to maintain a command prediction module of the application that when executed on the processor is operable to ([0077]). The claim is similar in scope to claim 1 therefore the claim is rejected under similar rationale.
Regarding claim 16, the claim is similar in scope to claim 4 therefore the claim is rejected under similar rationale.
Regarding claim 17, the claim is similar in scope to claim 4 therefore the claim is rejected under similar rationale.
Regarding claim 18, the claim is similar in scope to claim 5 therefore the claim is rejected under similar rationale.
Regarding claim 19, the claim is similar in scope to claim 6 therefore the claim is rejected under similar rationale.
Regarding claim 20, the claim is similar in scope to claim 8 therefore the claim is rejected under similar rationale.
Regarding claim 16, the claim is similar in scope to claim 4 therefore the claim is rejected under similar rationale.
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 extension fee 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.
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/HASSAN MRABI/Examiner, Art Unit 2144
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