CTNF 18/390,767 CTNF 91334 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of the Claims Claims 1-20 are pending for examination. Claims 1, 11 and 16 are independent Claims. Claims 1-20 are rejected under 35 U.S.C. §§101, 103. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception without significantly more. Independent Claims As Claims 1, 11, 16: Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes. Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . See the analysis below. The Claim recites: A computer-implemented method comprising: obtaining data pertaining to at least one of input device-related movement and input device-related action, and associated with a user using an application at a first temporal instance; predicting at least one of one or more input device-related movements and one or more input device-related actions to be carried out, at a second temporal instance subsequent to the first temporal instance, in connection with the user using the application, by processing at least a portion of the obtained data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions; wherein the method is performed by at least one processing device comprising a processor coupled to a memory. The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Regarding the non-emphasized limitations: Step 2A prong 1: “ predicting at least one of one or more input device-related movements and one or more input device-related actions to be carried out, at a second temporal instance subsequent to the first temporal instance, in connection with the user using the application, by processing at least a portion of the obtained data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions; ” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions. These steps are considered mental processes group of abstract idea. Step 2A prong 2: Limitations “ obtaining data pertaining to at least one of input device-related movement and input device-related action, and associated with a user using an application at a first temporal instance; wherein the method is performed by at least one processing device comprising a processor coupled to a memory. ” are insignificant extra solution activity. See MPEP §2106.05(g). The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No. Limitation “ wherein the method is performed by at least one processing device comprising a processor coupled to a memory” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). Limitation “ obtaining data pertaining to at least one of input device-related movement and input device-related action, and associated with a user using an application at a first temporal instance; ” appears to be directed to receiving or transmitting data over a network which is well known, routine or conventional as evidenced by the court cases cited at MPEP 2106.05(d). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”). The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. Dependent Claims As Claim 2, 12 and 17, the Claim recites “ further comprising: training the one or more machine learning techniques using input device-related movement data and input device-related action data derived from multiple users using one or more input devices on one or more applications . ” The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “ further comprising: training the one or more machine learning techniques using input device-related movement data and input device-related action data derived from multiple users using one or more input devices on one or more applications ” are mere instructions to apply an exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible. As Claim 3, 13 and 18, the Claim recites “ wherein training the one or more machine learning techniques comprises training at least two machine learning models comprising (i) training a first machine learning model using historical input device-related movement data and historical input device-related action data derived from the user using a given type of input device on the application, and (ii) training a second machine learning model using historical input device-related movement data and historical input device-related action data derived from multiple additional users using the given type of input device on the application .” The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “ wherein training the one or more machine learning techniques comprises training at least two machine learning models comprising (i) training a first machine learning model using historical input device-related movement data and historical input device-related action data derived from the user using a given type of input device on the application, and (ii) training a second machine learning model using historical input device-related movement data and historical input device-related action data derived from multiple additional users using the given type of input device on the application ” are mere instructions to apply an exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible. As Claim 4, the Claim recites “ wherein performing one or more automated actions comprises automatically outputting, via at least one interface implemented in connection with the application, the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions .” The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “ wherein performing one or more automated actions comprises automatically outputting, via at least one interface implemented in connection with the application, the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions ” are mere instructions to apply an exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible. As Claim 5, 14 and 19, the Claim recites “ wherein performing one or more automated actions comprises automatically executing the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions on the application upon receiving user instruction, subsequent to the outputting and via the at least one interface implemented in connection with the application, of the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions .” The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “ wherein performing one or more automated actions comprises automatically executing the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions on the application upon receiving user instruction, subsequent to the outputting and via the at least one interface implemented in connection with the application, of the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions ” are mere instructions to apply an exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible. As Claim 6, the Claim recites “ wherein predicting at least one of one or more input device-related movements and one or more input device-related actions comprises processing the at least a portion of the obtained data using one or more deep neural network-based algorithms .” The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “ wherein predicting at least one of one or more input device-related movements and one or more input device-related actions comprises processing the at least a portion of the obtained data using one or more deep neural network-based algorithms ” are mere instructions to apply an exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible. As Claim 7, 15 and 20, the Claim recites “ , wherein processing the at least a portion of the obtained data using one or more deep neural network-based algorithms comprises processing the at least a portion of the obtained data using at least one of one or more recurrent neural networks (RNNs) .” The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s). Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea . Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “ , wherein processing the at least a portion of the obtained data using one or more deep neural network-based algorithms comprises processing the at least a portion of the obtained data using at least one of one or more recurrent neural networks (RNNs) ” are mere instructions to apply an exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application. Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-4, 6-8, 10-13, 15-18 and 20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Verma et al. (U.S. 20200410392 hereinafter Verma) . As Claim 1, Verma teaches a computer-implemented method comprising: obtaining data pertaining to at least one of input device-related movement and input device-related action (Verma (¶0005 last 9 lines), “commands are low level interactions ( e.g., selecting a dimension ( input device related action ), sorting a column, a drag-and-drop operation ( input device related movement ), among others) with a user interface of a software application. A sequence of commands can be aimed at achieving a given task of the software application.”) , and associated with a user using an application at a first temporal instance (Verma (¶0118 line 6-10, fig. 13 item 1302), “process 1300 includes obtaining a sequence of commands performed by a user of an application. For example, the sequence of commands can include the commands performed by a user up to a current point in time”) ; predicting at least one of one or more input device-related movements and one or more input device-related actions to be carried out, at a second temporal instance subsequent to the first temporal instance, in connection with the user using the application (Verma (¶0120 line 3-5), “The task distribution includes an indication of whether the sequence of commands is associated with at least a first task or a second task of the application”) , by processing at least a portion of the obtained data using one or more machine learning techniques (Verma (¶0121 line 1-6), “the task distribution can be determined based on unsupervised topic modeling using a bi-term topic model (BTM), as described above. For example, as shown in FIG. 14, the bi-term topic model (BTM) 1404 can receive the sequence of commands 1402 as input, and can output a task distribution 1408. ”) ; and performing one or more automated actions (Verma (¶0130 line 1-3, fig. 13 item 1312), “the process 1300 includes outputting the command (or the multiple commands) as a recommendation to the application.”) based at least in part on the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions (Verma (¶0127 line 2-7), “after the InlineTextChange command in the sequence of commands 1402 is performed, the probability distribution 1412 of the set of possible commands can be determined to include, for the first task, a highest probability for a SaveSegment command and a second highest probability for a PanelCollapse command”) ; wherein the method is performed by at least one processing device comprising a processor coupled to a memory (Verma (¶0132 line 7-13), “the computing device or apparatus may include an input device, a task identification engine, a command recommendation engine, a help modeling engine, an output device, one or more processors, one or more microprocessors, one or more microcomputers, and/or other component(s) that is/are con figured to carry out the steps of process 1300”) . As Claim 2, besides Claim 1, Verma teaches further comprising: training the one or more machine learning techniques using input device-related movement data and input device-related action data derived from multiple users using one or more input devices on one or more applications (Verma (¶0044 line 9-13), “the training set and the test set can be split on the basis of the users. For example, twenty percent of users can be chosen for the test set, and the remaining eighty percent of the users can be chosen for the training set”) . As Claim 3, besides Claim 2, Verma teaches wherein training the one or more machine learning techniques comprises training at least two machine learning models comprising (i) training a first machine learning model using historical input device-related movement data and historical input device-related action data derived from the user using a given type of input device on the application (Verma (¶0037 line 1-3), “The task identification engine 102 can be trained to identify the ongoing task a user is performing ( or attempting to perform) for an application.”) , and (ii) training a second machine learning model using historical input device-related movement data and historical input device-related action (Verma (¶0044 last 10 lines), “the log data 101 can include data indicating commands performed up to a point in time, which can be used during inference mode (when a user is using the analytics platform) to identify tasks and recommend commands, and to determine when help is needed”) data derived from multiple additional users (Verma (¶0044 last 10 lines), “the training set and the test set can be split on the basis of the users. For example, twenty percent of users can be chosen for the test set, and the remaining eighty percent of the users can be chosen for the training set”) using the given type of input device on the application (Verma (¶0038 last 10 lines), “The command recommendation engine 104 can model command recommendations through a machine learning system architecture (e.g., a recurrent neural network (RNN)-based architecture or other machine learning or neural network architecture) by incorporating the task information at the input layer”) . As Claim 4, besides Claim 1, Verma teaches wherein performing one or more automated actions comprises automatically outputting, via at least one interface implemented in connection with the application (Verma (¶0111 line 1-5, fig. 10), “As shown in the proactive help panel 1004, a proactive help icon 1010 on the GUI is highlighted when it is determined that a user needs help with the software application. Additional information is presented on the GUI when the help icon 1010 is selected”) , the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions (Verma (¶0130 line 1-3, fig. 13 item 1312), “the process 1300 includes outputting the command (or the multiple commands) as a recommendation to the application.”) . As Claim 6, besides Claim 1, Verma teaches wherein predicting at least one of one or more input device-related movements and one or more input device-related actions comprises processing the at least a portion of the obtained data using one or more deep neural network-based algorithms (Verma (¶0006 last 3 lines), “A command recommendation model can include a machine learning system, such as a recurrent neural network (RNN)”) . As Claim 7, besides Claim 6, Verma teaches wherein processing the at least a portion of the obtained data using one or more deep neural network-based algorithms comprises processing the at least a portion of the obtained data using at least one of one or more recurrent neural networks (RNNs) (Verma (¶0006 last 3 lines), “A command recommendation model can include a machine learning system, such as a recurrent neural network (RNN)”) and one or more long short-term memory (LSTM) networks. As Claim 8, besides Claim 7, Verma teaches wherein processing the at least a portion of the obtained data using one or more LSTM networks comprises using one or more LSTM networks, trained in an unsupervised manner, in conjunction with at least one autoencoder architecture (Verma (¶0075 line 8-11), “a particular type ofRNN, called a multi-layered Long Short-Term Memory (LSTM) model, can be used to encode the input sequence of commands into command vectors of fixed dimensionality.”) . As Claim 10 , besides Claim 1, Verma teaches wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions (Verma (¶0101 line 5-11), “during a first iteration of the model of the LSTM classifier 908 (when the model has not yet learned anything), the command embeddings are randomly initialized. Over second, third and further iterations, the random command embeddings are updated (in effect, learned) in order to minimize the loss function (which relates to predicting the correct next command in the sequence).”) . As Claims 11-13 and 15 , the Claims are rejected for the same reasons as Claims 1-3 and 7, respectively. As Claims 16-18 and 20 , the Claims are rejected for the same reasons as Claims 1-3 and 7, respectively . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 5, 14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Verma in view of Love (U.S. 20200401628 hereinafter Love) . As Claim 5, besides Claim 4, Verma does not explicitly disclose: wherein performing one or more automated actions comprises automatically executing the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions on the application upon receiving user instruction, subsequent to the outputting and via the at least one interface implemented in connection with the application, of the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions. Love teaches: wherein performing one or more automated actions comprises automatically executing the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions on the application upon receiving user instruction, subsequent to the outputting and via the at least one interface implemented in connection with the application, of the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions (Love (¶0038 last 10 lines, fig. 6), “the visualization system 110 can detect user interaction with a row based on consecutive mouse movements (or touches on a touchscreen) across row cells, with a designated error ratio, which suggests that the user is interested in that row. In one embodiment, labels for parameters for which the entity has outlier values, such as parameter 3 in FIG. 6, are visually distinguishable from the remaining parameters. The figure label may thus serve as a visual indicator to a viewing user that the entity 602 has an outlier values for the associated parameter.”) . Verma disclose a system/method to predict user next interaction based on a current sequence of interactions. Love suggests the automatically execution of an action based on user interaction. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify suggestion of Verma instead be an auto action taught by Verma, with a reasonable expectation of success. The motivation would be to allow “the visualization system 110 may, responsive to a user selection or automatically, display correlated data within the same radial map.” (Love (¶0041 line 1-3)). As Claims 14 and 19 , the Claims are rejected for the same reasons as Claim 5 . 07-21-aia AIA Claim (s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Verma in view of Marantz et al. (U.S. 20150269176 hereinafter Marantz) . As Claim 9, besides Claim 1, Verma does not explicitly disclose wherein obtaining data comprises obtaining a plurality of identifying information of the user, identifying information of the application, information pertaining to type of input device, pixel coordinates associated with the at least one of input device-related movement and input device-related action, identifying information of the input device-related action, and timestamp information associated with the at least one of input device-related movement and input device-related action. Marantz teaches: wherein obtaining data comprises obtaining a plurality of identifying information of the user (Marantz (¶0039 last 6 lines), “The QPS 102 can then access a separate social network database to discover a subset of movies that the user's friends ( identifying information of the user ) liked.”) , identifying information of the application (Marantz (¶0123 line 1-3), “With respect to the specific topic of penalty scores, the query interpretation module 1504 can compute these values in any application-specific manner”) , information pertaining to type of input device (Marantz (¶0041 line 3-7), “will be assumed that a query corresponds to an alphanumeric string that is input by the user through any input mechanism (e.g., a keypad mechanism, a touch-sensitive screen input mechanism, a voice recognition mechanism, and so on)”) , pixel coordinates associated with the at least one of input device-related movement and input device-related action (Marantz (¶0084 line 7-9), “the user may make a hover-type selection by moving a mouse-controlled pointer over the query suggestion 904, or by clicking on the query suggestion 904”) , identifying information of the input device-related action (Marantz (¶0084 line 7-9), “the user may make a hover-type selection by moving a mouse-controlled pointer over the query suggestion 904, or by clicking on the query suggestion 904”) , and timestamp information associated with the at least one of input device-related movement and input device-related action (Marantz (¶0081 line 8-11), “The suggestion generating module 110, in turn, generates the refinement options based on a determination of rule modules and/or entity items which match the user's input query, at the present time,”) . Verma disclose a system/method to predict user next interaction based on a current sequence of interactions. Marantz suggests personalization suggestion to the user based on user action and profile. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify suggestion of Verma instead be personal suggestion taught by Marantz, with a reasonable expectation of success. The motivation would be to allow “generate and present a query refinement tool to the user, e.g., in the form of a bar or other user interface feature. The query refinement tool presents a plurality of refinement options that pertain to the current state of the user's input query.” (Marantz (¶0005)) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sultan (U.S. 2020/0326822) disclose a method for next user interaction prediction . Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147 Application/Control Number: 18/390,767 Page 2 Art Unit: 2147 Application/Control Number: 18/390,767 Page 3 Art Unit: 2147 Application/Control Number: 18/390,767 Page 4 Art Unit: 2147 Application/Control Number: 18/390,767 Page 5 Art Unit: 2147 Application/Control Number: 18/390,767 Page 6 Art Unit: 2147 Application/Control Number: 18/390,767 Page 7 Art Unit: 2147 Application/Control Number: 18/390,767 Page 8 Art Unit: 2147 Application/Control Number: 18/390,767 Page 9 Art Unit: 2147 Application/Control Number: 18/390,767 Page 10 Art Unit: 2147 Application/Control Number: 18/390,767 Page 11 Art Unit: 2147 Application/Control Number: 18/390,767 Page 12 Art Unit: 2147 Application/Control Number: 18/390,767 Page 13 Art Unit: 2147 Application/Control Number: 18/390,767 Page 14 Art Unit: 2147 Application/Control Number: 18/390,767 Page 15 Art Unit: 2147 Application/Control Number: 18/390,767 Page 16 Art Unit: 2147 Application/Control Number: 18/390,767 Page 17 Art Unit: 2147 Application/Control Number: 18/390,767 Page 18 Art Unit: 2147 Application/Control Number: 18/390,767 Page 19 Art Unit: 2147
Prosecution Insights