Detailed Action
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The amendment filed 10/29/25 has been entered. Claims 1, 6-9, 14-17, 19-22, 24-30 are pending.
3. This application is a continuation of S.N. 17/196720 filed 3/9/21, now U.S. Patent 11,972,272, which itself is a continuation of S.N. 16/447033 filed 6/20/19, now U.S. Patent 10,977,058. A Terminal Disclaimer filed 4/18/25 has been entered.
Claim Rejections - 35 USC § 112
4. In view of the amendment and Applicant’s remarks, the 112 rejection has been removed.
Claim Rejections - 35 USC § 103
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
6. 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.
7. Claims 1, 7-9, 14, 16-17, 19, 21-22, 24, and 26 are rejected under 35 U.S.C. § 103 as being unpatentable over Hosbettu et al. (US 2017/0228119 A1, hereinafter Hosbettu) and Gandhi et al. (US 2019/0155894 A1, hereinafter Gandhi).
As to independent claim 1, Hosbettu teaches a method comprising:
receiving, from a set of client devices and via a network, a data set (Hosbettu, FIG. 9, paragraph 0053, client devices 901, 909 and 910 communicate via network 908. FIG. 1, paragraph 0022,” Further, an activity/action log database 150 may store the parameters, actions, activities and flow order associated with each image.” [activity/action log maps to data set] Also, FIG. 8, paragraph 0042, log 810), each element of the data set comprising:
… data for user interactions with user interfaces in performing a plurality of tasks (Hosbettu, FIG. 3, paragraph 0025, FIG. 4, paragraph 0026-0028, Logged activities or tasks are associated with user interfaces providing forms such as that shown in FIG. 4. Interactions for tasks are performed such as clicking a leave approval button or inputting a comment or displaying an approval/reject message. Screen name maps to business object for a given activity log entry or screen instance, paragraph 0037, shows the screen evaluator may capture all the relevant actions and variables, and timeline. These all constitute various user interactions with the user interfaces top perform the various plurality of tasks);
based on data for the task, generating a bot to automatically perform the task (Hosbettu, paragraph 0005, Also, the method may include generating … a process automation model [maps to automated task] based on the identified subset of the stored one or more sequences of user interactions, using the generated one or more threshold values. FIG. 7, paragraphs 0020 and 0027-0041 as cited above also show the bot is generated to perform the task.), the generating of the bot comprising determining a parameter of the bot that controls entry into a data entry field (Hosbettu, FIG. 7, paragraph 0039, “At block 740, a Parameter Evaluator may find the parameters that associate the value of variables in different actions.” Hosbettu, FIG. 7, paragraph 0037, “Also, the screen evaluator may capture all relevant actions and variables occurring in the screen” and thus the bot controls entry into a data field).
generating a parameter validation code for the parameter, the parameter validation code configured to detect invalid parameter values (Hosbettu, paragraph 0021, 0022, 0025, 0027, 0039, 0046 show a system validating its learned models including the parameters associated with the bot. FIG. 8, paragraphs 0044-0047 - Confirmatory predictor vectors, confusion vectors, and dynamic thresholds for the vectors equate to generated parameter validation code that detects invalid parameter values);
running the generated bot (Hosbettu, paragraph 0020, “Once the icon is activated, the self-learning bot may execute and develop the capability to accomplish the user's goal. Based on understanding of the goals, it may continuously learn the rules about actions, sequence of steps etc. and quickly accomplish the task.” [Task bot is run and updated simultaneously.] FIG. 2, paragraph 0023, “In some embodiments, screen analyzer 110 may repeat the process flow performed by the user.”);
the running of the generated bot comprising: receiving by the bot data indicating a modified value of the parameter and validating the modified value of the parameter using the parameter validation code for the parameter (Hosbettu, paragraph 0021 shows validating its learned models, utilizing confusion vectors with adaptive threshold, as explained below, FIG. 8, paragraphs 0045-0047 show confirmatory predictor vectors, confusion vectors, and dynamic thresholds for the vectors equate to generated parameter validation code and using it on the modified values).
Hosbettu does not explicitly show generating of the bot comprises determining a default value per se for a parameter that controls data entry into a data entry field. In analogous art, Gandhi teaches a method including a machine learning model to determine default parameters included in automation objects that facilitate form data capture which equate to the automated process bots generated per Hosbettu (Gandhi, paragraphs 0023, 0027 and 0028). Gandhi teaches what does not appear to be expressly taught by Hosbettu for a method comprising:
… generating of the bot comprising determining a default value for a parameter that controls data entry into a data entry field (Gandhi, paragraph 0021, “In this regard, the form generation parameters [bot parameters] refer to properties that are defined, coded or scripted into a set of form generation automation rules that are applied for automatically generating forms.” Paragraph 0028, “At a high level a selected machine learning algorithm operates to determine a relevance score for fields of a form and determine default values (e.g., autofill or auto-suggest values) for fields.”).
Note that Hosbettu para 0026 shows activities the bot would perform involve “…a leave form. The form may include various screen elements such as labels… include elements that perform functions upon user activation” and para 0028 show “…a leave approval button may be single-clicked, which may lead to a leave approval screen form…textbox may be filled with comments input, and an approve button may be single-clicked” It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu to include determining default values for the automated task per Gandhi for reasons taught by Gandhi. Gandhi teaches these determination method steps to personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
Although Hosbettu shows:
receiving, by the bot, interactive user input (Hosbettu, FIG. 7, paragraph 0037, “Also, the screen evaluator may capture all relevant actions and variables occurring in the screen, and timeline, i.e., the order as well as the time window.” [User input is received while running the bot.]).
Nevertheless Hosbettu does not appear to expressly teach:
receiving, by the bot, interactive user input to modify the default value.
Gandhi expressly teaches what does not appear to be expressly taught by Hosbettu for a method further comprising:
receiving… interactive user input to modify the default value (Gandhi, paragraph 0014, “Form field values (" user input" or "user information") can refer to information entered by a user into the form.” Paragraph 0028, “At a high level a selected machine learning algorithm operates to determine a relevance score for fields of a form and determine default values (e.g., autofill or auto-suggest values) for fields.” Paragraph 0047, “The user session data can refer to data received as user inputs after fields…current session user information can be iteratively processed in a data capture experience to generate successive form data”).
It would have been obvious to a person with ordinary skill before the effective filing date of the claimed invention to modify Hosbettu per Gandhi, because it would provide and efficient way to personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
As to dependent claim 2, the rejection of parent claim 1 is incorporated. Hosbettu teaches wherein the accessing of the modified value of the parameter comprises:
receiving, by the bot, interactive user input…
As to dependent claim 3, the rejection of parent claim 1 is incorporated. Hosbettu teaches wherein the accessing of the modified value of the parameter comprises:
accessing, by the bot, data from a data file … (Hosbettu, FIG. 2, paragraph 0023, “And at step 260, screen analyzer 110 may identify any change in the user actions and sequence for a given screen compared to previous monitoring trials where the user utilized the same screen.” [File of data recorded for previous monitoring is accessed.]).
Hosbettu does not appear to expressly teach a method further comprising:
accessing… a data file to modify the default value.
Gandhi expressly teaches what does not appear to be expressly taught by Hosbettu for a method further comprising:
accessing… a data file to modify the default value (Gandhi, paragraph 0027, “In this regard, the machine learning model generator component also includes automation functions values (i.e., values for auto-fill function or auto-suggest function) [default values]. Given data from all user submission data and weighted parameters, the machine learning model generator generates a model that can receive the annotated schema, user profile data (which can include demographic data) and user submission data [accessed data files] to provide a set of probable values for some of the fields and the relevance score of each field or section.”).
The motivation to modify Hosbettu per Gandhi is the same as that given for claim 1, namely to efficiently personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
As to dependent claim 4, the rejection of parent claim 3 is incorporated. Hosbettu teaches a method:
wherein the accessing of the data from the data file comprises processing a known field location in the data file using optical character recognition (OCR) (Hosbettu, FIG. 2, paragraph 0023, “In some embodiments, screen analyzer 110 may use image processing techniques (e.g., edge detection, Optical Character Recognition (OCR)) to get contours and edges, and deducing various elements from the screen and labeling them… At step 230, screen analyzer 110 may detect the objects (e.g., text boxes [known field location], labels, button, drop-down lists, sliders, etc.) present in the screen of the graphical user interface. At step 240, screen analyzer 110 may identify the objects on the screen (e.g., by determining the function(s) associated with the objects).”).
As to dependent claim 7, the rejection of parent claim 1 is incorporated. Hosbettu teaches wherein the running of the bot comprises:
automatically interacting with a user interface of the selected task (Hosbettu, paragraph 0020, “Embodiments of the present disclosure provide systems, wherein an icon is presented on a user's environment (e.g., desktop). Once the icon is activated, the self-learning bot may execute and develop the capability to accomplish the user's goal.”).
As to dependent claim 8, the rejection of parent claim 1 is incorporated. (Please also note the alternative recitation). Hosbettu teaches wherein the generated parameter validation code for the parameter includes a list of acceptable values for the parameter (Hosbettu, paragraph 0044, 0047 show threshold sets of acceptable values).
Claim 9 shows the same features as claim 1 and is rejected for the same reasons. Furthermore, Hosbettu para 0005, 0054 show one or more processors and the memory that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method. Furthermore, Hosbettu paragraph 0020, 0046 shows providing the bot to a client device of the set of client devices and via the network.
As to dependent claim 10, the rejection of parent claim 9 is incorporated. Hosbettu teaches wherein the accessing of the modified value of the parameter comprises:
receiving, by the bot, interactive user input… (Hosbettu, FIG. 7, paragraph 0037, “Also, the screen evaluator may capture all relevant actions and variables occurring in the screen, and timeline, i.e., the order as well as the time window.” [User input is received while running the bot.]).
Hosbettu does not appear to expressly teach a method further comprising:
receiving… interactive user input to modify the default value.
Gandhi expressly teaches what does not appear to be expressly taught by Hosbettu for a method further comprising:
receiving… interactive user input to modify the default value (Gandhi, paragraph 0014, “Form field values (" user input" or "user information") can refer to information entered by a user into the form.” Paragraph 0028, “At a high level a selected machine learning algorithm operates to determine a relevance score for fields of a form and determine default values (e.g., autofill or auto-suggest values) for fields.” ).
The motivation to modify Hosbettu per Gandhi is the same as that given for claim 9, namely to efficiently personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
As to dependent claim 11, the rejection of parent claim 9 is incorporated. Hosbettu teaches the accessing of the modified value of the parameter comprises:
accessing, by the bot, data from a data file … (Hosbettu, FIG. 2, paragraph 0023, “And at step 260, screen analyzer 110 may identify any change in the user actions and sequence for a given screen compared to previous monitoring trials where the user utilized the same screen.” [File of data recorded for previous monitoring is accessed.]).
Hosbettu does not appear to expressly teach a method further comprising:
accessing… a data file to modify the default value.
Gandhi expressly teaches what does not appear to be expressly taught by Hosbettu for a method further comprising:
accessing… a data file to modify the default value (Gandhi, paragraph 0027, “In this regard, the machine learning model generator component also includes automation functions values (i.e., values for auto-fill function or auto-suggest function) [default values]. Given data from all user submission data and weighted parameters, the machine learning model generator generates a model that can receive the annotated schema, user profile data (which can include demographic data) and user submission data [accessed data files] to provide a set of probable values for some of the fields and the relevance score of each field or section.”).
The motivation to modify Hosbettu per Gandhi is the same as that given for claim 9, namely to efficiently personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
As to dependent claim 12, the rejection of parent claim 11 is incorporated. Hosbettu teaches a method:
wherein the accessing of the data from the data file comprises processing a known field location in the data file using optical character recognition (OCR) (Hosbettu, FIG. 2, paragraph 0023, “In some embodiments, screen analyzer 110 may use image processing techniques (e.g., edge detection, Optical Character Recognition (OCR)) to get contours and edges, and deducing various elements from the screen and labeling them… At step 230, screen analyzer 110 may detect the objects (e.g., text boxes [known field location], labels, button, drop-down lists, sliders, etc.) present in the screen of the graphical user interface. At step 240, screen analyzer 110 may identify the objects on the screen (e.g., by determining the function(s) associated with the objects).”).
As to dependent claim 14, the rejection of parent claim 9 is incorporated. Hosbettu teaches a system:
wherein the generating of the bot comprises determining that two fields of a user interface have the same value (Hosbettu, FIG. 1, paragraph 0029, “Again with reference to FIG. 1, in some embodiments, a rule generator 120 may build a decision tree (rules) with valid values and extremas, and optimize the rules using confirmatory predictors...Find an order [relationship] with time frame and start and end actions with associated variables and values [bot parameters];” FIG. 4, paragraph 0026, Bot actions with associated variable values/parameters include form field values as shown in FIG. 4. FIG. 5, paragraph 0027, “Order of activity” column, 560, indicates a relationship order between textbox input and selected buttons which equate to fields of a user interface.)
Claim 16 shows the same features as claim 9 and is rejected for the same reasons. Furthermore, Hosbettu para 0007, 0054, 0059 show the non-transitory computer readable medium that stores instructions that when executed by one or more processors cause the one or more processors to perform the operations.
As to dependent claim 17, the rejection of parent claim 1 is incorporated. (Please also note the alternative recitation). Hosbettu teaches wherein the generated parameter validation code for the parameter includes a list of acceptable values for the parameter (Hosbettu, para 0044, 0047 show threshold sets of acceptable values).
Claim 19 show the same features as claim 14 and is rejected for the same reasons.
As to dependent claim 21, the rejection of parent claim 2 is incorporated. Hosbettu teaches the accessing of the modified value of the parameter comprises:
determining that the interactive user input comprises a value for the parameter that is invalid (Hosbettu para 0022, 0025 show a model validator and error table which logs the determined errors in parameters); and
causing a pop-up window to be presented that indicates that the value is invalid (Hosbettu para 0022 shows error messages being presented in various interface options including pop-up screens).
As to dependent claim 22, the rejection of parent claim 10 is incorporated. Hosbettu teaches the accessing of the modified value of the parameter comprises:
determining that the interactive user input comprises a value for the parameter that is invalid (Hosbettu para 0022, 0025 show a model validator and error table which logs the determined errors in parameters); and
causing a pop-up window to be presented that indicates that the value is invalid (Hosbettu para 0022 shows error messages being presented in various interface options including pop-up screens).
As to dependent claim 23, the rejection of parent claim 16 is incorporated. Hosbettu teaches the accessing of the modified value of the parameter comprises:
receiving, by the bot, interactive user input… (Hosbettu, FIG. 7, paragraph 0037, “Also, the screen evaluator may capture all relevant actions and variables occurring in the screen, and timeline, i.e., the order as well as the time window.” [User input is received while running the bot.]).
Hosbettu does not appear to expressly teach a method further comprising:
receiving… interactive user input to modify the default value.
Gandhi expressly teaches what does not appear to be expressly taught by Hosbettu for a method further comprising:
receiving… interactive user input to modify the default value (Gandhi, paragraph 0014, “Form field values (" user input" or "user information") can refer to information entered by a user into the form.” Paragraph 0028, “At a high level a selected machine learning algorithm operates to determine a relevance score for fields of a form and determine default values (e.g., autofill or auto-suggest values) for fields.” ).
The motivation to modify Hosbettu per Gandhi is the same as that given for claim 1, namely to efficiently personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
As to dependent claim 26, in addition to that mentioned for claim 1, Hosbettu does not appear to explicitly teach wherein the bot uses the default value for the parameter unless the value is overridden by the user. Gandhi expressly teaches what does not appear to be expressly taught by Hosbettu for a method further comprising: wherein the bot uses the default value for the parameter unless the value is overridden by the user (Gandhi, paragraph 0014, “Form field values (" user input" or "user information") can refer to information entered by a user into the form.” Paragraph 0028, “At a high level a selected machine learning algorithm operates to determine a relevance score for fields of a form and determine default values (e.g., autofill or auto-suggest values) for fields.” Paragraph 0047, “The user session data can refer to data received as user inputs after fields…current session user information can be iteratively processed in a data capture experience to generate successive form data”). Thus the default value is determined and used unless a user enters a new value, thus overriding the default one. It would have been obvious to a person with ordinary skill before the effective filing date of the claimed invention to modify Hosbettu per Gandhi, because it would provide and efficient way to personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
8. Claims 6, 15, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Hosbettu and Gandhi and Eder (US 2009/0043637 A1 hereinafter Eder).
As to dependent claim 6, the rejection of parent claim 1 is incorporated. Although Hosbettu teaches a method for generating rules for bots including arithmetic operations (Hosbettu, paragraph 0039, “The automatic rule generators can support arithmetic operators including basic logical operators (less than, equal, greater than, etc.)” Paragraph 0046), Hosbettu and Gandhi do not appear to expressly teach identifying or determining a rule or parameter for a bot by determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface, and based on the probability and a threshold, configuring the bot to set the value of the first field to be the sum of the other two fields.
In analogous art, Eder teaches a system and method for initializing and operating data bots that manipulate information utilized in system processing by an enterprise (Eder, paragraph 0026, FIG. 1, paragraph 0034). Eder teaches what is not expressly taught by Hosbettu and Gandhi for a method:
determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface (Paragraph 0164, “Bots are independent components of application software that have specific tasks to perform. In the case of causal predictive model bots, their primary task is to refine the element and factor variable selection to reflect only causal variables. [Note: these variables are summed together to value an element of value when they are interdependent].” The predictive modeling calculates probabilities of values of fields based on operations applied to other fields. Paragraphs 0282 and 0295 also show calculating probabilities for fields that involve other fields combined by having arithmetic operations applied to them). , and based on the probability and a threshold, configuring the bot to set the value of the first field to be the sum of the other two fields (Eder, paragraph 0018, “If attributes [parameters] from one element of value drive those from another, then the elements of value [parameters] can be combined for analysis and/or the impact of the individual attributes can be summed together to calculate a value for the element.” Paragraph 0164, “Bots are independent components of application software that have specific tasks to perform. In the case of causal predictive model bots, their primary task is to refine the element and factor variable selection to reflect only causal variables. (Note: these variables are summed together to value an element of value when they are interdependent).” Based on the predictive modeling, whereby the probability of the field taking on the calculated field based on other fields, reaches a threshold, the bot is assigned the predicted value. In this case it would thus be the sum of the other two fields).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, to include determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface, and based thereon configuring the bot to set the value of the first field as the sum of the other two fields per Eder for reasons taught by Eder. Eder teaches bots with summed parameter values to improve and enhance the efficiency and effectiveness of business valuation methods by automating the retrieval storage and analysis of information when valuing elements of value and segments of value for an organization (Eder, paragraphs 0012-0014).
As to dependent claim 15, the rejection of parent claim 10 is incorporated. Although Hosbettu teaches a method for generating rules for bots including arithmetic operations (Hosbettu, paragraph 0039, “The automatic rule generators can support arithmetic operators including basic logical operators (less than, equal, greater than, etc.)” Paragraph 0046), Hosbettu, and Gandhi do not appear to expressly teach identifying or determining a rule or parameter for a bot by determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface, and based on the probability and a threshold, configuring the bot to set the value of the first field to be the sum of the other two fields.
In analogous art, Eder teaches a system and method for initializing and operating data bots that manipulate information utilized in system processing by an enterprise (Eder, paragraph 0026, FIG. 1, paragraph 0034). Eder teaches what is not expressly taught by Hosbettu and Gandhi for a system:
determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface (Paragraph 0164, “Bots are independent components of application software that have specific tasks to perform. In the case of causal predictive model bots, their primary task is to refine the element and factor variable selection to reflect only causal variables. [Note: these variables are summed together to value an element of value when they are interdependent].” The predictive modeling calculates probabilities of values of fields based on operations applied to other fields. Paragraphs 0282 and 0295 also show calculating probabilities for fields that involve other fields combined by having arithmetic operations applied to them). , and based on the probability and a threshold, configuring the bot to set the value of the first field to be the sum of the other two fields (Eder, paragraph 0018, “If attributes [parameters] from one element of value drive those from another, then the elements of value [parameters] can be combined for analysis and/or the impact of the individual attributes can be summed together to calculate a value for the element.” Paragraph 0164, “Bots are independent components of application software that have specific tasks to perform. In the case of causal predictive model bots, their primary task is to refine the element and factor variable selection to reflect only causal variables. (Note: these variables are summed together to value an element of value when they are interdependent).” Based on the predictive modeling, whereby the probability of the field taking on the calculated field based on other fields, reaches a threshold, the bot is assigned the predicted value. In this case it would thus be the sum of the other two fields).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, to include determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface, and based thereon configuring the bot to set the value of the first field as the sum of the other two fields per Eder for reasons taught by Eder. Eder teaches bots with summed parameter values to improve and enhance the efficiency and effectiveness of business valuation methods by automating the retrieval storage and analysis of information when valuing elements of value and segments of value for an organization (Eder, paragraphs 0012-0014).
As to dependent claim 20, the rejection of parent claim 19 is incorporated. Although Hosbettu teaches a method for generating rules for bots including arithmetic operations (Hosbettu, paragraph 0039, “The automatic rule generators can support arithmetic operators including basic logical operators (less than, equal, greater than, etc.)” Paragraph 0046), Hosbettu and Gandhi do not appear to expressly teach identifying or determining a rule or parameter for a bot by determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface, and based on the probability and a threshold, configuring the bot to set the value of the first field to be the sum of the other two fields.
In analogous art, Eder teaches a system and method for initializing and operating data bots that manipulate information utilized in system processing by an enterprise (Eder, paragraph 0026, FIG. 1, paragraph 0034). Eder teaches what is not expressly taught by Hosbettu and Gandhi for a non-transitory computer readable medium:
determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface (Paragraph 0164, “Bots are independent components of application software that have specific tasks to perform. In the case of causal predictive model bots, their primary task is to refine the element and factor variable selection to reflect only causal variables. [Note: these variables are summed together to value an element of value when they are interdependent].” The predictive modeling calculates probabilities of values of fields based on operations applied to other fields. Paragraphs 0282 and 0295 also show calculating probabilities for fields that involve other fields combined by having arithmetic operations applied to them). , and based on the probability and a threshold, configuring the bot to set the value of the first field to be the sum of the other two fields (Eder, paragraph 0018, “If attributes [parameters] from one element of value drive those from another, then the elements of value [parameters] can be combined for analysis and/or the impact of the individual attributes can be summed together to calculate a value for the element.” Paragraph 0164, “Bots are independent components of application software that have specific tasks to perform. In the case of causal predictive model bots, their primary task is to refine the element and factor variable selection to reflect only causal variables. (Note: these variables are summed together to value an element of value when they are interdependent).” Based on the predictive modeling, whereby the probability of the field taking on the calculated field based on other fields, reaches a threshold, the bot is assigned the predicted value. In this case it would thus be the sum of the other two fields).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, to include determining a probability that a first field of a user interface has a value that is a sum of two other fields of the user interface, and based thereon configuring the bot to set the value of the first field as the sum of the other two fields per Eder for reasons taught by Eder. Eder teaches bots with summed parameter values to improve and enhance the efficiency and effectiveness of business valuation methods by automating the retrieval storage and analysis of information when valuing elements of value and segments of value for an organization (Eder, paragraphs 0012-0014).
9. Claims 24-25, 27-30 are rejected under 35 U.S.C. § 103 as being unpatentable over Hosbettu and Gandhi and Ternan et al (US 2014/0372269 A1 hereinafter Ternan).
As to dependent claim 24, Hosbettu and Gandhi do not explicitly show: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining, based on the data for the task, that a particular single value is used for the data entry field in more than a threshold percentage of interactions. Based on Applicant’s remarks, this limitation is interpreted to mean one of several potential values is found that nevertheless is used in more than a threshold percentage of interactions. Accordingly, this is the same thing as a most popular value for the data entry field among the data for the task, in that a most popular value is determined as being one of the values that is used in more than a threshold percentage of interactions. In analogous art, Ternan teaches a system and method for determining a default value for a parameter inputted on a form on a user interface (Ternan para 0034, Figure 3a). Ternan teaches what is not expressly taught by Hosbettu and Gandhi for a method: determining, based on the data for the task, that a particular single value is used for the data entry field in more than a threshold percentage of interactions (Ternan para 0034, Figure 3a, claim 6 show the determination of the default selector value is based on determining a most popular value that is entered into that field for that function more than a threshold percentage of interactions). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, for the determining of the default value for the parameter that controls data entry into the data entry field to comprise determining a most popular value for the data entry field among the data for the task, because it would provide an efficient way to provide a default parameter value based on previous user input data. Hosbettu especially as modified by Gandhi would be motivated to use the previous user input data to determine a convenient default value relevant for users, and Ternan provides the solution to accomplish this for the reasons taught by Ternan as explained above (Ternan para 0034).
As to dependent claim 25, Hosbettu and Gandhi do not explicitly show: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining a most popular value for the data entry field among the data for the task. In analogous art, Ternan teaches a system and method for determining a default value for a parameter inputted on a form on a user interface (Ternan para 0034, Figure 3a). Ternan teaches what is not expressly taught by Hosbettu and Gandhi for a method: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining a most popular value for the data entry field among the data for the task (Ternan para 0034, Figure 3a, claim 6 show the determination of the default selector value is based on determining a most popular value that is entered into that field for that function). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, for the determining of the default value for the parameter that controls data entry into the data entry field to comprise determining a most popular value for the data entry field among the data for the task, because it would provide an efficient way to provide a default parameter value based on previous user input data. Hosbettu especially as modified by Gandhi would be motivated to use the previous user input data to determine a convenient default value relevant for users, and Ternan provides the solution to accomplish this for the reasons taught by Ternan as explained above (Ternan para 0034).
As to dependent claim 27, Hosbettu and Gandhi do not explicitly show: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining, based on the data for the task, that a particular single value is used for the data entry field in more than a threshold percentage of interactions. Based on Applicant’s remarks, this limitation is interpreted to mean one of several potential values is found that nevertheless is used in more than a threshold percentage of interactions. Accordingly, this is the same thing as a most popular value for the data entry field among the data for the task, in that a most popular value is determined as being one of the values that is used in more than a threshold percentage of interactions. In analogous art, Ternan teaches a system and method for determining a default value for a parameter inputted on a form on a user interface (Ternan para 0034, Figure 3a). Ternan teaches what is not expressly taught by Hosbettu and Gandhi for a method: determining, based on the data for the task, that a particular single value is used for the data entry field in more than a threshold percentage of interactions (Ternan para 0034, Figure 3a, claim 6 show the determination of the default selector value is based on determining a most popular value that is entered into that field for that function more than a threshold percentage of interactions). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, for the determining of the default value for the parameter that controls data entry into the data entry field to comprise determining a most popular value for the data entry field among the data for the task, because it would provide an efficient way to provide a default parameter value based on previous user input data. Hosbettu especially as modified by Gandhi would be motivated to use the previous user input data to determine a convenient default value relevant for users, and Ternan provides the solution to accomplish this for the reasons taught by Ternan as explained above (Ternan para 0034).
As to dependent claim 28, Hosbettu and Gandhi do not explicitly show: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining a most popular value for the data entry field among the data for the task. In analogous art, Ternan teaches a system and method for determining a default value for a parameter inputted on a form on a user interface (Ternan para 0034, Figure 3a). Ternan teaches what is not expressly taught by Hosbettu and Gandhi for a system: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining a most popular value for the data entry field among the data for the task (Ternan para 0034, Figure 3a, claim 6 show the determination of the default selector value is based on determining a most popular value that is entered into that field for that function). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, for the determining of the default value for the parameter that controls data entry into the data entry field to comprise determining a most popular value for the data entry field among the data for the task, because it would provide an efficient way to provide a default parameter value based on previous user input data. Hosbettu especially as modified by Gandhi would be motivated to use the previous user input data to determine a convenient default value relevant for users, and Ternan provides the solution to accomplish this for the reasons taught by Ternan as explained above (Ternan para 0034).
As to dependent claim 29, Hosbettu and Gandhi do not explicitly show: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining, based on the data for the task, that a particular single value is used for the data entry field in more than a threshold percentage of interactions. Based on Applicant’s remarks, this limitation is interpreted to mean one of several potential values is found that nevertheless is used in more than a threshold percentage of interactions. Accordingly, this is the same thing as a most popular value for the data entry field among the data for the task, in that a most popular value is determined as being one of the values that is used in more than a threshold percentage of interactions. In analogous art, Ternan teaches a system and method for determining a default value for a parameter inputted on a form on a user interface (Ternan para 0034, Figure 3a). Ternan teaches what is not expressly taught by Hosbettu and Gandhi for a method: determining, based on the data for the task, that a particular single value is used for the data entry field in more than a threshold percentage of interactions (Ternan para 0034, Figure 3a, claim 6 show the determination of the default selector value is based on determining a most popular value that is entered into that field for that function more than a threshold percentage of interactions). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, for the determining of the default value for the parameter that controls data entry into the data entry field to comprise determining a most popular value for the data entry field among the data for the task, because it would provide an efficient way to provide a default parameter value based on previous user input data. Hosbettu especially as modified by Gandhi would be motivated to use the previous user input data to determine a convenient default value relevant for users, and Ternan provides the solution to accomplish this for the reasons taught by Ternan as explained above (Ternan para 0034).
As to dependent claim 30, Hosbettu and Gandhi do not explicitly show: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining a most popular value for the data entry field among the data for the task. In analogous art, Ternan teaches a system and method for determining a default value for a parameter inputted on a form on a user interface (Ternan para 0034, Figure 3a). Ternan teaches what is not expressly taught by Hosbettu and Gandhi for a non-transitory computer readable medium: wherein the determining of the default value for the parameter that controls data entry into the data entry field comprises determining a most popular value for the data entry field among the data for the task (Ternan para 0034, Figure 3a, claim 6 show the determination of the default selector value is based on determining a most popular value that is entered into that field for that function). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu, especially as modified by Gandhi, for the determining of the default value for the parameter that controls data entry into the data entry field to comprise determining a most popular value for the data entry field among the data for the task, because it would provide an efficient way to provide a default parameter value based on previous user input data. Hosbettu especially as modified by Gandhi would be motivated to use the previous user input data to determine a convenient default value relevant for users, and Ternan provides the solution to accomplish this for the reasons taught by Ternan as explained above (Ternan para 0034).
10. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968).
Response to Arguments
11. Applicant's arguments filed 10/29/25 have been fully considered but they are not persuasive. Regarding independent claim 1, Applicant argues that Hosbettu and Gandhi do not show “receiving, by the bot, interactive user input to modify the default value, resulting in a modified value of the parameter.” However, as pointed out in the Action, Hosbettu para 0020 shows “Once the icon is activated, the self-learning bot may execute and develop the capability to accomplish the user's goal. Based on understanding of the goals, it may continuously learn the rules about actions, sequence of steps etc. and quickly accomplish the task.” Thus the updating of the task bot including any parameter changes happen while the task bot is running. See also FIG. 2, paragraph 0023 which shows “In some embodiments, screen analyzer 110 may repeat the process flow performed by the user.” This is not merely validating an automation model as Applicant argues, but indeed is interactive user input received by the bot. Also note that the training of the bot, such as in para 0047-0048, may happen even while the bot is running.
Applicant further argues that the cited portions of Gandhi only show determining a default value and not interactive user input by a bot. Para 0047 of Gandhi shows “The user session data can refer to data received as user inputs after fields…current session user information can be iteratively processed in a data capture experience to generate successive form data.” Note specifically that this occurs during a user session and thus while the bot is running, and for example the user input can then be iteratively processed in a (real time bot session) data capture to generate successive form data. Applicant characterizes this as data defined for forms, but this in any case is interactive user input received by the bot and it will modify some default value of a parameter. If applicant means anything more as to the nature of the parameter or value, then this needs to be brought out in the claim language.
Furthermore, Applicant argues that Hosbettu does not show creating a form for users to complete and that therefore the motivation to use Gandhi cannot be applied to Hosbettu. However, note that Hosbettu para 0026 shows activities the bot would perform involve “…a leave form. The form may include various screen elements such as labels… include elements that perform functions upon user activation” and para 0028 show “a leave approval button may be single-clicked, which may lead to a leave approval screen form…textbox may be filled with comments input, and an approve button may be single-clicked” and so indeed the motivation to use Gandhi would apply to Hosbettu. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method for generating a bot for automatically performing tasks as taught by Hosbettu to include determining default values for the automated task per Gandhi for reasons taught by Gandhi. Gandhi teaches these determination method steps to personalize and optimize a form completing experience to increase the number of users that complete the form (Gandhi, paragraphs 0015, 0040-0041).
Applicant argues that the dependent claims are allowable for the same reasons mentioned for the independent claims and then specifically singles out dependent claims 21-22, arguing that the interactive user input mentioned is not shown in Hosbettu. In response, please note that as just explained above, Hosbettu does indeed show the interactive user input. The portions of Hosbettu cited for dependent claims 21-22 show the model validator and error table which logs the determined errors in parameters (thus showing determining invalid parameter values); and error messages being presented in various interface options including pop-up screens (thus showing a pop-up window is presented when an error is indicated, such as indicating that the parameter value is determined to be invalid). But the “interactive user input” is the same as that shown in Hosbettu for that feature in independent claim 1, from which claims 21 and 22 each depend. Applicant does not argue anything more regarding these dependent claims.
Conclusion
12. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Purushothaman, US 2019/0171513 A1 is cited for teaching a system to create a robotic script for a remediation process for remediating a computing related event based on recorded actions (see Abstract, FIG. 4, paragraphs 0001-0025, 0063-0068).
Tirk (WO 2009139869 A1) show generating validation code for parameter values of a bot.
Huang (CN 110008023 A) analyses the completion time to do tasks for a bot application model.
Swinke (US 2018/0144126) indicates recommendations for automating tasks.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PAUL SAX whose telephone number is (571)272-4072. The examiner can normally be reached Monday - Friday, 9:30 - 6:00 Est.
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/STEVEN P SAX/Primary Examiner, Art Unit 2146