DETAILED ACTION
This Office Action is in response to claims filed on 07/10/2024.
Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 13, 15, 17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goyal et al. Pub. No. US 2022/0147386 A1 (hereinafter Goyal).
Goyal was cited in IDS filed 07/10/2024.
With regard to claim 1, Goyal teaches a method for providing a task automation service, performed by at least one computing device ([0004], Embodiments disclosed herein concern improved techniques for combining a plurality of distinct recordings pertaining to user interactions with one or more software applications each of which concerns performing a task. Then, the combined recording can be used to form a software automation process for performing the task in an automated fashion.), the method comprising:
collecting information of an object, in which a user’s input occurs, by using a recording engine ([0038], FIG. 1 is a simplified block diagram of a robotic process automation (RPA) utilization environment 100 according to one embodiment. The RPA utilization environment 100 serves to support recordation of a series of user interactions of a user with one or more application programs operating on a computing device. In the case that the recording pertains to a same or similar task, the recording can sometimes be merged to yield an aggregated recording.)
generating an object information list for the object based on the information collected by the recording engine ([0040], In general, a recording is an electronic record of a series of user interactions, such as actions or events, with one or more software programs operating on one or more computing devices. The recording can be stored in an electronic file. The data stored within the electronic file can denote the order in which user interactions occurred. The electronic file can, for example, use a structured format, such as a JSON format, to detail the data within the recording; [0049], In one embodiment, pre-processing can begin by storing from the electronic files of the recordings into a table in a database. He database table, for a given step in a given recording, can store a recording-step key and various attributes for such step.);
determining first object information corresponding to the user’s input ([0051], Additionally, from the attributes associated with a given step of a recording, a fingerprint can be determined for that step. The fingerprint is a contextual identifier for a step.) based on a result of comparing a plurality of kinds of candidate object information included in the object information list ([0058], FIG. 4B is a diagram of an aggregator 450 according to one embodiment. The aggregator 450 can, for example, pertain to the aggregator 314 illustrated in FIG. 3. The aggregator 450 can include a matched detection module; [0078], FIG. 7A is a flow diagram of an exemplary flowchart 700 of a recording prior to transformation processing. The exemplary flowchart 700 describes a sequence of steps for events or actions from the recording. In this example, the steps are provided in nodes of the exemplary flowchart 700. In particular, the exemplary flowchart 700 includes node A 702, node B 704, node C 706, node D 708, node E 710, and node F 712. Based on comparing contextual identifiers (e.g., fingerprints), nodes B and D are determined to be matching based on their matching fingerprints, and nodes C and E are determined to be matching based on their matching fingerprints.); and
automatically generating an activity corresponding to the user’s input based on the first object information and predefined pattern information ([0113], Consequently, the merge process produces the merged exemplary flowchart 1240 in which the first exemplary flowchart 1200 and the secondary exemplary flowchart 1220 are merged together. The merged exemplary flowchart 1240 includes an initial branch point 1242 that forms a left branch and a right branch.).
With regard to claim 2, Goyal teaches the method of claim 1
Goyal further teaches wherein the object information list includes user input information on the object ([0049], The database table, for a given step in a given recording, can store a recording-step key and various attributes for such step … action value can pertain to clientAction, buttonAction, textboxAction, and other similar values) and an object adjacent to the object ([0049], As examples, application name can pertain to explorer, chrome, java, and other similar names; [0050], Table I provided below provides an example of a database table for storing data from a recording (Examiner notes: Wherein the object information list details multi-application step recordings comprising adjacent objects).).
With regard to claim 3, Goyal teaches the method of claim 1
Goyal further teaches wherein the object information list includes information on any one of an application, a system and a program, in which the user’s input for the object is performed ([0049], In one embodiment, pre-processing can begin by storing data from the electronic files of the recordings into a table in a database. The database table, for a given step in a given recording, can store a recording-step key and various attributes for such step. The attributes can vary with implementation. However, some exemplary attributes include one or more of: application name, action name, action value, user interface (“UI”) criteria name, and class name.).
With regard to claim 4, Goyal teaches the method of claim 1
Goyal further teaches determining whether each of object information collected by the recording engine includes essential information set in advance ([0049], The database table, for a given step in a given recording, can store a recording-step key and various attributes for such step. The attributes can vary with implementation. However, some exemplary attributes include one or more of: application name, action name, action value, user interface (“UI”) criteria name, and class name (Examiner notes: Such that the recording engine maintains an implementation of preset attributes to record).); and
generating the object information list based on a result of the determination ([0050], In the example shown in Table I, the database table can include a recording-step key, which can be formatted as a recording number (e.g., 10 or 11) together with a step number (e.g., 1, 2, 3, …) in the recording. The attributes recorded in the database table for each of the steps in a given recording can then, for example, include application name, action name, and action type.).
With regard to claim 13, Goyal teaches the method of claim 1
Goyal further teaches wherein the automatically generating the activity corresponding to the user’s input ([0064], FIG. 6 is a flow diagram of a recording transformation process 600 according to one embodiment. In general, the recording transformation process 600 can, for example, be performed by the RPA system 102 illustrated in FIG. 1, the RPA system 202 illustrated in FIG. 1, the RPA system 202 illustrated in FIG. 2, or the RPA system 300 in illustrated in FIG. 3, as examples.) includes:
determining whether there is an activity of the same pattern as that of the activity ([0068], Next, the recording transformation process 600 can determine whether the digital representation includes a repeating sequence of user-initiated events. A decision 610 can determine whether a repeating sequence has been found.); and
automatically generating an activity corresponding to the pattern based on a result of the determination ([0068], When the decision 610 determines that a repeating sequence has been found, the digital representation can be modified 612 to denote the repeating sequence).
With regard to claim 15, Goyal teaches a system for providing a task automation service, the system comprising:
an object information collection module collecting information on an object, in which a user’s input occurs, by using a recording engine ([0038], FIG. 1 is a simplified block diagram of a robotic process automation (RPA) utilization environment 100 according to one embodiment. The RPA utilization environment 100 serves to support recordation of a series of user interactions of a user with one or more application programs operating on a computing device. In the case that the recording pertains to a same or similar task, the recording can sometimes be merged to yield an aggregated recording.) and generating an object information list for the object based on the collected information ([0040], In general, a recording is an electronic record of a series of user interactions, such as actions or events, with one or more software programs operating on one or more computing devices. The recording can be stored in an electronic file. The data stored within the electronic file can denote the order in which user interactions occurred. The electronic file can, for example, use a structured format, such as a JSON format, to detail the data within the recording; [0049], In one embodiment, pre-processing can begin by storing from the electronic files of the recordings into a table in a database. He database table, for a given step in a given recording, can store a recording-step key and various attributes for such step.);
an object information determination module ([0046], The RPA system 202 can also include an aggregator 236. The aggregator 236 can be used to consolidate multiple recordings into a single recording. For example, a plurality of separate recordings that were made following a series of user-initiated interactions with one or more software programs can be combined into a more robust recording.) determining first object information corresponding to the user’s input ([0051], Additionally, from the attributes associated with a given step of a recording, a fingerprint can be determined for that step. The fingerprint is a contextual identifier for a step.) based on a result of comparing a plurality of kinds of candidate object information included in the object information list ([0058], FIG. 4B is a diagram of an aggregator 450 according to one embodiment. The aggregator 450 can, for example, pertain to the aggregator 314 illustrated in FIG. 3. The aggregator 450 can include a matched detection module; [0078], FIG. 7A is a flow diagram of an exemplary flowchart 700 of a recording prior to transformation processing. The exemplary flowchart 700 describes a sequence of steps for events or actions from the recording. In this example, the steps are provided in nodes of the exemplary flowchart 700. In particular, the exemplary flowchart 700 includes node A 702, node B 704, node C 706, node D 708, node E 710, and node F 712. Based on comparing contextual identifiers (e.g., fingerprints), nodes B and D are determined to be matching based on their matching fingerprints, and nodes C and E are determined to be matching based on their matching fingerprints.); and
an activity generation module automatically generating an activity corresponding to the user’s input based on the first object information and predefined pattern information ([0113], Consequently, the merge process produces the merged exemplary flowchart 1240 in which the first exemplary flowchart 1200 and the secondary exemplary flowchart 1220 are merged together. The merged exemplary flowchart 1240 includes an initial branch point 1242 that forms a left branch and a right branch.).
With regard to claim 17, it is a system having similar limitations to claim 13. Thus, claim 17 is rejected for the same rationale as applied to claim 13.
With regard to claim 19, Goyal teaches a system for providing a task automation service ([0135], FIG. 17 illustrates a block diagram of an exemplary computing environment 1700 for an implementation of an RPA system, such as the RPA systems disclosed herein. The embodiments described herein may be implemented using the exemplary computing environment 1700), the system comprising:
a processor ([0135], The processing exemplary computing environment 1700 includes one or more processing unit 1702, 1704.); and
a memory ([0135], and memory 1706, 1708) storing an instruction ([0135], The tangible memory 1705, 1708 may be volatile memory …, non-volatile memoery, … or some combination of the two, accessible by the processing units; [0137], The tangible storage 1710 can stroe instructions for the software implementing one or more features of a RPA system as described herein.),
wherein the instruction is executed by processor ([0135], The processing units 1702, 1706, execute computer-executable instructions.), the processor performs;
an operation of collection information of an object, in which a user’s input occurs, by using a recording engine ([0038], FIG. 1 is a simplified block diagram of a robotic process automation (RPA) utilization environment 100 according to one embodiment. The RPA utilization environment 100 serves to support recordation of a series of user interactions of a user with one or more application programs operating on a computing device. In the case that the recording pertains to a same or similar task, the recording can sometimes be merged to yield an aggregated recording.);
an operation of generating an object information list for the object based on the information collected by the recording engine ([0040], In general, a recording is an electronic record of a series of user interactions, such as actions or events, with one or more software programs operating on one or more computing devices. The recording can be stored in an electronic file. The data stored within the electronic file can denote the order in which user interactions occurred. The electronic file can, for example, use a structured format, such as a JSON format, to detail the data within the recording; [0049], In one embodiment, pre-processing can begin by storing from the electronic files of the recordings into a table in a database. He database table, for a given step in a given recording, can store a recording-step key and various attributes for such step.);
an operation of determining first object information corresponding to the user’s input ([0051], Additionally, from the attributes associated with a given step of a recording, a fingerprint can be determined for that step. The fingerprint is a contextual identifier for a step.) based on a result of comparing a plurality of kinds of candidate object information included in the object information list ([0058], FIG. 4B is a diagram of an aggregator 450 according to one embodiment. The aggregator 450 can, for example, pertain to the aggregator 314 illustrated in FIG. 3. The aggregator 450 can include a matched detection module; [0078], FIG. 7A is a flow diagram of an exemplary flowchart 700 of a recording prior to transformation processing. The exemplary flowchart 700 describes a sequence of steps for events or actions from the recording. In this example, the steps are provided in nodes of the exemplary flowchart 700. In particular, the exemplary flowchart 700 includes node A 702, node B 704, node C 706, node D 708, node E 710, and node F 712. Based on comparing contextual identifiers (e.g., fingerprints), nodes B and D are determined to be matching based on their matching fingerprints, and nodes C and E are determined to be matching based on their matching fingerprints.); and
an operation of automatically generating an activity corresponding to the user’s input based on the first object information and predefined pattern information ([0113], Consequently, the merge process produces the merged exemplary flowchart 1240 in which the first exemplary flowchart 1200 and the secondary exemplary flowchart 1220 are merged together. The merged exemplary flowchart 1240 includes an initial branch point 1242 that forms a left branch and a right branch.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5-12, 14, 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. Pub. No. US 2022/0147386 A1 (hereinafter Goyal) in view of Weng et al. Patent No. US 8,176,467 B2 (hereinafter Weng).
With regard to claim 5, Goyal teaches the method of claim 1
Goyal further teaches wherein the first object information includes a user input type for the object, a location of the object, a size of the object and process information on the object ([0049], As examples, application name can pertain to explorer, chrome, java, and other similar names; action value can pertain to LEFT_CLICK, SET_TEXT, and other similar values; action value can pertain to clientAction, buttonAction, textboxAction, and other similar values; UI criteria name can pertain to Recorder warning, New Tab, Close and other similar criteria names; and class name can pertain to Chrome_WidgetWin_1, GlassWndClass-GlassWindowClass-3), and other similar class names.).
However, Goyal does not explicitly teach object information including a location of the object or size of the object.
In analogous art, Weng teaches a location of the object, a size of the object (Col. 10,lines 12-20, In a further embodiment, recorder utility 116 (FIG. 1) can use the information related to the position of the screen where the event occurred and determine control based on what application had control of that area of the screen. For example, if user 150 (FIG. 1) clicks a button on a mouse, monitor module 115 (FIG. 1) can determine the x- and y-coordinates of the mouse pointer on the screen where the click occurred and what application had control of that area of the screen.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches first object information include object location and size data. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for combining known prior art element of collecting of object location and size data with the known prior art elements of recording user action data with particular attributes to yield predictable result, with reasonable expectation of success. Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide object location and area data in association with recorded user action data in order to determine particular user interactions.
With regard to claim 6, Goyal teaches the method of claim 1
Goyal reasonably teaches first object determination corresponding to a user input (Goyal, [0051]). However, Goyal does not explicitly teach continuous input determination for a particular object from the object information list.
In analogous art, Weng teaches wherein the determining the first object information corresponding to the user’s input includes:
selecting one kind of object information from the object information list (Col. 9, lines 31-40, In one embodiment, when an event occurs, monitor module 115 (FIG. 1) invokes an instance of recorder utility 116 (FIG. 1) in the next step 1138 of process 224 in FIG. 11. Once invoked, recorder utility 116 collects data about the event from hooks 117 (FIG. 1), application 152 (FIG. 1), and operating system 151 (FIG. 1) in next step 1139 of process 224 in FIG. 11. In one embodiment, the data collected about the event includes the action of user 150 (FIG. 1), the object of the action of user 150, and the identifying information about the active window when the action occurred.);
determining whether the user’s input corresponding to the selected object information is continuous input for the object (Col. 9, lines 51-53, In the same or different embodiment, recorder utility can combine multiple events that occur in a short period into a single event; Col. 9, lines 64-66, In one embodiment, after collecting the information in step 1139 in FIG. 11, recorder utility 116 (FIG. 1) can decide whether the event is related to application 152 (FIG. 1) in step 1140 of process 224 in FIG. 11.); and
determining the selected object information as candidate object information based on a result of the determination (Col. 10, lines 12-20, In a further embodiment, recorder utility 116 (FIG. 1) can use the information related to the position on the screen where the event occurred and determine control based on what application had control of that area of the screen.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches continuous input object determination. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for combining known prior art element of object continuity determination with the known prior art elements of object pattern matching and activity generation to yield predictable result, with reasonable expectation of success. Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide object continuity generation for candidate pattern matching.
With regard to claim 7, Goyal and Weng teaches the method of claim 6
Weng further teaches wherein whether the user’s input is the continuous input for the object is determined based on whether objects, in which the user’s input occurs, are the same (Col. 9, lines 53-56, For example, if user 150 (FIG. 1) presses multiple keystrokes on the keyboard within the same text box in the same window in a short period of time, recorder utility 116 (FIG. 1) can combine these multiple events in a single event.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches determination of continuous objects through relational user input. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for combining known prior art element of collecting and comparing relevant, related user input commands with the known prior art elements of object pattern matching and activity generation to yield predictable result, with reasonable expectation of success. Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide relational continuous user input determination.
With regard to claim 8, Goyal and Weng teaches the method of claim 6
Goyal further teaches wherein the determining the selected object information as the candidate object information is performed ([0083], The merge process 800 can begin with a decision 802 that can determine whether recordings are to be merged. When the decision 802 determines that recordings are not to be merged at this time, the merge process 800 can wait until recordings are to be merged.)
Goyal reasonably teaches select object information processing (Goyal, [0051]). However, Goyal does not explicitly teach selected object information comprising continuous input and wait conditions.
Weng teaches only when it is determined that the user’s input is the continuous input for the object and it is determined that it is not necessary to wait for a user’s next input (Col. 10, lines 21-25, In some embodiments, if the event is not related to application 152 (FIG.1), recorder utility 116 (FIG.1) does not save the information about the event, and recorder utility 116 goes into a waiting state until invoked again by monitor module 115 (FIG. 1); Col. 10, lines 34-43, For following events, the data recorded at step 1141 by recorder utility 116 include the actions of user 150, the element of the object of the action, and the active window. The information recorded includes (a) user actions for application 152 in the scenario; (b) operating system information related to the user actions and the scenario; and (c) information about application 152 that is related to the user actions and the scenario. The data is saved in memory and will be used by correlative action module 104 to create the data objects and/or code generation module 105 to create the source code.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches selection of object information with respect to continuous input and wait conditions. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for combining known prior art element of recording complete, continuous user input interactions with the known prior art elements of object pattern matching and activity generation to yield predictable result, with reasonable expectation of success. Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide captured user input completely and related attributes accurately to properly generate an automated process.
With regard to claim 9, Goyal teaches the method of claim 6
Weng further teaches wherein whether it is necessary to wait for the user’s next input is determined based on a user’s input type (Col. 9, lines 51-53, recorder utility can combine multiple events that occur in a short period into a single event … if user quickly clicks a button on a mouse twice, recorder utility 116 (FIG. 1) can combine the two single click events into one double click event. In a further example, if user 150 (FIG. 1) presses a special key on the keyboard (e.g., “Ctrl”, “Shift”, or “Alt” keys) and then another key on the keyboard, recorder utility 116 can combine the two separate keystroke events in a single event (Examiner notes: Such that particular user input types necessarily requires a wait for additional user input).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches user’s input type wait conditions. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for combining known prior art element of special key events comprising combinational user input types with the known prior art elements of object pattern matching and activity generation to yield predictable result, with reasonable expectation of success. Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide user input wait events associated with special key combination input types.
With regard to claim 10, Goyal and Weng teaches the method of claim 6
Weng further teaches wherein the determining the selected object information as the candidate object information includes:
determining the object information as candidate object information (Col. 9-Col. 10, lines 64-67 and lines 1-3, In one embodiment, after collecting the information in step 1139 in FIG. 11, recorder utility 116 (FIG. 1) can decide whether the event is related to application 152 (FIG. 1) in step 1140 of process 224 in FIG. 11. As an example, recorder utility 116 can determine whether the event is related to application 152 by establishing what application spawned the window that was active when the event occurred.), based on a result of determining whether it is necessary to wait for a user’s next input when it is determined that the user’s input is not the continuous input for the object (Col. 10, lines 21-25, In some embodiments, if the event is not related to application 152 (FIG. 1), recorder utility 116 (FIG. 1) does not save information about the event and recorder utility 116 goes into a waiting state until invoked again by monitor module 115 (FIG. 1).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches continuous object information resolution determination conditions. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for combining known prior art element of resolving continuous user input conditions with the known prior art elements of object pattern matching and activity generation to yield predictable result, with reasonable expectation of success. Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide user input processing associated with validating continuous object information conditions.
With regard to claim 11, Goyal and Weng teaches the method of claim 6
Goyal reasonably teaches object information list comprising information related to application name, action name, action value, user interface (“UI”) criteria name, and class name (Goyal, [0049]). However, Goyal does not explicitly teach object information comprising location and area subsets of a particular application.
Weng further teaches wherein the first object information corresponding to the user’s input is object information having a small object size among the plurality of kinds of candidate object information included in the object information list (Col. 10, For example, if user 150 (FIG. 1) clicks a button on a mouse, monitor module 115 (FIG. 1) can determine the x- and y-coordinates of the mouse point on the screen when the click occurred and what application had control of that area of the screen (Examiner notes: Such that the first object information corresponds to the particular button location and area corresponding to the control area of a recorded application).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches specific area and location specific object information. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for clarifying specificity of particular objects associated with an application under control, such that enables granular process automation (Weng, Col. 8). Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide granular automation of limited area and location specific objects.
With regard to claim 12, Goyal and Weng teaches the method of claim 6
Weng further teaches wherein the first object information corresponding to the user’s input is determined based on process information on the object among the plurality of kinds of candidate object information included in the object information list (Col. 10, lines 27-42, If, however, the event does relate to application 152 (FIG.1), the next step 1141 of process 224 in FIG.11 is saving the data. For lead events, the data recorded at step 1141 by recorder utility 116 (FIG. 1) can include the action of user 150 (FIG. 1), the element selected or modified by the actions of user 150, and possibly also the top-level window containing this element. For following events, the data recorded at step 1141 by recorder utility 116 include the actions of user 150, the element of the object of the action, and the active window. The information recorded includes (a) user actions for application 152 in the scenario; (b) operating system information related to the user actions and the scenario; and (c) information about application 152 that is related to the user actions and the scenario. The data is saved in memory and will be used by correlative action module 104 to create the data objects).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Weng with the teachings of Goyal in order to provide a method that teaches object determination based on process information included in saved object information list. The motivation for applying Weng teaching with Goyal teaching is to provide a method that allows for particular object information processing only within a particular recorded process such that exclusive processing of relevant object information (Weng, Col. 10). Goyal and Weng are analogous art directed towards execution arrangements and process automation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Weng with Goyal to teach the claimed invention in order to provide scope relevant processing of object information associated with a particular recorded process.
With regard to claim 14, Goyal and Weng teaches the method of claim 6
Goyal further teaches wherein the pattern includes a combination of one or more activities including a user’s input type, a number of inputs and information on the object in which the user’s input occurs ([0076], Upon receiving a linear flowchart …, then the processing can determine whether the flowchart includes a simple loop. The processing can search for a loop with at least “s” steps which has “f” distinct contextual identifiers (e.g., fingerprints) and the same sequence is immediately repeated at least “n” times; [0077], As an example, given an exemplary sequence of <a,b,c,d,e,c,d,e,c,d,e,f>, the processing can cause the exemplary sequence to be transformed into <a,b,[c,d,e],f> if the parameters s=3, f=3, n=3 are used. As noted above, in one embodiment, the context identifiers (e.g., fingerprints) for each step can be based on five fields (application name, action name, action type, class name, and UI criteria name).).
With regard to claim 16, it is a system having similar limitations to claim 5. Thus, claim 16 is rejected for the same rationale as applied to claim 5.
With regard to claim 18, it is a system having similar limitations to claim 14. Thus, claim 18 is rejected for the same rationale as applied to claim 14.
With regard to claim 20, it is a system having similar limitations to claim 5. Thus, claim 20 is rejected for the same rationale as applied to claim 5.
Conclusion
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/I.A.C./Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195
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