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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 13 February 2026 has been entered.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-2, 4-5, and 13-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Semar et al. (NPL from IDS: “Automating SmartPhone Automation”, published Dec. 2015, hereinafter “Semar”) in view of Lee et al. (NPL: “A prediction and auto-execution system of smartphone application services based on user context-awareness”, published April 2014, hereinafter “Lee”).
Regarding claim 1, Semar teaches a control method, comprising:
when a first action relating to a first content occurs in a first context, recognizing a first operation specified by the first context, the first content, and the first action (Semar, Section IV Sub-Section G Paragraph 5 – “Our algorithm uses time intervals to identify each unique pattern as opposed to the specific time as routines do not occur at a precise time for every occurrence. An illustration of this fact would be the time that a user may arrive at work throughout the week, which can range in a number of minutes as opposed to the precise second.” – teaches using a first action (user arriving) relating to a first content (arriving at work) in a first context (time, location) to recognize a first operation (identify a unique pattern) specified by the first context, content, and action)
detecting, from a usage pattern database (DB), a first usage pattern having a preceding operation corresponding to the first operation (Semar, Fig. 4 – depicts pseudocode for the AutoMate Learning Algorithm which describes detecting from a usage pattern database (“if pattern does not already exist in database”) a first usage pattern having a preceding operation corresponding to the first operation (searches database for matching action and context));
setting a trigger condition (Semar, Fig. 4 – “Create a pattern using the changed setting as the Action and the current context of the device (time, location) as the Triggers” and “When a pattern reaches the defined threshold” – teaches setting trigger conditions) and an intent action based on a following operation of the first usage pattern (Semar, Fig. 4 – “Recommend to the user as a routine in a notification” – teaches generating an intent action based on a following operation (action of the recommended routine) of the first usage pattern); and
building a user interface (UI) widget comprising the preceding operation, the following operation, the trigger condition, and the intent action, the UI widget including interactive elements which can be selected by a user to enable or disable the first usage pattern (Semar, Fig. 7 and Section IV Subsection H Paragraph 1 – “In addition to autonomous automation, AutoMate also allows users to manually edit and further configure routines. Once a routine is recommended through a notification, the user is taken to the routine configuration screen as seen in Figure 7, where they can add or remove as many actions and triggers as they would like to configure the routine to their specific preferences.” – teaches building a user interface (UI) widget (Fig. 7 shows the user interface element) comprising the preceding operation (user opening or configuring settings at certain location such as school, as in Fig. 2 “setting up location” and Fig. 7), the following operation (user setting ringer to silent at certain location such as school, as in Fig. 7 “silent at school”), the trigger condition (the user being at school, as in Fig. 7 – “Enabled Triggers” “Location: School”), and the intent action (turning user mobile device ringer to silent while at school. as in Fig. 7 “Actions” “Ringer: Silent and Vibrate”), the UI widget including interactive elements which can be selected by a user to enable or disable the first usage pattern (Fig. 7 shows user interface elements that may be selected by user to enable, disable, and configure the usage pattern));
performing, when the first usage pattern is enabled via the UI widget, the intent action as the trigger condition is satisfied (Semar, Fig. 4 – “When a pattern reaches the defined threshold: Recommend to the user as a routine in a notification If user accepts recommendation: Enforce as rule and automate the routine” and Fig. 7 – teaches performing, when the first usage pattern is enabled via the UI widget (Fig. 7 shows usage pattern enabled within user interface element), the intent action as the trigger condition is satisfied (Fig. 4 shows if user accepts routine, routine is automated and thus performs intent action as trigger condition is satisfied)).
Semar fails to explicitly teach wherein the first content is user created content; parsing the user created content; and setting a trigger condition and an intent action based at least in part on parsing the user created content.
However, analogous to the field of the claimed invention, Lee teaches:
wherein the first content is user created content (Lee, Section 4 Paragraph 3 – “The first module receives sensing data from four sensors and a phone system and generates a log with twelve properties of Simple Context”, Section 4, second set of bullet points, Bullet Point 2 – “AppID: the full name of App which is running at ‘time’” and Fig. 7 – teaches wherein the first content is user created content (log of twelve properties includes AppID, the application (or content) the user opened and is running on user smartphone at certain time, Fig. 7 shows user created content such as applications opened during certain context));
parsing the user created content (Lee, Section 3 Paragraph 3 – “Two modules work for training. A Simple Context generation & reduction module gathers training log data from a smartphone for a certain period of time and sends meaningful logs to a server. A user context inference module generates a context inference model including behavior patterns for a user. The model will be used by an App prediction module in the real-time phase.” and in Section 4 Paragraph 5 – “The second module uses a reduction method introduced in our previous research in order to reduce the cost and the data size for transmission between a smartphone and a server.” – teaches parsing the user created content (gathers log data from user smartphone for a certain period of time and sends meaningful logs to a server, thus logs are parsed and reduced for reducing cost and data size to send only meaningful logs to the server));
setting a trigger condition and an intent action based at least in part on parsing the user created content (Lee, Section 5.2 Numbered List 1-5 – “The module determines an App to automatically execute at the current point of time based on the pattern selected when inferring the current Situation. It finds the most similar item consisting of the pattern to decide the appropriate point of time. It calculates the similarity between the run-time log item and items in the selected pattern. For Time property, it uses the threshold value. And it checks two items exactly match for Space property and Behavior property. It finds the most similar item. It selects the App in the item or the next item.” – teaches setting a trigger condition (matching most similar item consisting of the pattern, uses threshold values such as threshold for time property) and an intent action (module determines an application to automatically execute based on selected pattern) based at least in part on parsing the user created content, as in Section 3 Paragraph 3 – “A Simple Context generation & reduction module gathers training log data from a smartphone for a certain period of time and sends meaningful logs to a server. A user context inference module generates a context inference model including behavior patterns for a user.” and Section 5.1 Second Numbered List, Point 1 – “A user context inference module receives a real-time log (Simple Context) and infers Behavior context with the trained Bayesian network model” – teaches setting trigger conditions and an intent action based at least in part on parsing the user created content (Behavior property is used in setting trigger condition for automatic application execution, Behavior property is based on received logs parsed for meaningful log data))
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the parsing of user created content and setting a trigger condition and an intent action based at least in part on parsing the user created content of Lee to the operations, usage patterns, and control method of Semar in order to set a trigger condition and action based on a following operation and at least in part on parsing user created content. Doing so would infer users’ meaningful context from sensing data and predict an appropriate application based on the run-time context to automatically execute it on a user’s smartphone (Lee, Introduction).
Claims 13 and 14 incorporates all the limitations of claim 1 in a non-transitory computer-readable storage medium and a control device, and are rejected on similar grounds as above.
Regarding claim 2, the combination of Semar and Lee teaches the control method of claim 1, wherein the preceding operation is defined based on a preceding context, a preceding content, and a preceding action, and the following operation is defined based on a following context, a following content, and a following action (Semar, Fig. 4 – “Create a pattern using the changed setting as the Action and the current context of the device (time, location) as the Triggers. If pattern does not already exist in the database: Add it as a new entry Else: If the previous occurrence of the pattern is on the same day:” – teaches preceding operations (operations stored in patterns in the database) defined based on a preceding context, content, and action (patterns are comprised of actions, contexts, and content) and following operations, as in Fig. 4 – “Recommend to the user as a routine in a notification” – generates routines that are comprised of following context, content, and action).
Claim 18 incorporates substantively all the limitations of claim 1 and 2 in an electronic device and is rejected on similar grounds as above.
Regarding claim 4, the combination of Semar and Lee teaches the control method of claim 1, wherein the first context, the first content, and the first action of the first operation are matched to a preceding context, a preceding content, and a preceding action of the preceding operation (Semar, Fig. 4 – “Create a pattern using the changed setting as the Action and the current context of the device (time, location) as the Triggers. If pattern does not already exist in the database:” – teaches searching a pattern database to match the first context, content, and action of the first operation (create a pattern) to a preceding context, content, and action of the preceding operation (if pattern does not already exist in database, meaning searches database for matching preceding operation)).
Claims 16 and 19 are similar to claim 4, hence similarly rejected.
Regarding claim 5, the combination of Semar and Lee teaches the control method of claim 1, wherein the setting of the trigger condition and the intent action further comprises:
setting the trigger condition based on a following context of the following operation (Semar, Figure 7 Screenshot of AutoMate Routine Configuration Screen (Name of routine: “Silent at School”; Triggers enabled: “Location: School”; Actions: “Ringer: Silent and Vibrate”)” – teaches setting the trigger condition based on a following context of the following operation (the following context being the location of the recommended routine as shown in Fig. 7)); and
setting the intent action based on the following action of the following operation (Semar, Figure 7 Screenshot of AutoMate Routine Configuration Screen (Name of routine: “Silent at School”; Triggers enabled: “Location: School”; Actions: “Ringer: Silent and Vibrate”)” – teaches setting the intent action (action of the routine set ringer to silent and vibrate) based on the following action of the following operation).
Claims 17 and 20 are similar to claim 5, hence similarly rejected.
Claim(s) 3, 6-12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Semar in view of Lee, and further in view of Harit et al. (FOR from IDS: KR20190139773, published Dec. 2019, hereinafter “Harit”).
Regarding claim 3, the combination of Semar and Lee teaches the control method of claim 2.
The combination of Semar and Lee fails to explicitly teach wherein the preceding content and the following content are the same.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches:
wherein the preceding content and the following content are the same (Harit, [0078] – “Herein, the smartphone may be the context expanding device 10 and the smart watch may be the context information generating device 20. The smart watch may notify the user when a text message is received, generate contextual information indicating that the notification for the text message is floating, and send it to the smartphone. The smartphone may receive context information from the smart watch, and determine a context expansion action based on the context information and the context expansion trigger event when the context expansion trigger event occurs.” – teaches wherein the preceding content (the received text message) and the following content (text message and context information sent to smart watch) are the same).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the preceding and following content being the same of Harit to the control method of Semar and Lee in order to detect a usage pattern wherein the preceding content and following content are the same. Doing so would resume context by providing the necessary details for other applications or devices in use, from previous tasks / applications / devices / operations, in order to maintain a seamless experience during switching (Harit, [0144]).
Regarding claim 6, the combination of Semar and Lee teaches the control method of claim 1.
The combination of Semar and Lee fails to explicitly teach wherein the preceding operation is an operation of generating a target content in a preceding context, and the following operation is an operation of opening the target content in a following context.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches:
wherein the preceding operation is an operation of generating a target content in a preceding context, and the following operation is an operation of opening the target content in a following context (Harit, [0078] – “Herein, the smartphone may be the context expanding device 10 and the smart watch may be the context information generating device 20. The smart watch may notify the user when a text message is received, generate contextual information indicating that the notification for the text message is floating, and send it to the smartphone. The smartphone may receive context information from the smart watch, and determine a context expansion action based on the context information and the context expansion trigger event when the context expansion trigger event occurs.” – teaches wherein the preceding operation is an operation of generating a target content (text message is received, smart watch generates context information) in a preceding context (the smart watch), and the following operation is an operation of opening the target content (text message is received) in a following context (sending the text message to the smartphone)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the preceding operation being an operation of generating a target content in a preceding context and a following operation being an operation of opening the target content in a following context of Harit to the control method of Semar and Lee in order to generate and open target content in preceding and following contexts. Doing so would resume context by providing the necessary details for other applications or devices in use, from previous tasks / applications / devices / operations, in order to maintain a seamless experience during switching (Harit, [0144])
Regarding claim 7, the combination of Semar, Lee, and Harit teaches the control method of claim 6,
wherein the preceding context and the following context correspond to different times (Semar, Section IV Sub-section F Paragraph 3 – “Auto-Mate checks the user’s location at a minimum of every 200 metres travelled or at a minimum of every 5 minutes as opposed to constantly monitoring the user’s location.” – teaches wherein the preceding context (prior to travel or 5 minutes passing) and the following context (after 200 metres travelled or after 5 minutes) correspond to different times)
The combination of Semar and Lee fails to explicitly teach wherein the preceding context and the following context correspond to a same location.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches:
wherein the preceding context and the following context correspond to different times and a same location (Harit, [0084] – “ In this case, if the user runs the taxi app after picking up the smartphone, the user recognizes this as a context expansion trigger event for entering "ABC cafe" at the destination, and automatically enters "ABC cafe" at the destination of the taxi app. have. The smartphone may enter “ABC Cafe” as a destination when the user runs a taxi app within a predetermined time, for example, within 10 minutes after the text message is displayed on the screen of the smart watch.” – teaches wherein the preceding context (ABC café context from text message) and the following context (ABC café context in taxi app) corresponds to different times (predetermined time, for example, within 10 minutes) and a same location).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the preceding and following contexts corresponding to different times and the same location of Harit to further modify the control method of Semar, Lee, and Harit in order to have the preceding and following context of a usage pattern correspond to a different time and same location. Doing so would reduce the way of interaction for the end user by intelligently bypassing some actions such as finding an application on the device, running the application, and filling in the required information, and reduce the cognitive load and processing time of the user (Harit, [0144]).
Regarding claim 8, the combination of Semar, Lee, and Harit teaches the control method of claim 6.
wherein the preceding context and the following context correspond to different times and different locations (Semar, Section IV Sub-section F Paragraph 3 – “Auto-Mate checks the user’s location at a minimum of every 200 metres travelled or at a minimum of every 5 minutes as opposed to constantly monitoring the user’s location.” – teaches wherein the preceding context (prior to travel or 5 minutes passing) and the following context (after 200 metres travelled or after 5 minutes) correspond to different times and different locations).
Regarding claim 9, the combination of Semar and Lee teaches the control method of claim 1,
The combination of Semar and Lee fails to explicitly teach wherein the detecting of the first usage pattern further comprises: searching the usage pattern DB with a first operation record set corresponding to the first operation, wherein the first operation record set comprises a type of the first content, an entity of the first content, a time type of the first context, a location type of the first context, and/or a type of the first action.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches wherein the detecting of the first usage pattern further comprises:
searching the usage pattern DB with a first operation record set corresponding to the first operation, wherein the first operation record set comprises a type of the first content, an entity of the first content, a time type of the first context, a location type of the first context, and/or a type of the first action (Harit, [0064] – “The database may be included in the context expansion device 10 or may be included in a separate database device. The database may store information about context information, context extension trigger events, usage history information, context expansion actions, and action lists. The database may store a relationship between at least one of the context information, the usage history information, and the context expansion trigger event and the context expansion action.” – teaches searching the usage pattern database with a first operation record set (context information, usage history information) corresponding to the first operation, wherein the operation record set comprises a type of the first content (context expansion action), an entity of the first content (relationship between context information and context expansion action), a location and time type of the first context (context information), and/or a type of the first action (action lists).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the database comprising record sets of Harit to the control method of Semar and Lee in order to search the database using operation record sets comprising types and entities of context, content, action, and operation. Doing so would enable capturing data that can be used to accurately determine the context continuity intent (Harit, [0118]).
Regarding claim 10, the combination of Semar and Lee teaches the control method of claim 1.
The combination of Semar and Lee fails to explicitly teach further comprising: accumulating a plurality of prior operation record sets associated with a plurality of prior operations; and adding the first usage pattern to the usage pattern DB based on repetition of candidate record sets corresponding to the preceding operation and the following operation of the first usage pattern in the plurality of prior operation record sets.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches further comprising:
accumulating a plurality of prior operation record sets associated with a plurality of prior operations (Harit, [0110] – “In one embodiment, the device 100 is configured to assimilate the obtained context metadata with predetermined data stored in the database 108. In one embodiment, assimilation comprises comparing the characteristics previously acquired by sequential time-stepping processing with the characteristics currently obtained. An assimilation of contextual metadata may include data aggregation. For example, the aggregated data may include time, place, event, action, suggestion, task, emotion, and the like.” And in [0111] – “In one embodiment, the content data, context metadata, and device metadata include at least one of context data, media data, and advertisement data, wherein the usage data includes a usage duration of the application, a usage pattern of the application.” – teaches accumulating a plurality of prior operation record sets associated with a plurality of prior operations (device 100 stored assimilated data in database 108, including context metadata, wherein the context metadata includes at least one of context data, media data, and usage patterns associated with prior operations)); and
adding the first usage pattern to the usage pattern DB based on repetition of candidate record sets corresponding to the preceding operation and the following operation of the first usage pattern in the plurality of prior operation record sets (Harit, [0065] – “In one embodiment, the context extension device 10 may determine the context extension action based on matching information between the context information stored in the database, the context extension trigger event, and the context extension action. In one embodiment, the context extension device 10 may determine the context extension action based on matching information between context information stored in the database, context extension trigger event, usage history information, and context extension action. The context expanding apparatus 10 may determine the context expanding action based on data related to a specific user or a specific group among data stored in the database.” – teaches repetition of candidate record sets (context extensions) corresponding to the preceding operation (usage history, stored context information) and following operation (trigger events, context extension action itself) of the first usage pattern in the plurality of prior operation record sets, and in [0066] – “In one embodiment, the context extension device 10 may update the database based on the context information received from the context information generation device 20, the context extension trigger event that occurred, and the action actually performed. In one embodiment, the context extension device 10 may update the database based on the context information received from the context information generation device 20, the context expansion trigger event that occurred, the usage history information, and the action actually performed. . The context expanding apparatus 10 may update the matching information between the context information stored in the database and the context expanding action. The context extension device 10 may update the matching information between the context information stored in the database, the context expansion trigger event, and the context expansion action. The context extension device 10 may update matching information between context information, context extension trigger events, usage history information, and context extension actions stored in a database.” – teaches storing the determined usage pattern in the database based on the repetition of candidate sets (updates the database based on received context information, performed action, and matching information)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the accumulation of prior operation record sets and adding usage patterns to the database of Harit to the control method of Semar and Lee in order to create a plurality of prior operation records and determine usage patterns based on preceding and following operations of the usage patterns, to then store the determined usage pattern in the usage pattern database. Doing so would enable predicting actions dynamically based on current context and physical user interactions, intelligently predict the next task of the most likely user, and analyze contextual information, usage history, and physical interactions (Harit, [0143]).
Regarding claim 11, the combination of Semar, Lee, and Harit teach the control method of claim 10.
The combination of Semar and Lee fails to explicitly teach wherein the accumulating of the plurality of prior operation record sets further comprises: collecting a first context record and a first content record associated with a first prior operation; and storing a first prior operation record set by combining the first context record and the first content record.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches wherein the accumulating of the plurality of prior operation record sets further comprises:
collecting a first context record and a first content record associated with a first prior operation (Harit, [0111] – “In an embodiment, the device 100 is configured to determine content data and context extension data associated with the first user device based on the received input.” – teaches collecting a first context record (context extension data) and first content record (content data) associated with a first prior operation (first user device received input)); and
storing a first prior operation record set by combining the first context record and the first content record (Harit, [0077] – “Referring to FIG. 7A, when a user wears a smart watch and receives a text message related to a movement such as "now come to ABC cafe for James's birthday party" with the smart watch, the user was in an idle state.” – teaches storing a first prior operation record set (user device received input) by combining first context record and first content record (message includes content, James’ birthday, and context, ABC café))).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the collecting and storing of a first prior operation of Harit to further modify the control method of Semar, Lee, and Harit in order to collect a prior operation comprising combined context and content records. Doing so would enable capturing data that can be used to accurately determine the context continuity intent (Harit, [0118]).
Regarding claim 12, the combination of Semar, Lee, and Harit teach the control method of claim 11.
The combination of Semar and Lee fails to explicitly teach wherein the first context record and the first content record each comprise basic data and processed data, wherein the processed data is generated by processing the basic data.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches:
wherein the first context record and the first content record each comprise basic data and processed data, wherein the processed data is generated by processing the basic data (Harit, [0110] – “In one embodiment, the device 100 is configured to assimilate the obtained context metadata with predetermined data stored in the database 108. In one embodiment, assimilation comprises comparing the characteristics previously acquired by sequential time-stepping processing with the characteristics currently obtained. An assimilation of contextual metadata may include data aggregation.” – teaches wherein the first context and content record (data of the database) comprises basic and processed data (predetermined data, assimilated data), wherein the processed data is generated by processing the basic data (assimilation may comprise comparing characteristics with predetermined data, or aggregation, which amounts to processing the data and including it in the database), and in [0111] – “In an embodiment, the device 100 is configured to determine content data and context extension data associated with the first user device based on the received input.” – teaches the content records comprising basic and processed data (content data being fed to same device 100 that performs data processing and storing in database).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the basic and processed data of Harit to further modify the control method of Semar, Lee, and Harit in order to have the content and context records comprise basic and processed data. Doing so would enable capturing data that can be used to accurately determine the context continuity intent (Harit, [0118]).
Regarding claim 15, the combination of Semar and Lee teaches the control device of claim 14,
wherein the preceding operation is defined based on a preceding context, a preceding content, and a preceding action, and the following operation is defined based on a following context, a following content, and a following action (Semar, Fig. 4 – “Create a pattern using the changed setting as the Action and the current context of the device (time, location) as the Triggers. If pattern does not already exist in the database: Add it as a new entry Else: If the previous occurrence of the pattern is on the same day:” – teaches preceding operations (operations stored in patterns in the database) defined based on a preceding context, content, and action (patterns are comprised of actions, contexts, and content) and following operations, as in Fig. 4 – “Recommend to the user as a routine in a notification” – generates routines that are comprised of following context, content, and action).
The combination of Semar and Lee fails to explicitly teach wherein the preceding content and the following content are the same.
However, analogous to the field of automating mobile device operations and scheduling, Harit teaches:
wherein the preceding content and the following content are the same ((Harit, [0078] – “Herein, the smartphone may be the context expanding device 10 and the smart watch may be the context information generating device 20. The smart watch may notify the user when a text message is received, generate contextual information indicating that the notification for the text message is floating, and send it to the smartphone. The smartphone may receive context information from the smart watch, and determine a context expansion action based on the context information and the context expansion trigger event when the context expansion trigger event occurs.” – teaches wherein the preceding content (the received text message) and the following content (text message and context information sent to smart watch) are the same).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the preceding and following content being the same of Harit to the preceding and following operations of Semar and Lee in order to detect a usage pattern wherein the preceding content and following content are the same. Doing so would resume context by providing the necessary details for other applications or devices in use, from previous tasks / applications / devices / operations, in order to maintain a seamless experience during switching (Harit, [0144]).
Response to Arguments
Applicant's arguments filed 13 February 2026 have been fully considered but they are not persuasive.
Applicant argues on pp. 6-8 of Remarks that Semar, Lee, and Harit fail to teach the amended limitations of claim 1 regarding building a UI widget in response to the detection of usage patterns. Examiner respectfully disagrees and points to Semar at Section IV Subsection D – “The user interface for AutoMate was designed to be accessible and easy to use. To help attain this goal, AutoMate uses default Android design elements and follows Google’s Android design guidelines… The map used to define locations uses the Google Maps API which is a feature that Android users are likely to be familiar with. The icons to perform actions such as deleting or editing routines are default icons provided by Google which promotes familiarity.”, Section IV Subsection H Paragraph 1 – “In addition to autonomous automation, AutoMate also allows users to manually edit and further configure routines. Once a routine is recommended through a notification, the user is taken to the routine configuration screen as seen in Figure 7, where they can add or remove as many actions and triggers as they would like to configure the routine to their specific preferences.”, and Fig. 7 – teaches building a UI widget in response to the detection of usage patterns. Semar’s user interface as shown in Fig. 7 displays a routine, or usage pattern, comprising the preceding operation (user opening or configuring settings at certain location such as school, as in Fig. 2 “setting up location” and Fig. 7), the following operation (user setting ringer to silent at certain location such as school, as in Fig. 7 “silent at school”), the trigger condition (the user being at school, as in Fig. 7 – “Enabled Triggers” “Location: School”), and the intent action (turning user mobile device ringer to silent while at school. as in Fig. 7 “Actions” “Ringer: Silent and Vibrate”), with interactive elements that allow the user to selectively enable or disable the detected routines, or usage patterns.
Applicant argues on pp. 6 of Remarks that Lee fails to teach the user created content of claim 1. Examiner respectfully disagrees and points to the specification at Pg. 17 Lines 16-20 – “An operation record may include at least one of a context record and a content record. The operation record may include data received from a sensor (e.g., the sensor module 176) and/or a program (e.g., the program 140), and data processed from the received data. The operation record may include log information.” and in Pg. 25 Lines 15-19 – “Referring to FIG. 4, in operation 410, a content action may be monitored. The content action may include a content operation to be performed on a content, for example, generating, opening, editing, and deleting the content. A change may occur in the content based on the content action. Based on such a change in the content, the content action may be detected. In operation 420, the content corresponding to the detected content action may be parsed”. The broadest reasonable interpretation, in light of the specification, of the “user created content” and “parsing the user created content” amounts to data received from a sensor, which may include log information, where the user created content, or content action that is parsed, may be generating, opening, editing, and deleting the content. Lee teaches at Section 4 Paragraph 3 – “The first module receives sensing data from four sensors and a phone system and generates a log with twelve properties of Simple Context”, Section 4, second set of bullet points, Bullet Point 2 – “AppID: the full name of App which is running at ‘time’” and Fig. 7 – teaches user created content (log of twelve properties includes AppID, which is the application, or content, the user opened and is running on user smartphone at certain time. Fig. 7 shows user created content such as applications opened during certain contexts. Lee further teaches in Section 3 Paragraph 3 – “Two modules work for training. A Simple Context generation & reduction module gathers training log data from a smartphone for a certain period of time and sends meaningful logs to a server. A user context inference module generates a context inference model including behavior patterns for a user. The model will be used by an App prediction module in the real-time phase.” and in Section 4 Paragraph 5 – “The second module uses a reduction method introduced in our previous research in order to reduce the cost and the data size for transmission between a smartphone and a server.” – teaches parsing the user created content (gathers log data from user smartphone for a certain period of time and sends meaningful logs to a server, logs are parsed for reducing cost and data size to send only meaningful logs to the server).
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the parsing of user created content and setting a trigger condition and an intent action based at least in part on parsing the user created content of Lee to the operations, usage patterns, and control method of Semar. Doing so would infer users’ meaningful context from sensing data and predict an appropriate application based on the run-time context to automatically execute it on a user’s smartphone (Lee, Introduction). Semar and Lee are both directed to the field of autonomous smartphone management based on behavior pattern recognition, and as in Semar at Section V Subsection B Paragraph 2 – “Improving the above areas within AutoMate would result in an application that can used in many more ways than simply automating setting changes, for example automatically playing the user’s favourite genre of music when headphones are plugged in during the night.” – Semar states improvements to AutoMate would result in an application that does more than simply automating setting changes. Lee addresses this at Section 3 Paragraph 1 – “Thus, a method to autonomously manage various Apps will be more useful than previous services like contents recommendation or phone settings changing services. In this paper, we propose a prediction and auto-execution system for smartphone applications to overcome the passive usage.” A person of ordinary skill in the art seeking to improve upon the teachings of Semar would have been led to Lee’s auto-execution system for smartphone applications (See MPEP 2183(I)(G)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al. (WO 2020159213, published Aug. 2020) teaches an electronic device that provides a customized function suitable for a user based on the user’s electronic device usage patterns. Teaches wherein usage patterns are based on context occurring within a certain time, and providing a recommendation or configuration associated with the context.
Park et al. (US Pub. No. 2020/0244750, published July 2020) teaches systems and operating methods to determine usage patterns of user devices based on first and second information received from two external devices used by the user. Teaches generating user context information based on usage logs and service information occurring within a certain context and time.
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/LOUIS CHRISTOPHER NYE/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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