DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 18/945,654, filed on November 13, 2024.
Oath/Declaration
Oath/Declaration as filed on November 13, 2024 is noted by the Examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
In particular, the limitations “appearance time information” in third line of the claim renders the claim indefinite, because the meaning of the coined terms “appearance time information” recited in the third line of the claim is not apparent in light of the specification. See MPEP § 2173.05(a). Examiner recommends applicant amend the claim, without adding new matter, to positively recite in definite terms more clearly what “appearance time information” actually is. In addition, any claim(s) dependent on claim 1 are objected to based on same above reasoning.
The claim recites limitation term “at least one character” in fifth line of the claim, but it not exactly clear whether the limitation term is referring to a same at least one character recited in third line of the claim, or to a different at least one character. Therefore, Examiner suggests the limitation term should be amended, without adding new matter, in a manner that clarifies exactly what the limitation term is referring to. In addition, any claim(s) dependent on claim 1 are objected to based on same above reasoning.
The claim recites limitation terms “the at least one character” in sixth and ninth lines of the claim, but it not exactly clear whether the limitation terms are referring to a same at least one character recited in third line of the claim, or to same at least one character recited in fifth line of the claim. Therefore, Examiner suggests the limitation terms should be amended, without adding new matter, in a manner that clarifies exactly what the limitation terms are referring to. In addition, any claim(s) dependent on claim 1 are objected to based on same above reasoning.
The claim recites limitation term “a context” in seventh line of the claim, but it not exactly clear whether the limitation term is referring to a same context recited in first line of the claim, to same context recited in fifth line of the claim, or to a different context. Therefore, Examiner suggests the limitation term should be amended, without adding new matter, in a manner that clarifies exactly what the limitation term is referring to. In addition, any claim(s) dependent on claim 1 are objected to based on same above reasoning.
Claim 10 is objected to because of the following informalities:
In particular, the limitations “appearance time information” in sixth line of the claim renders the claim indefinite, because the meaning of the coined terms “appearance time information” recited in the sixth line of the claim is not apparent in light of the specification. See MPEP § 2173.05(a). Examiner recommends applicant amend the claim, without adding new matter, to positively recite in definite terms more clearly what “appearance time information” actually is. In addition, any claim(s) dependent on claim 10 are objected to based on same above reasoning.
The claim recites limitation term “at least one character” in seventh line of the claim, but it not exactly clear whether the limitation term is referring to a same at least one character recited in sixth line of the claim, or to a different at least one character. Therefore, Examiner suggests the limitation term should be amended, without adding new matter, in a manner that clarifies exactly what the limitation term is referring to. In addition, any claim(s) dependent on claim 10 are objected to based on same above reasoning.
The claim recites limitation terms “the at least one character” in eighth and eleventh lines of the claim, but it not exactly clear whether the limitation terms are referring to a same at least one character recited in sixth line of the claim, or to same at least one character recited in seventh line of the claim. Therefore, Examiner suggests the limitation terms should be amended, without adding new matter, in a manner that clarifies exactly what the limitation terms are referring to. In addition, any claim(s) dependent on claim 10 are objected to based on same above reasoning.
The claim recites limitation term “a context” in ninth line of the claim, but it not exactly clear whether the limitation term is referring to a same context recited in first line of the claim, to same context recited in seventh line of the claim, or to a different context. Therefore, Examiner suggests the limitation term should be amended, without adding new matter, in a manner that clarifies exactly what the limitation term is referring to. In addition, any claim(s) dependent on claim 10 are objected to based on same above reasoning.
Claim 19 is objected to because of the following informalities:
In particular, the limitations “appearance time information” in fifth line of the claim renders the claim indefinite, because the meaning of the coined terms “appearance time information” recited in the fifth line of the claim is not apparent in light of the specification. See MPEP § 2173.05(a). Examiner recommends applicant amend the claim, without adding new matter, to positively recite in definite terms more clearly what “appearance time information” actually is.
The claim recites limitation term “at least one character” in sixth line of the claim, but it not exactly clear whether the limitation term is referring to a same at least one character recited in fifth line of the claim, or to a different at least one character. Therefore, Examiner suggests the limitation term should be amended, without adding new matter, in a manner that clarifies exactly what the limitation term is referring to.
The claim recites limitation terms “the at least one character” in seventh and tenth lines of the claim, but it not exactly clear whether the limitation terms are referring to a same at least one character recited in fifth line of the claim, or to same at least one character recited in sixth line of the claim. Therefore, Examiner suggests the limitation terms should be amended, without adding new matter, in a manner that clarifies exactly what the limitation terms are referring to.
The claim recites limitation term “a context” in eighth line of the claim, but it not exactly clear whether the limitation term is referring to a same context recited in sixth line of the claim, or to a different context. Therefore, Examiner suggests the limitation term should be amended, without adding new matter, in a manner that clarifies exactly what the limitation term is referring to.
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, 10, and 19 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Madden et al., U.S. Patent Application Publication 2021/0234723 A1 (hereinafter Madden).
Regarding claim 1, Madden teaches a method for obtaining context associated with a character in a video performed by a device, the method comprising: (200 FIGS. 1-5, paragraph[0059] of Madden teaches in some implementations, one or more subsystems may pre-process activity data before it is received by the activity data collector 108; for example, a home security system including one or more home surveillance devices 106b (e.g., cameras) may pre-process video/image activity data to identify humans or objects in the video/images prior to providing the activity data 116 to the home monitoring system 104; in some implementations, a user device may provide supplemental information with the activity data 116 that assists in identifying a type of activity related to the activity data 116; and for example, a “smart” watch or activity-tracking personal device or application may provide supplemental information identifying a type of activity in which a user is engaged (e.g., a workout or exercise), and See also at least ABSTRACT, paragraphs[0040]-[0058], and [0060]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans or objects in a video prior to providing the activity data to a home monitoring system, and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data)) obtaining appearance time information for at least one character appearing in a specific video; obtaining a context related to at least one character from a video portion corresponding to a time at which the at least one character appeared based on the appearance time information (FIGS. 1-5, paragraphs[0087]-[0088] of Madden teaches in some implementations, the pattern recognition module 110 can detect relationships between human users and objects from the collected activity data 116; for example, a particular user may be detected in the activity data 116 to be carrying a particular mobile phone by tracking both the particular user and the particular mobile phone through the home network 102 (e.g., entering/exiting the house together, walking around the house together); in another example, a particular user may be detected in the activity data 116 to be carrying a backpack by tracking both the particular user (e.g., using a site-specific human model for the particular user) and the backpack (e.g., using a site-specific object model for the backpack) using image data collected from the home surveillance devices 106b in the home network 102; in some implementations, the pattern recognition module 110 can detect routines for human users from the collected activity data 116; a routine may be detected by the pattern recognition model and determined to be significant such that one or more rules 118 are generated for the routine (e.g., a “Jane's morning routine”); a routine can include a geo-path of the user through the area within the home network 102, a schedule including a start and stop time, a set of actions performed by the user, and a repetition frequency; a routine can include multiple sub-routines, where the sub-routines can be combined together into a routine; for example, a morning routine for a user may include sub-routines i) wake up and use bathroom, ii) walk to kitchen and make coffee, and iii) leave the house; and each one of the sub-routines can have an associate time frame and duration in which the sub-routine is completed (e.g., “wake up and use bathroom” is from 7:15-7:45 AM), and See also at least ABSTRACT, paragraphs[0040]-[0086], and [0089]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans users or objects in a video prior to providing the activity data to a home monitoring system and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data, wherein a pattern recognition module of the home monitoring system detects relationships between the human users and the objects and even detects routines for the human users such as a schedule including a start time and stop time));
generating at least one graph representing a context of the at least one character based on the context related to the at least one character; and classifying the at least one character using the at least one graph (FIGS. 1-5, paragraph[0051] of Madden teaches a graph-based knowledge base is generated for the home network 102, including two or more nodes, where each node is an object, human, or routine in an area monitored by the home network 102 and each link is a relationship between two or more nodes (206); for example, two nodes can be a person “Jane” and a smart phone associated with Jane (e.g., “Jane's phone”), where the link between the node “Jane” and the node “Jane's phone” defines a relationship (e.g., Jane owns Jane's phone) between them; and further details about generating a graph-based knowledge base is discussed below with reference to FIGS. 3A-C, and See also at least ABSTRACT, paragraphs[0040]-[0050], and [0052]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans users or objects in a video prior to providing the activity data to a home monitoring system and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data, wherein a pattern recognition module of the home monitoring system detects relationships between the human users and the objects and even detects routines for the human users such as a schedule including a start time and stop time, and wherein the one or more subsystems generate a graph-based knowledge base that includes two or more nodes, which have a relationship link, that are each an object, human or routine in area monitored by the home network)).
Regarding claim 10, Madden teaches a device that obtains context related to a character in a video, the device comprising: at least one memory; and at least one processor, wherein the at least one processor is configured to (104 FIGS. 1-5, paragraph[0059] of Madden teaches in some implementations, one or more subsystems may pre-process activity data before it is received by the activity data collector 108; for example, a home security system including one or more home surveillance devices 106b (e.g., cameras) may pre-process video/image activity data to identify humans or objects in the video/images prior to providing the activity data 116 to the home monitoring system 104; in some implementations, a user device may provide supplemental information with the activity data 116 that assists in identifying a type of activity related to the activity data 116; and for example, a “smart” watch or activity-tracking personal device or application may provide supplemental information identifying a type of activity in which a user is engaged (e.g., a workout or exercise), and See also at least ABSTRACT, paragraphs[0024], [0040]-[0058], [0060]-[0094], and [0197] of Madden (i.e., Madden teaches at least computer-readable storage medium encoded with executable instructions as well as one or more subsystems that pre-processes video activity data to identify humans or objects in a video prior to providing the activity data to a home monitoring system, and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data)): obtain appearance time information for at least one character appearing in a specific video; obtain a context related to at least one character from a video portion corresponding to a time at which the at least one character appeared based on the appearance time information (FIGS. 1-5, paragraphs[0087]-[0088] of Madden teaches in some implementations, the pattern recognition module 110 can detect relationships between human users and objects from the collected activity data 116; for example, a particular user may be detected in the activity data 116 to be carrying a particular mobile phone by tracking both the particular user and the particular mobile phone through the home network 102 (e.g., entering/exiting the house together, walking around the house together); in another example, a particular user may be detected in the activity data 116 to be carrying a backpack by tracking both the particular user (e.g., using a site-specific human model for the particular user) and the backpack (e.g., using a site-specific object model for the backpack) using image data collected from the home surveillance devices 106b in the home network 102; in some implementations, the pattern recognition module 110 can detect routines for human users from the collected activity data 116; a routine may be detected by the pattern recognition model and determined to be significant such that one or more rules 118 are generated for the routine (e.g., a “Jane's morning routine”); a routine can include a geo-path of the user through the area within the home network 102, a schedule including a start and stop time, a set of actions performed by the user, and a repetition frequency; a routine can include multiple sub-routines, where the sub-routines can be combined together into a routine; for example, a morning routine for a user may include sub-routines i) wake up and use bathroom, ii) walk to kitchen and make coffee, and iii) leave the house; and each one of the sub-routines can have an associate time frame and duration in which the sub-routine is completed (e.g., “wake up and use bathroom” is from 7:15-7:45 AM), and See also at least ABSTRACT, paragraphs[0040]-[0086], and [0089]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans users or objects in a video prior to providing the activity data to a home monitoring system and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data, wherein a pattern recognition module of the home monitoring system detects relationships between the human users and the objects and even detects routines for the human users such as a schedule including a start time and stop time)); generate at least one graph representing a context of the at least one character based on the context related to the at least one character; and classify the at least one character using the at least one graph (FIGS. 1-5, paragraph[0051] of Madden teaches a graph-based knowledge base is generated for the home network 102, including two or more nodes, where each node is an object, human, or routine in an area monitored by the home network 102 and each link is a relationship between two or more nodes (206); for example, two nodes can be a person “Jane” and a smart phone associated with Jane (e.g., “Jane's phone”), where the link between the node “Jane” and the node “Jane's phone” defines a relationship (e.g., Jane owns Jane's phone) between them; and further details about generating a graph-based knowledge base is discussed below with reference to FIGS. 3A-C, and See also at least ABSTRACT, paragraphs[0040]-[0050], and [0052]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans users or objects in a video prior to providing the activity data to a home monitoring system and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data, wherein a pattern recognition module of the home monitoring system detects relationships between the human users and the objects and even detects routines for the human users such as a schedule including a start time and stop time, and wherein the one or more subsystems generate a graph-based knowledge base that includes two or more nodes, which have a relationship link, that are each an object, human or routine in area monitored by the home network)).
Regarding claim 19, Madden teaches at least one non-transitory computer readable medium storing at least one instruction, based on the at least one instruction being executed by at least one processor, a device controls to (FIGS. 1-5, paragraph[0059] of Madden teaches in some implementations, one or more subsystems may pre-process activity data before it is received by the activity data collector 108; for example, a home security system including one or more home surveillance devices 106b (e.g., cameras) may pre-process video/image activity data to identify humans or objects in the video/images prior to providing the activity data 116 to the home monitoring system 104; in some implementations, a user device may provide supplemental information with the activity data 116 that assists in identifying a type of activity related to the activity data 116; and for example, a “smart” watch or activity-tracking personal device or application may provide supplemental information identifying a type of activity in which a user is engaged (e.g., a workout or exercise), and See also at least ABSTRACT, paragraphs[0024], [0040]-[0058], [0060]-[0094], and [0197] of Madden (i.e., Madden teaches at least computer-readable storage medium encoded with executable instructions as well as one or more subsystems that pre-processes video activity data to identify humans or objects in a video prior to providing the activity data to a home monitoring system, and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data)): obtain appearance time information for at least one character appearing in a specific video; obtain a context related to at least one character from a video portion corresponding to a time at which the at least one character appeared based on the appearance time information (FIGS. 1-5, paragraphs[0087]-[0088] of Madden teaches in some implementations, the pattern recognition module 110 can detect relationships between human users and objects from the collected activity data 116; for example, a particular user may be detected in the activity data 116 to be carrying a particular mobile phone by tracking both the particular user and the particular mobile phone through the home network 102 (e.g., entering/exiting the house together, walking around the house together); in another example, a particular user may be detected in the activity data 116 to be carrying a backpack by tracking both the particular user (e.g., using a site-specific human model for the particular user) and the backpack (e.g., using a site-specific object model for the backpack) using image data collected from the home surveillance devices 106b in the home network 102; in some implementations, the pattern recognition module 110 can detect routines for human users from the collected activity data 116; a routine may be detected by the pattern recognition model and determined to be significant such that one or more rules 118 are generated for the routine (e.g., a “Jane's morning routine”); a routine can include a geo-path of the user through the area within the home network 102, a schedule including a start and stop time, a set of actions performed by the user, and a repetition frequency; a routine can include multiple sub-routines, where the sub-routines can be combined together into a routine; for example, a morning routine for a user may include sub-routines i) wake up and use bathroom, ii) walk to kitchen and make coffee, and iii) leave the house; and each one of the sub-routines can have an associate time frame and duration in which the sub-routine is completed (e.g., “wake up and use bathroom” is from 7:15-7:45 AM), and See also at least ABSTRACT, paragraphs[0024], [0040]-[0086], and [0089]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans users or objects in a video prior to providing the activity data to a home monitoring system and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data, wherein a pattern recognition module of the home monitoring system detects relationships between the human users and the objects and even detects routines for the human users such as a schedule including a start time and stop time)); generate at least one graph representing a context of the at least one character based on the context related to the at least one character; and classify the at least one character using the at least one graph (FIGS. 1-5, paragraph[0051] of Madden teaches a graph-based knowledge base is generated for the home network 102, including two or more nodes, where each node is an object, human, or routine in an area monitored by the home network 102 and each link is a relationship between two or more nodes (206); for example, two nodes can be a person “Jane” and a smart phone associated with Jane (e.g., “Jane's phone”), where the link between the node “Jane” and the node “Jane's phone” defines a relationship (e.g., Jane owns Jane's phone) between them; and further details about generating a graph-based knowledge base is discussed below with reference to FIGS. 3A-C, and See also at least ABSTRACT, paragraphs[0024], [0040]-[0050], and [0052]-[0094] of Madden (i.e., Madden teaches one or more subsystems that pre-processes video activity data to identify humans users or objects in a video prior to providing the activity data to a home monitoring system and that even provides supplemental information with the activity data that assists in identifying a type of activity related to the activity data, wherein a pattern recognition module of the home monitoring system detects relationships between the human users and the objects and even detects routines for the human users such as a schedule including a start time and stop time, and wherein the one or more subsystems generate a graph-based knowledge base that includes two or more nodes, which have a relationship link, that are each an object, human or routine in area monitored by the home network)).
Potentially Allowable Subject Matter
Claims 2-9, and 11-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome applicable objection(s), indicated above, and if rewritten in independent form including all of the limitations of the base claim and any intervening claims, because for each of claims 2-9, and 11-18 the prior art references of record do not teach the combination of all element limitations as presently claimed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDUL-SAMAD A ADEDIRAN whose telephone number is (571)272-3128. The examiner can normally be reached on Monday through Thursday, 8:00 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amr Awad can be reached on 571-272-7764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDUL-SAMAD A ADEDIRAN/Primary Examiner, Art Unit 2621
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