The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This is in response to amended claims filed on 12/23/24 in which Claims 1-30 are presented for examination of which Claims 1, 15, 20 and 21 are in independent form.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Please include “remediation conversation ” into the title.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-30 rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. US 12183173 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because broader claims in a later application constitute obvious double patenting of narrow claims in an issued patent. See In re Van Ornum and Stang, 214, USPQ 761, 766, and 767 (CCPA) (the court sustained an obvious double patenting rejection of generic claims in a continuation application over narrower species claims in an issued patent); In re Vogel, 164 USPQ 619, 622, and 623 (CCPA 1970) (generic application claim specifying "meat" is obvious double patenting of narrow patent claim specifying "pork"). Alternatively, it would have been obvious to one having ordinary skill in the art at the time the invention was made to omit the additional elements, since it has been held that omission of an element and its function in a combination where the remaining elements perform the same functions as before involves only routine skill in the art. In re Karlson, 136 USPQ 184.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 15, 20 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process and organizing human activity without adding significantly more. The independent Claims recite:
detecting a security event in an environment based on a first portion of collected sensor data (mental process: notice someone entering a restricted area);
generating contextual information for the security event based on a second portion of the collected sensor data, wherein the second portion is different from the first portion (mental process: notice bad weather and a dog chasing the individual);
retrieving a user profile of a first user accessing security information for the environment on an output device, wherein the user profile is indicative of access rights and user interface preferences (human activity: check phone for tornado warning in effect);
generating a user-specific remediation conversation from a plurality of different remediation conversations based on the user profile, the security event, and a user-specific subset of the contextual information, wherein each of the plurality of different remediation conversations comprises dialogue that is tailored for providing the security information and guiding a given user to resolve the security event (mental process: decide on whether to ask individual if they are in danger and determine possible ways to help); and
outputting at least a first portion of the user-specific remediation conversation (mental process: offer the individual a refuge from the weather and dog).
This judicial exception is not integrated into a practical application because there is no particular machine, particular transformation and no meaningful limitations that would amount to significantly more. The claims do not include additional elements.
MPEP 2106.05(e) states that the claim should add meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment to transform the judicial exception into patent-eligible subject matter. MPEP 2106.05(h) states that limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The detecting, generating, and retrieving steps recite an abstract idea.
The generating and outputting steps recite the additional elements. The generating and outputting limitations are mere insignificant extra solution activity that do no not integrate the abstract idea into a practical application. The recited memory and processor (Claim 1), AND non-transitory computer-readable medium and processor (Claim 20) are generic computer components to the abstract idea which is merely applying an abstract idea “with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea”.
The combination of the recited generating, outputting and computer limitations would not integrate the abstract idea into a practical application as it is merely using generic computer components to which the idea is applied to.
The last analysis regarding does the claim recite additional elements that amount to significantly more than the judicial exception is similar to the last analysis except that well-understood, routine and conventional devices are consider as part of the analysis. The generating and outputting limitations are merely insignificant extra solution activity so this doesn’t amount to significantly more. The recited processor, memory and non-transitory computer-readable medium are generic computer components that the abstract idea is applied to, and again the mere applying of an abstract idea “with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea” does not amount to significantly more. The combination of the recited receiving and computer limitations does not amount to significantly more as it is an application to generic computer components to which the idea is applied to.
Dependent Claims 2-14, 16-19 and 22-30 recite further mental processes and human activity and hence do not add any particular machine, particular transformation or meaningful limitations that would amount to significantly more and therefore they are rejected as well.
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 of this title, 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 1-3, 5, 7-11, 14-17, 19-21 and 24-30 are rejected under 35 U.S.C. 103 as being unpatentable over Porras et al. (Porras; US 20160219078) in view of Zhu et al. (Zhu; US 20190324780), further in view of Tur et al. (Tur; US 20170061316 A1).
Regarding Claim 1, Porras discloses an apparatus for providing personalized and contextualized environment security information (Abstract), comprising:
a memory (Fig 8); and
a processor (Fig 8) coupled with the memory and configured to:
detect a security event in an environment based on a first portion of collected sensor data ([0028] user interaction detection device(s) 106 may include the interactive display device 104 and/or other human activity detection devices (e.g., various types of sensors, including motion sensors, kinetic sensors, proximity sensors, thermal sensors, pressure sensors, force sensors, inertial sensors, cameras, microphones, gaze tracking systems, and/or others; [0036] network activity data 140 may be generated by, e.g., one or more network sensors or passive network monitoring programs; 602 of Fig 6A monitor network traffic);
generate contextual information for the security event based on a second portion of the collected sensor data, wherein the second portion is different from the first portion ([0038]-[0039] security threats detected; [0111] 610 of Fig 6A, nodes or flows on the network visualization may be highlighted dynamically in response to the occurrence of network events or un-highlighted in response to the network events being remediated (e.g., by user interactions 120));
user interface device(s) 104 may be embodied as…a touchscreen display device…includes audio input and output devices capable of capturing and recording human conversational spoken natural language input and outputting system-generated conversational spoken natural language output (such as microphones, speakers and headphones or earbuds)),
generate a user-specific remediation conversation from a plurality of different remediation conversations based on the user user dialog detected, 614 translate dialog to network directive, 664 respond with NL dialog), wherein each of the plurality of different remediation conversations (XXX?) comprises dialogue ([0092] natural language generator (NLG) module 446 generates a natural language version of the dialog output intent 444, the NL dialog output 448, which is output via, e.g., one or more speakers) interaction model 414 may be defined or personalized for specific types of users) system may proceed to block 672 and respond by outputting NL dialog asking the user for further clarification of the request); and
output at least a first portion of the user-specific remediation conversation on the output device ([0092] the NL dialog output 448, which is output via, e.g., one or more speakers, displays, or other user interface and/or user interaction detection devices 104, 106; 664 of Fig 6B). Porras does NOT specify a user profile for the conversation nor tailoring the dialogue, but does teach identifying a user ([0075] handling subsystem 122 may perform authentication processes to verify a user's identity).
In the same field of endeavor, Zhu discloses a method of receiving a user input including a partial request from a client system of a first user, analyzing the user input to generate one or more candidate hypotheses based on a personalized language model where each of the candidate hypotheses includes one or more of an intent-suggestion or a slot-suggestion, sending instructions for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, to the client system, where each suggested auto-completion comprises the partial request and the corresponding candidate hypothesis, receiving an indication of a selection by the first user of a first suggested auto-completion of the suggested auto-completions from the client system, and executing one or more tasks based on the first suggested auto-completion selected by the first user via one or more agents.
Zhu teaches a user profile indicative of access rights and user interface preferences ([0004] user profile may include demographic information, communication-channel information, and information on personal interests of the user; [0006] execute tasks that are relevant to user interests and preferences based on the user profile without a user input. In particular embodiments, the assistant system may check privacy settings to ensure that accessing a user's profile or other user information and executing different tasks are permitted subject to the user's privacy settings; [0039] assistant system 140 may proactively execute pre-authorized tasks that are relevant to user interests and preferences based on the user profile).
Zhu discloses generating a remediation conversation based on the user profile, the security event, and the contextual information, wherein the remediation conversation comprises dialogue for providing security information and guiding the first user to resolve the security event (Abstract, [0056] processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time. The analysis result may be stored in the user context engine 225 also as part of the user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Zhu using a user profile in order to facilitate convenience and efficiency when creating a dialogue with a known user.
The combination doesn’t specify tailoring the dialogue. However Porras teaches some examples of applications including multi-modal user interfaces…for example, Tur et al., PCT International Application Publication No. WO 2011/028833, entitled “Method and Apparatus for Tailoring Output of an Intelligent Automated Assistant to a User” ([0083]).
In the same field of endeavor, Tur et al. (Tur; US 20170061316 A1) discloses a method for tailoring the output of an intelligent automated assistant by conducting an interaction with a human user, collecting data about the user using a multimodal set of sensors positioned in a vicinity of the user, making a set of inferences about the user in accordance with the data, and tailoring an output to be delivered to the user in accordance with the set of inferences.
Tur discloses tailoring the dialogue ([0005] apparatus for tailoring the output of an intelligent automated assistant; [0013] adjustments can be applied to all users; [0056]-[0057] Fig 4 step 412, the output selection module 204 of the interaction management system 106 formulates an output responsive to the user's intent (e.g., directions to Bart's house)…In step 414, the output is adjusted in accordance with the user's preferences…this adjustment is applied to one or more of the following system actions: the pattern of assistance (e.g., the steps used to guide the user toward fulfilling his intent), the modality of the system output (e.g., speech, text, graphics, etc.), or the words that make up the system output (e.g., less formal language for younger and/or informally dressed users)) to a user profile ([0017] system inputs may include stored user data, such as a user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras and Zhu with Tur using tailored dialogue in order to provide added knowledge and the ability to adapt to user preferences to enhance user experience as suggested by Tur ([0004]).
Regarding Claim 2, Porras discloses the collected sensor data is received from a plurality of sensors located in the environment ([0028] user interaction detection device(s) 106 may include the interactive display device 104 and/or other human activity detection devices (e.g., various types of sensors, including motion sensors, kinetic sensors, proximity sensors, thermal sensors, pressure sensors, force sensors, inertial sensors, cameras, microphones, gaze tracking systems, and/or others; [0036] network activity data 140 may be generated by, e.g., one or more network sensors or passive network monitoring programs; 602 of Fig 6A monitor network traffic).
2>Regarding Claims 3 and 17, Porras discloses the processor is further configured to: receive, from the first user, a user response to the user-specific remediation conversation, wherein the user response requests additional information pertaining to the security event ([0113] In block 616, the computing system 100 branches in one of two directions, depending on the interaction type. If the computing system 100 interprets the user interaction as a network exploration directive (e.g., a request to manipulate the view of the visualization), the computing system 100 branches to block 622); identify a subset of the plurality of sensors in the environment that generated data used to formulate the security event (sensors that detect events are identified sensors and are a part of a subset of sensors); collect additional sensor data from the subset of the plurality of sensors or from all of the plurality of sensors (602 of Fig 6A continues to monitor system); and output a second portion of the user-specific remediation conversation (60 of Fig 6A continues to update display).
3>Regarding Claims 5 and 19, Porras discloses the user response is one of: a verbal response (Fig 4B, [0092] the NL dialog output 448, which is output via, e.g., one or more speakers), a gesture ([0021]), a physical input ([0027] touchscreen), and an expression ([0070] facial expression).
3>Regarding Claim 6, Porras discloses the processor is further configured to: receive a second user response from the first user ([0071] interaction model 414 may be defined or personalized for specific types of users); and output a third portion of the user-specific remediation conversation, wherein portions of the user-specific remediation conversation are outputted until the security event is resolved or the first user ceases providing user responses (see Figs 4B, 6A for ongoing dialog; [0117] In block 670 update the graphical elements of the visualization 114 to indicate graphically in the visualization 114 that the node is now quarantined).
Regarding Claim 7, Porras discloses the user-specific remediation conversation for the first user is different from a second user-specific remediation conversation from the plurality of different remediation conversations associated with a second user having at least one of a different access rights or a different user interface preferences ([0071] interaction model 414 may be defined or personalized for specific types of users; [0075] handling subsystem 122 may perform authentication processes to verify a user's identity).
2>Regarding Claim 8, Porras discloses the first portion of the collected sensor data comprises first sensor data from one sensor of the plurality of sensors and wherein the second portion of the collected sensor data comprises second sensor data from one or more different sensors ([0028], [0036] describe a plurality of different sensors, all sensors are used to monitor for security events).
8>Regarding Claim 9, Porras discloses the contextual information comprises a cause of the security event, and wherein the processor is further configured to generate the contextual information for the security event based on the second portion of the collected sensor data by: determining the cause of the security event by comparing the second portion of the collected sensor data with activity templates comprising sensor data during historic activities and associated activity identifiers ([0038] network activity data 140 may include historical records of network activity and/or predictive models and/or predictive models (models make predictions based a determination that a current activity is comparatively close to a historic activity); [0052] the network topology data 220 may identify nodes…infection profile data 222 includes, for example, statistical information based on historical infection data, or other information which indicates typical patterns or behaviors of known infections); and retrieving the second portion of the collected sensor data from the one or more different sensors based on the cause of the security event ([0038]-[0039] security threats detected; [0111] 610 of Fig 6A, nodes or flows on the network visualization may be highlighted dynamically in response to the occurrence of network events or un-highlighted in response to the network events being remediated (e.g., by user interactions 120), 616, 622 (continues in loop of directives and monitoring); [0028], [0036] describe a plurality of different sensors, all sensors are used to monitor for security events).
9>Regarding Claim 10, Porras discloses the user-specific remediation conversation comprises options for resolving the security event, wherein different options are provided by the user-specific remediation conversation for different contextual information (616 of Fig 6A options of network exploration or security directive, similarly with 664 of Fig 6B; Fig 6A, 622 continues to update the display and continue the dialog regarding highlighted events).
Regarding Claim 11, Porras discloses the user interface preferences comprises a preferred medium of communication, wherein a medium includes audio, video, and/or physical feedback; wherein audio preferences include at least one of: a preferred language ([0080] natural language), a preferred voice output, or a preferred speech speed; wherein video preferences include at least one of: an appearance of a user interface where the user-specific remediation conversation is generated (446, 448 of Fig 4B, 604 of Fig 6A; Fig 6B), or a video quality; and wherein physical feedback preferences include at least one of: a touchscreen sensitivity of the output device ([0027]) touchscreen display), a vibration strength of the output device, or a haptic feedback sensitivity of the output device.
2>Regarding Claim 14, Porras discloses the security event represents a summary of monitored activity over a period of time from at least one sensor of the plurality of sensors (610 of Fig 6A summarizes highlight network events).
Regarding Claim 15, Porras discloses a method for providing personalized and contextualized environment security information (Abstract), comprising:
detecting a security event in an environment based on a first portion of collected sensor data ([0028] user interaction detection device(s) 106 may include the interactive display device 104 and/or other human activity detection devices (e.g., various types of sensors, including motion sensors, kinetic sensors, proximity sensors, thermal sensors, pressure sensors, force sensors, inertial sensors, cameras, microphones, gaze tracking systems, and/or others; [0036] network activity data 140 may be generated by, e.g., one or more network sensors or passive network monitoring programs; 602 of Fig 6A monitor network traffic);
generating contextual information for the security event based on a second portion of the collected sensor data, wherein the second portion is different from the first portion ([0038]-[0039] security threats detected; [0111] 610 of Fig 6A, nodes or flows on the network visualization may be highlighted dynamically in response to the occurrence of network events or un-highlighted in response to the network events being remediated (e.g., by user interactions 120));
user interface device(s) 104 may be embodied as…a touchscreen display device…includes audio input and output devices capable of capturing and recording human conversational spoken natural language input and outputting system-generated conversational spoken natural language output (such as microphones, speakers and headphones or earbuds)),
generating a user-specific remediation conversation from a plurality of different remediation conversations based on the user user dialog detected, 614 translate dialog to network directive, 664 respond with NL dialog), wherein each of the plurality of different remediation conversations comprises dialogueinteraction model 414 may be defined or personalized for specific types of users) system may proceed to block 672 and respond by outputting NL dialog asking the user for further clarification of the request); and
outputting at least a first portion of the user-specific remediation conversation on the output device ([0092] the NL dialog output 448, which is output via, e.g., one or more speakers, displays, or other user interface and/or user interaction detection devices 104, 106; 664 of Fig 6B). Porras does NOT specify a user profile for the conversation nor tailoring the dialogue, but does teach identifying a user ([0075] handling subsystem 122 may perform authentication processes to verify a user's identity).
Zhu teaches a user profile indicative of access rights and user interface preferences ([0004] user profile may include demographic information, communication-channel information, and information on personal interests of the user; [0006] execute tasks that are relevant to user interests and preferences based on the user profile without a user input. In particular embodiments, the assistant system may check privacy settings to ensure that accessing a user's profile or other user information and executing different tasks are permitted subject to the user's privacy settings; [0039] assistant system 140 may proactively execute pre-authorized tasks that are relevant to user interests and preferences based on the user profile).
Zhu discloses generating a remediation conversation based on the user profile, the security event, and the contextual information, wherein the remediation conversation comprises dialogue for providing security information and guiding the first user to resolve the security event (Abstract, [0056] processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time. The analysis result may be stored in the user context engine 225 also as part of the user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Zhu using a user profile in order to facilitate convenience and efficiency when creating a dialog with a known user.
Tur discloses tailoring the dialogue ([0005] apparatus for tailoring the output of an intelligent automated assistant; [0013] adjustments can be applied to all users; [0056]-[0057] Fig 4 step 412, the output selection module 204 of the interaction management system 106 formulates an output responsive to the user's intent (e.g., directions to Bart's house)…In step 414, the output is adjusted in accordance with the user's preferences…this adjustment is applied to one or more of the following system actions: the pattern of assistance (e.g., the steps used to guide the user toward fulfilling his intent), the modality of the system output (e.g., speech, text, graphics, etc.), or the words that make up the system output (e.g., less formal language for younger and/or informally dressed users)) to a user profile ([0017] system inputs may include stored user data, such as a user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras and Zhu with Tur using tailored dialogue in order to provide added knowledge and the ability to adapt to user preferences to enhance user experience as suggested by Tur ([0004]).
Regarding Claim 16, Porras discloses the collected sensor data is received from a plurality of sensors located in the environment ([0028] user interaction detection device(s) 106 may include the interactive display device 104 and/or other human activity detection devices (e.g., various types of sensors, including motion sensors, kinetic sensors, proximity sensors, thermal sensors, pressure sensors, force sensors, inertial sensors, cameras, microphones, gaze tracking systems, and/or others; [0036] network activity data 140 may be generated by, e.g., one or more network sensors or passive network monitoring programs; 602 of Fig 6A monitor network traffic).
Regarding Claim 20, Porras discloses a non-transitory computer-readable medium storing instructions, executable by a processor ([0136]), for performing a method for providing personalized and contextualized environment security information (Abstract), comprising:
detecting a security event in an environment based on a first portion of collected sensor data ([0028] user interaction detection device(s) 106 may include the interactive display device 104 and/or other human activity detection devices (e.g., various types of sensors, including motion sensors, kinetic sensors, proximity sensors, thermal sensors, pressure sensors, force sensors, inertial sensors, cameras, microphones, gaze tracking systems, and/or others; [0036] network activity data 140 may be generated by, e.g., one or more network sensors or passive network monitoring programs; 602 of Fig 6A monitor network traffic);
generating contextual information for the security event based on a second portion of the collected sensor data, wherein the second portion is different from the first portion ([0038]-[0039] security threats detected; [0111] 610 of Fig 6A, nodes or flows on the network visualization may be highlighted dynamically in response to the occurrence of network events or un-highlighted in response to the network events being remediated (e.g., by user interactions 120));
user interface device(s) 104 may be embodied as…a touchscreen display device…includes audio input and output devices capable of capturing and recording human conversational spoken natural language input and outputting system-generated conversational spoken natural language output (such as microphones, speakers and headphones or earbuds)),
generating a user-specific remediation conversation from a plurality of different remediation conversations based on the user profile, the security event, and a user-specific subset of the contextual information (Figs 6A-6B, 612 user dialog detected, 614 translate dialog to network directive, 664 respond with NL dialog), wherein each of the plurality of different remediation conversations comprises dialogue ([0092] natural language generator (NLG) module 446 generates a natural language version of the dialog output intent 444, the NL dialog output 448, which is output via, e.g., one or more speakers) tinteraction model 414 may be defined or personalized for specific types of users) system may proceed to block 672 and respond by outputting NL dialog asking the user for further clarification of the request); and
outputting at least a first portion of the user-specific remediation conversation on the output device ([0092] the NL dialog output 448, which is output via, e.g., one or more speakers, displays, or other user interface and/or user interaction detection devices 104, 106; 664 of Fig 6B). Porras does NOT specify a user profile for the conversation nor tailoring the dialogue, but does teach identifying a user([0075] handling subsystem 122 may perform authentication processes to verify a user's identity).
Zhu teaches a user profile indicative of access rights and user interface preferences ([0004] user profile may include demographic information, communication-channel information, and information on personal interests of the user; [0006] execute tasks that are relevant to user interests and preferences based on the user profile without a user input. In particular embodiments, the assistant system may check privacy settings to ensure that accessing a user's profile or other user information and executing different tasks are permitted subject to the user's privacy settings; [0039] assistant system 140 may proactively execute pre-authorized tasks that are relevant to user interests and preferences based on the user profile).
Zhu discloses generating a remediation conversation based on the user profile, the security event, and the contextual information, wherein the remediation conversation comprises dialogue for providing security information and guiding the first user to resolve the security event (Abstract, [0056] processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time. The analysis result may be stored in the user context engine 225 also as part of the user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Zhu using a user profile in order to facilitate convenience and efficiency when creating a dialog with a known user.
Tur discloses tailoring the dialogue ([0005] apparatus for tailoring the output of an intelligent automated assistant; [0013] adjustments can be applied to all users; [0056]-[0057] Fig 4 step 412, the output selection module 204 of the interaction management system 106 formulates an output responsive to the user's intent (e.g., directions to Bart's house)…In step 414, the output is adjusted in accordance with the user's preferences…this adjustment is applied to one or more of the following system actions: the pattern of assistance (e.g., the steps used to guide the user toward fulfilling his intent), the modality of the system output (e.g., speech, text, graphics, etc.), or the words that make up the system output (e.g., less formal language for younger and/or informally dressed users)) to a user profile ([0017] system inputs may include stored user data, such as a user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras and Zhu with Tur using tailored dialogue in order to provide added knowledge and the ability to adapt to user preferences to enhance user experience as suggested by Tur ([0004]).
Regarding Claim 21, Porras discloses a method for providing personalized and contextualized environment security information (Abstract), comprising:
executing one or more machine learning models ([0071] rules, templates, and/or classifiers of the non-verbal interaction model 414 may be predefined, developed based on experimentation/observation, or learned by applying e.g., machine learning techniques to training data) that individually or in combination:
determine contextual information (including network sensors and the sensors detecting a user) for a security event based on input sensor data ([0038]-[0039] security threats detected; [0111] 610 of Fig 6A, nodes or flows on the network visualization may be highlighted dynamically in response to the occurrence of network events or un-highlighted in response to the network events being remediated (e.g., by user interactions 120)), wherein the contextual information comprises a cause of the security event ([0038] analyze the network activity data 140 over time to determine network flow characteristics and node behaviors that may indicate the existence of a network infection or some other type of network threat);
identify a plurality of remediation actions for resolving the security event based on the contextual information ([0097]-[0099] security initiative 124 may comprise a high level directive corresponding to a gesture to “quarantine that node,” the network-executable actions 132 produced by the security initiative translator module 510…security initiative translator module 510 may resolve the higher-level network security directives using a pre-defined set of templates, rules, or policies, which may include, for example, “block,” “deny,” “allow,” “redirect,” “quarantine,” “undo,” “constrain,” and/or “info” directives);
output a user remediation conversation comprising dialogue ([0092] the NL dialog output 448, which is output via, e.g., one or more speakers, displays, or other user interface and/or user interaction detection devices 104, 106; 664 of Fig 6B) visualization 700 to present the user with remediation options; Figs 6A-6B, 612 user dialog detected, 614 translate dialog to network directive, 664 respond with NL dialog);
receive, in the user remediation conversation, for example, “block,” “deny,” “allow,” “redirect,” “quarantine,” “undo,” “constrain,” and/or “info” directives); and
execute the selection of the at least one remediation action ([0099] A “block” directive may, for example, cause the system 110 to implement a full duplex filter). Porras does NOT specify a user profile for the conversation nor tailoring the dialogue, but does teach identifying a user ([0075] handling subsystem 122 may perform authentication processes to verify a user's identity).
Zhu teaches a user profile indicative of access rights and user interface preferences ([0004] user profile may include demographic information, communication-channel information, and information on personal interests of the user; [0006] execute tasks that are relevant to user interests and preferences based on the user profile without a user input. In particular embodiments, the assistant system may check privacy settings to ensure that accessing a user's profile or other user information and executing different tasks are permitted subject to the user's privacy settings; [0039] assistant system 140 may proactively execute pre-authorized tasks that are relevant to user interests and preferences based on the user profile).
Zhu discloses generating a remediation conversation based on the user profile, the security event, and the contextual information, wherein the remediation conversation comprises dialogue for providing security information and guiding the first user to resolve the security event (Abstract, [0056] processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time. The analysis result may be stored in the user context engine 225 also as part of the user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Zhu using a user profile in order to facilitate convenience and efficiency when creating a dialog with a known user.
Tur discloses tailoring the dialogue ([0005] apparatus for tailoring the output of an intelligent automated assistant; [0013] adjustments can be applied to all users; [0056]-[0057] Fig 4 step 412, the output selection module 204 of the interaction management system 106 formulates an output responsive to the user's intent (e.g., directions to Bart's house)…In step 414, the output is adjusted in accordance with the user's preferences…this adjustment is applied to one or more of the following system actions: the pattern of assistance (e.g., the steps used to guide the user toward fulfilling his intent), the modality of the system output (e.g., speech, text, graphics, etc.), or the words that make up the system output (e.g., less formal language for younger and/or informally dressed users)) to a user profile ([0017] system inputs may include stored user data, such as a user profile).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras and Zhu with Tur using tailored dialogue in order to provide added knowledge and the ability to adapt to user preferences to enhance user experience as suggested by Tur ([0004]).
Regarding Claim 24, Porras discloses the security event is detected in an environment, and wherein the user remediation conversation further comprises functional calls for performing daily operational procedure associated with the environment ([0053] reputation data 224 may be updated regularly (e.g., daily)).
Zhu teaches an agenda item may comprise a recurring item such as a daily digest ([0049]).
24>Regarding Claim 25, Porras discloses the functional calls enable a user to search the contextual information by requesting one or more of: video data, map data, event data ([0029] examples of network exploration directives 118 involve querying the system 110 for specific data, for example, to request that the visualization 114 display additional details about the current behavior of a network flow or node), photos, and personnel information.
24>Regarding Claim 26, Porras discloses the functional calls enable a user to edit security settings comprising one or more of: managing alerts ([0020] generate an interactive display for a human user, based on those alerts, thereby presenting a real-time visual depiction of the network, and of the current activity, flows, and cyber-threats. Components of the system 110 are designed to conduct conversational natural language dialog with a human user, including to receive natural language requests for one or more desired courses of action to remediate network threats), managing alarms, and generating emergency protocols ([0097]-[0099] security initiative 124 may comprise a high level directive corresponding to a gesture to “quarantine that node,” the network-executable actions 132 produced by the security initiative translator module 510…security initiative translator module 510 may resolve the higher-level network security directives using a pre-defined set of templates, rules, or policies, which may include, for example, “block,” “deny,” “allow,” “redirect,” “quarantine,” “undo,” “constrain,” and/or “info” directives).
Regarding Claim 27, Porras discloses at least one other remediation action from the plurality of remediation actions is presented in different dialogue visualization 700 to present the user with remediation options; Figs 6A-6B, 612 user dialog detected, 614 translate dialog to network directive, 664 respond with NL dialog, [0071] interaction model 414 may be defined or personalized for specific types of users; [0075] handling subsystem 122 may perform authentication processes to verify a user's identity).
Zhu discloses remediation action from remediation actions in different dialogue for a different input user profile ([0056] processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time. The analysis result may be stored in the user context engine 225 also as part of the user profile).
Tur discloses tailoring the dialogue ([0005] apparatus for tailoring the output of an intelligent automated assistant).
Regarding Claim 28, Porras discloses the user remediation conversation is output in an audio-format ([0027] user interface device(s) 104 includes audio input and output devices capable of capturing and recording human conversational spoken natural language input and outputting system-generated conversational spoken natural language output).
Regarding Claim 29, Porras discloses user responses in the user remediation conversation are provided using one or more of: voice commands ([0099]), text, or gestures ([0070] text, gestures).
Regarding Claim 30, Porras discloses the dialogue is tailored based on the user interface preferences comprising a preferred medium of communication, wherein a medium includes audio, video, and/or physical feedback; wherein audio preferences include at least one of: a preferred language ([0080] natural language), a preferred voice output, or a preferred speech speed; wherein video preferences include at least one of: an appearance of a user interface where the user remediation conversation is generated (446, 448 of Fig 4B, 604 of Fig 6A), or a video quality; and wherein physical feedback preferences include at least one of: a touchscreen sensitivity of an output device ([0027]) touchscreen display), a vibration strength of the output device, or a haptic feedback sensitivity of the output device (XZ).
Tur discloses tailoring the dialogue ([0005] apparatus for tailoring the output of an intelligent automated assistant).
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Porras, Zhu and Tur, further in view of Bar-Nahum et al. (Bar-Nahum; US 20200162489 A1).
3>Regarding Claims 4 and 18, Porras doesn’t teach tracking an object, but does teach tracking capabilities ([0028]).
In the same field of endeavor, Bar-Nahum discloses a security system wherein once an indication of a detected security event is received, one or more sensors are selected based on the detected security event. The selected sensors are used to detect additional information associated with a protected airspace associated with the detected security event.
Bar-Nahum discloses the user response further requests performing object tracking on an object in the environment, wherein the processor is further configured to output the second portion of the user-specific remediation conversation by: identifying the object using the additional sensor data; collecting the additional sensor data until the object is no longer detected; and generating an image of the object for display ([0063]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Bar-Nahum using object tracking in order to provide a swift response to the actual location of the security event and prevent or reduce the amount of property damage, loss of human life, or prevent criminal activity, as suggested by Bar-Nahum ([0002]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Porras, Zhu and Tur, further in view of Levy et al. (Levy; US 20190319987 A1).
Regarding Claim 12, Porras discloses the security event is indicative of a deviation from a known trend in the environment, wherein the processor is further configured to: execute a machine learning algorithm to classify whether input sensor data comprises the deviation ([0052] infection profile data 222 includes, for example, statistical information based on historical infection data, or other information which indicates typical patterns (trend) or behaviors of known infections (deviations from trends)), but doesn’t specify comparing vectors.
In the same field of endeavor, Levy discloses an interface for a threat management facility of an enterprise network supports the use of third-party security products within the enterprise network by providing access to relevant internal instrumentation and a programmatic interface for direct or indirect access to local security agents on compute instances within the enterprise network .
Levy discloses generating a feature vector representing the known trend in historic sensor data; comparing the feature vector against an input feature vector of the input sensor data; and classifying the input sensor data as comprising the deviation in response to determining, based on the comparing, that a difference between the feature vector and the input feature vector is greater than a threshold difference ([0193] the entity model may characterize a baseline of expected events derived from on events detected from the entity over an historical window, and may be expressed, e.g., as a vector in an event vector space or any other suitable representation for making comparisons to new event vectors in the event stream 1604; Fig 16).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Levy using vectors in order to improve accuracy and efficiency for security in an enterprise network, as suggested by Levy (Abstract, [0003]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Porras, Zhu and Tur, further in view of MALHOTRA et al. (Malhotra; US 20160189509 A1).
2>Regarding Claim 13, Porras discloses the security event is indicative of an inconsistency ([0052] infection profile data 222 includes, for example, statistical information based on historical infection data, or other information which indicates typical patterns or behaviors of known infections (inconsistency)), but doesn’t teach overriding a sensor.
In the same field of endeavor, Malhotra discloses a system for learned overrides for home security. A sensor of a security system may be armed. A trip signal may be received indicating a tripping of the sensor. It may be determined that the trip signal can be automatically overridden based on matching an identity of the sensor and a state of the security system with a pattern in a model. The pattern may represent a state of the security system in which automatically overriding the trip signal from the sensor is permitted. The trip signal from the sensor may be automatically overridden without input from a user.
Malhotra discloses between at least two sensors, wherein the processor is further configured to: receive a first sensor output from a first sensor in the environment, and a second sensor output from a second sensor in the environment; and determine, based on historical sensor data, that the first sensor should not output the first sensor output when the second sensor outputs the second sensor output ([0024] trip signal may be received indicating a tripping of the sensor. It may be determined that the trip signal can be automatically overridden based on matching an identity of the sensor and a state of the security system with a pattern in a model).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Malhotra using an override in order to create a convenient tailored security system to the preferences of a user based on gathered patterns, as suggested by Malhotra ([0001]).
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Porras and Zhu further in view of Levy.
Regarding Claim 22, Porras discloses the one or more machine learning models ([0071] interaction model 414 may be defined or personalized for specific types of users) are trained to determine the contextual information using a training dataset ([0071] rules, templates, and/or classifiers of the non-verbal interaction model 414 may be predefined, developed based on experimentation/observation, or learned by applying e.g., machine learning techniques to training data), but doesn’t teach using vectors.
Levy discloses a plurality of input vectors each comprising an activity template and a pre-determined cause ([0193] the entity model may characterize a baseline of expected events derived from on events detected from the entity over an historical window, and may be expressed, e.g., as a vector in an event vector space or any other suitable representation for making comparisons to new event vectors in the event stream 1604; Fig 16), wherein the activity template comprises sensor values from a plurality of sensors ([0177] first entity model may be a model characterizing a pattern of events expected from the number of sensors in a vector space).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Porras with Levy using vectors in order to improve accuracy and efficiency for security in an enterprise network, as suggested by Levy (Abstract, [0003]).
22>Regarding Claim 23, Porras discloses the plurality of sensors include different types of sensors ([0028] user interaction detection device(s) 106 may include the interactive display device 104 and/or other human activity detection devices (e.g., various types of sensors, including motion sensors, kinetic sensors, proximity sensors, thermal sensors, pressure sensors, force sensors, inertial sensors, cameras, microphones, gaze tracking systems, and/or others; [0036] network activity data 140 may be generated by, e.g., one or more network sensors or passive network monitoring programs; 602 of Fig 6A monitor network traffic).
Levy discloses different sensors ([0177] first entity model may be a model characterizing a pattern of events expected from the number of sensors in a vector space).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a. CHEN et al. (WO 2016173865 A1) discloses a method of operating a device by controlling the device based on input received from group members, uses a sensor unit for monitoring each group member for detecting an instruction provided by a group member. The instruction includes a visual or audible instruction.
b. D’SOUZA et al. (WO 2006101472 A1) discloses an alarm system computes a situation context output as a function of information received from sensor. The alarm system extracts contextual information related to situation of environment and aggregates contextual information using context aggregation to produce situation context output.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK S RUSHING whose telephone number is (571)270-5876. The examiner can normally be reached on 10-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached at 571-272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK S RUSHING/Primary Examiner, Art Unit 2689
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