Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an artificial intelligence module, cybersecurity module, a power management module in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Regarding claim 4, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 4 recites “(e.g., 2.4 GHz and 5 GHz)”. E.g. is the same as “for example”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-12 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Casaburi (US 10893058) in view of Justin (IN202441026890A) in view of Malasani (US 9584335).
Regarding claim 1, Casaburi teaches a wireless router (Fig. 5, Device 500) comprising,
At least one processor (Fig. 5, 520 processor).
(iv) a cybersecurity module (Fig. 5, 155 Malware Detection Component) configured to detect and block malware (C9 L37-48, Fig. 4, At step 420, if the presence of malware is predicted, based on the traffic flow signature matching a known (or suspected) malware signature, then the malware detection component may invoke a remedial action. For example, the malware detection component could generate an alert to notify owners or users of the computing device indicating that the device may have been compromised by a malware application. Similarly, when the malware detection component is installed on a network routing device connecting a local network segment, then the malware detection component may cause network traffic to/from that device to be dropped. That is, the traffic flows on that device may be blocked.)
using machine learning (C7L33-52,” the comparison component 215 (or security service) may compare the generated signature to a model of known good and bad signatures and determine whether to classify the generated signature as being indicative of malware. To do, the comparison component 215 (or security service) may use any suitable machine learning or modeling techniques to classify or score the signature as being indicative of malware”).
However, Casaburi does not explicitly teach an artificial intelligence module configured to optimize signal routing and bandwidth allocation and a power management module configured to adjust power usage based on sensor input.
In an analogous art, Justin teaches
(ii) an artificial intelligence module configured to optimize signal routing (Page 11 lines 18-20 “Through AI-driven analytics and real-time monitoring, the network can dynamically adjust routing paths based on network 20 conditions, congestion levels, and user demands.”) and bandwidth allocation (Page 11 L2; “AI algorithms to optimize bandwidth utilization”).
(v) a power management module configured to adjust power usage based on sensor input (Page 11 L2-3; “AI algorithms to optimize transmit power allocation”).
(vi) wherein the artificial intelligence module dynamically controls bandwidth allocation (Page 11 L2; “AI algorithms to optimize bandwidth utilization”) and security settings (Page 11 L4-6 “AI-enabled fault detection algorithms can identify patterns indicative of cyber-attacks or security breaches, enabling the network to fortify its defenses and mitigate potential threats”) based on real-time data from the sensors and network activity (Page 11 L18-20, “AI-driven analytics and real-time monitoring, the network can dynamically adjust routing paths based on network conditions, congestion levels, and user demands”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Casaburi’s teaching of router to also include Justin’s teaching of using to adjust power and bandwidth so create a more intelligent, dynamic and efficient communication system.
However, said references do not explicitly teach a plurality of sensors comprising environmental sensors.
In an analogous art, Malasani teaches a plurality of sensors comprising at least an RF signal (C4 L25-30, FIG. 1A shows a WiFi router, can be used to detect the presence of a large number of different mobile wireless devices (user associated devices) and/or the state of various other sensors as well, Casaburi, Justin and Malasani teach these mobile wireless devices that are effective as user associated devices can include wireless dog collars, key fobs, presence sensors, fitness trackers, laptops, tablet computers, mobile phones mobile phones and other type of user presence sensors.., Casaburi, Justin and Malasani teach these user presence sensors generate wireless WiFi signals that the WiFi router can detect directly) and
an environmental sensor (C13 L52- C14L11 “82 “the invention may be a wireless router with integrated sensors and activators .. Typical sensors include Motion detectors, temperature detectors, humidity detectors, moisture detectors, light sensors, camera modules, microphones, shock detectors.. Interface to automatically link action between some of these sensors and router controlled devices (certain sensors trigger certain action on router controlled devices) These automatic actions are based on certain times of the days or week Users can be notified of these sensor events through their mobile devices Users can control these router controlled devices remotely and at home through their mobile devices.”) Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Casaburi’s teaching of router and Justin’s teaching of AI to also include Malasani’s teaching environmental sensors to control the environment so that the router can be more versatile.
Regarding claim 2, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the artificial intelligence module continuously analyzes network performance metrics to adaptively optimize network connectivity (Justin, Page10 L9-12, “By leveraging real time data, machine learning techniques, and predictive analytics, the proposed system empowers networks to dynamically adapt to changing conditions, optimize performance, and preemptively address potential disruptions”). Malasani teaches the Wifi network (“FIG. 1A shows an overview of the concept. Here a router, such as a WiFi router”). Therefore, it would have been obvious to further apply Justin’s teaching of optimize network connectivity to Malasani’s WiFi network so that the Wi-fi network can be optimized and provide better quality and throughput for the users.
Regarding claim 3, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the environmental sensor monitors conditions such as temperature, humidity and occupancy to inform network adjustrnents (Malasani C13 L52- C14L11, “ the invention may be a wireless router with integrated sensors and activators.. Typical sensors include Motion detectors, temperature detectors, humidity detectors, moisture detectors, light sensors, camera modules, microphones, shock detectors.. Interface to automatically link action between some of these sensors and router controlled devices (certain sensors trigger certain action on router controlled devices) These automatic actions are based on certain times of the days or week Users can be notified of these sensor events through their mobile devices Users can control these router controlled devices remotely and at home through their mobile devices.”)
Regarding claim 4, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the artificial intelligence module manages multiple RF frequency bands (e.g., 2.4 GHz and 5 GHz”)( 801.11n) (Malasani, C1 L24-26, “These WiFi networks, which typically operate according various IEEE 802.11 standards (e.g. 801.11b, 802.11g, 801.11n, 802.11ac”) to optimize network performance based on user demand and environmental conditions (Justin, Page 11 L18-20 “Through AI-driven analytics and real-time monitoring, the network can dynamically adjust routing paths based on network conditions, congestion levels, and user demands.”)
Regarding claim 5, Casaburi, Justin and Malasani teach the wireless router method of claim 1, for detecting and mitigating cybersecurity threats in a wireless router, comprising,
(i) Implementing a cybersecurity module that employs machine learning algorithms to continuously monitor network traffic for anomalies (Casaburi C8 L30-35 “the method 300 begins at step 305, where the malware detection component launches the traffic-monitoring component. At step 310, the monitoring component observes a set of traffic flows present in the local network segment, as well as identifies new traffic flows as they are established,” C9L3-7, “Following step 325 or 330, if additional (or new) traffic flows remain to be evaluated, then malware detection component selects the next traffic flow to evaluate in the loop beginning at step 315, until all traffic flows observed at step 310 have been evaluated”).
(ii) Automatically blocking identified malware and potential threats based on real- time analysis without requiring manual intervention (Casaburi C9 L37-48, Fig. 4, At step 420, “if the presence of malware is predicted, based on the traffic flow signature matching a known (or suspected) malware signature, then the malware detection component may invoke a remedial action. For example, the malware detection component could generate an alert to notify owners or users of the computing device indicating that the device may have been compromised by a malware application. Similarly, when the malware detection component is installed on a network routing device connecting a local network segment, then the malware detection component may cause network traffic to/from that device to be dropped. That is, the traffic flows on that device may be blocked”).
Regarding claim 6, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the artificial intelligence module manages bandwidth allocation for multiple Internet of Things (IoT) devices connected to the network (Justin, Page 15; “8. A system for facilitating real-time data transmission and processing in IoT devices through AI-enabled wireless networks”).
Regarding claim 7, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the power management module adjusts power usage based on the number of connected devices and their activity levels (Justin, Page 15; “8. A system for facilitating real-time data transmission and processing in IoT devices through AI-enabled wireless networks”).
Regarding claim 8, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the artificial intelligence module dynamically selects the optimal communication channel to minimize interference and maximize throughput (Justin, page 10, L16-19 “with AI driven spectrum sensing, network nodes can intelligently detect and dynamically allocate unused or underutilized spectrum, thereby maximizing throughput, minimizing interference, and enhancing spectral efficiency”).
Regarding claim 11, Casaburi, Justin and Malasani teach the wireless router of claim 1, further comprising a mechanism for automatically updating firmware based on detected vulnerabilities and performance improvements identified through machine learning analysis (Justin, Page 6 L5-12 “AI algorithms can discern patterns indicative of potential threats or vulnerabilities, fortifying the network against cyber-attacks and intrusion attempts. Furthermore, adaptive routing algorithms dynamically adjust traffic paths based on network conditions and congestion levels, optimizing throughput and latency for end-users”).
Regarding claim 12, Casaburi, Justin and Malasani teach the wireless router of claim 1, wherein the artificial intelligence module optimizes power consumption based on historical network usage patterns to enhance energy efficiency (Justin, Page 11, L5-8; “by continuously learning from network dynamics and user behavior, AI-driven resource allocation algorithms can adapt and evolve over time, optimizing performance in a dynamic and unpredictable environment”).
Regarding claim 14, Casaburi, Justin and Malasani teach the smart wireless router of claim 1, further comprising a mobile application configured to provide real-time alerts (Casaburi C2 L21-27, “if the malware application is predicted to be present on the computing device, then an alert may be generated indicating the predicted presence of the malware application on the first computing device. Further still, in some cases, network traffic associated with the first network flow may be dropped or blocked until the predicted presence of malware is investigated or resolved”),
network monitoring (Casaburi Fig. 4 Step 405, “monitor traffic flow”), environmental readings (Malasani temperature detectors, humidity detectors), and remote device management (Malasani C13 L52- C14L11 “82 “ the invention may be a wireless router with integrated sensors and activators.. Typical sensors include Motion detectors, temperature detectors, humidity detectors, moisture detectors, light sensors, camera modules, microphones, shock detectors.. Interface to automatically link action between some of these sensors and router-controlled devices (certain sensors trigger certain action on router controlled devices)).
Regarding claim 15, Casaburi, Justin and Malasani teach the smart wireless router of claim 14, wherein the mobile application displays AI- generated network anomaly reports and allows user override of prioritized bandwidth and security configurations (Justin, Page 11, L14-16, “AI-enabled fault detection algorithms can identify patterns indicative of cyber-attacks or security breaches, enabling the network to fortify its defenses and mitigate potential threats”).
Regarding claim 16, Casaburi, Justin and Malasani teach the smart wireless router of claim 1, wherein the environmental sensors trigger automated control signals for one or more smart devices, including HVAC systems (Malasani; C4 L25-31, “FIG. 4 shows an example of how, under the “sensors” section of the router control page, an air conditioning device (which also acts as a sensor as well) that connected to the router (here using a wireless ZigBee connection) may be found. This air conditioning device is an example of a router controlled device.”), smart blinds, or air purifiers.
Regarding claim 17, Casaburi, Justin and Malasani teach the smart wireless router of claim16, wherein the AI engine communicates with smart home platforms using one or more protocols including Zigbee (Malasani; C4 L25-31, “FIG. 4 shows an example of how, under the “sensors” section of the router control page, an air conditioning device (which also acts as a sensor as well) that connected to the router (here using a wireless ZigBee connection) may be found. This air conditioning device is an example of a router controlled device.”), Z-Wave or Matter.
Regarding claim 18, Casaburi, Justin and Malasani teach the smart wireless router of claim 1, wherein the AI engine dynamically assigns priority levels to connected devices based on device classification, usage history, and real-time network demands (Justin Page 11 L3-5, “Through intelligent traffic shaping and prioritization mechanisms, the network can ensure equitable distribution of resources among users while minimizing energy consumption and latency”).
Regarding claim 19, Casaburi, Justin and Malasani teach the smart wireless router of claim 18, wherein the router maintains a prioritized traffic shaping queue that preempts non-critical traffic in favor of latency-sensitive applications (Justin Page 11, “Through intelligent traffic shaping and prioritization mechanisms, the network can ensure equitable distribution of resources among users while minimizing energy consumption and latency”).
Regarding claim 20, Casaburi, Justin and Malasani teach the smart wireless router of claim 1, wherein the router initiates scheduled security scans of connected IoT devices and enforces quarantine protocols for compromised endpoints (Justin, Page 15 Claim 8, “facilitating real-time data transmission and processing in IoT devices through AI-enabled wireless networks”).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Casaburi (US 10893058) in view of Justin (IN202441026890A) in view of Malasani (US 9584335) in view of Mehdi (US 20190356549).
Regarding claim 9, Casaburi, Justin and Malasani teach the wireless router of claim 1, further comprising a user interface (Malasani C14 L7-10; “Users can be notified of these sensor events through their mobile devices Users can control these router-controlled devices remotely and at home through their mobile devices.”) that provides real-time solutions for network optimization based on data collected from the sensors (Justin, Page 10 L9-12, “By leveraging real time data, machine learning techniques, and predictive analytics, the proposed system empowers networks to dynamically adapt to changing conditions, optimize performance, and preemptively address potential disruptions”).
Justin does not explicitly teach insights and recommendations. In an analogous art, Mehdi teaches a user interface that provides insights and recommendations for network optimization. ([0057] accessing network parameters that are to be optimized (e.g., parameters for reducing cost or more efficient management of the network); showing the representation of the network topology and/or optimized topology to the user on a graphical user interface; recommending changes to the network topology that reflect the optimal topology (e.g., suggesting usage/repurposing scenarios of current infrastructure); and recommending changes to other attributes of the network (e.g., applications/services and so on).) Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Justin’s teaching of network optimization to also include Mehdi’s teaching of the recommendations so that the user can be better informed and be able to better control the optimization of the network.
Claim(s) 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Casaburi (US 10893058) in view of Justin (IN202441026890A) in view of Malasani (US 9584335) in view of Official Notice.
Regarding claim 10, Casaburi, Justin and Malasani teach the smart wireless router of claim 1, further comprising mechanism based on AI-detected performance metrics and vulnerabilities (Justin, Page 6 L5-7, “AI algorithms can discern patterns indicative of potential threats or vulnerabilities, fortifying the network against cyber-attacks and intrusion attempts”). However, Justin does not explicitly teach an over-the-air (OTA) firmware update. However, the examiner takes official notice and submits that the limitation “over-the-air (OTA) firmware update” is well known in the art. Therefore, it would have been obvious for one of ordinary skill in the art at the time of the invention to modify said references to also include this well known in the art feature of OTA firmware update in order to allow the system to receive up-to-date fixes and new features.
Regarding claim 13, Casaburi, Justin and Malasani teach the smart wireless router of claim 1, except wherein encrypted DNS and VPN routing features are selectively enabled or disabled by the AI engine in response to real-time threat assessments. However, the examiner takes official notice and submits that the limitation “encrypted DNS and VPN routing features are selectively enabled or disabled by the AI engine in response to real-time threat assessments” is well known in the art. Therefore, it would have been obvious for one of ordinary skill in the art at the time of the invention to modify said references to include this well known in the art feature in order to comply with standard practice.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUNG L LAM whose telephone number is (571)272-6497. The examiner can normally be reached Monday -Thursday 9-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Anderson can be reached at 571-272-4177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DUNG L LAM/Examiner, Art Unit 2646
/MATTHEW D. ANDERSON/Supervisory Patent Examiner, Art Unit 2646