DETAILED ACTION
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 .
Priority
It is noted that the present application is a 371 National Phase Patent Application of PCT/US2022/041128, for which the 371(c) filing date is 08/22/2022. Applicant claims the benefit of US Provisional Application No. 63/235,338, filed 08/20/2021. Claims 8-20 have been afforded the benefit of this filing date.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/20/2024 and 10/09/2025 has been considered by examiner and made of record in the application file.
Election/Restrictions
Applicant’s election without traverse of Species II in the reply filed on 07/17/2026 is acknowledged.
Drawings
Applicant replacement drawings filed 02/20/2024 are acknowledged and accepted.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claims are crowded, examiner suggest to add a space after each claim ends.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 8-9, 12, 14-15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over O`Shea (US 20190004144 A1) in view of Fong (US 20190349861 A1) in further view of Johnson (US 20140080520 A1).
Regarding Claim 8, O`Shea discloses A user interface system for identifying and mapping at least one RF emitter on the surface of the Earth comprising (paragraph [0032], Fig.1, "FIG. 1 illustrates an example of a system 100 for determining emitter locations, according to one or more implementations." and paragraph [0141], Fig.6, "The processors 602 output the estimates to a user or administrator, e.g., through a display coupled to the receiver station 600, and/or transmit the estimates to other devices, e.g., other receiver stations or network nodes."(i.e., discloses of a interface system for emitter locations.)):
a source of RF data collected from said at least one RF emitter (paragraph [0032], Fig.1, "The system 100 includes a sensing device 102, an area 110 that includes a plurality of emitters that are indicated by candidate emitter locations 112, 114, 116, 118 and 119, and a receiver station 120." and paragraph [0034], "The sensing device 102 includes one or more radio signal receivers, also referred to as sensors, which are configured to receive radio signals from emitters. In some implementations, the sensors correspond to radio frequency (RF) antennas coupled to transponders and/or network interfaces on board the sensing device. The sensing device 102 also includes other hardware components, such as a digitizer (e.g., an analog to digital converter, or ADC) that converts the received analog radio signals to a digital format, one or more processors, and memory that stores instructions corresponding to operations performed by the sensing device, and also stores the radio signal data and/or processed information generated based on the radio signal data." (i.e., receiving station 120 and sensing device 102 collecting RF emitter data.));
a source of analytical processes (paragraph [0077], Fig., "In some implementations, maps of emitter locations or information about specific emitters can be used as analytics that are computable using the above techniques for dealing with low SNR scenarios." (i.e., there is a source of analytics.));
a source of visual and analytical tools (paragraph [0109], "For example, a result of process 300 is that, a heat-map of distance metrics for all candidate locations on the ground is obtained using signal measurements made by one sensing device, e.g., sensing device 102. This heat map provides low distance scores at inverted peaks on the ground where emitters are estimated to be emitting the patterned signal of interest. Further, this heat map can also provide a spatial map of locations at which unknown and low SNR signals are emitting. This spatial map of locations can be used for commercial radio deployment, spectrum assignment, connectivity analytics, emergency rescue missions, or numerous other radio applications. For example, each “hot” point on the heat map can represent a location of a cellular tower, a ship at sea, or some other radio signal emitter where mapping, coordination, or rescue provides a valuable service." and paragraph [0141], Fig., "In some implementations, the one or more processors 602 execute instructions stored in the memory 604 to perform the operations of the process 300 and/or the process 400. The processors 602 output the estimates to a user or administrator, e.g., through a display coupled to the receiver station 600, and/or transmit the estimates to other devices, e.g., other receiver stations or network nodes." (i.e., par.141 discloses a display for source of visual and analytical tool also disclosed in par.109 for analysis such as a heat map.)).
However, O`Shea does not explicitly disclose a source of external data collections; and an artificial intelligence module for selectively accessing and processing said RF data, said analytical processes, said visual and analytical tools, and said external data collections; wherein said artificial intelligence module is operative to generate tracking information for said at least one RF emitter.
Fong discloses a source of external data collections (paragraph [0029], "a RF sniffer on the case 120 and/or as the backpack 130 attached to the case 120 can detect in-range RF emitter(s), work with other RF sniffers through the coordination by the server 140 to geolocate, track and map the in-range RF emitters and use the aggregated data from the RF sniffers and/or with external data sources (e.g. FCC cell tower DB, internal or other 3rd party DBs etc.) to assess the in-range RF emitter(s)…cloud for mapping." (i.e., User external sources to obtain RF emitter(s).)).
O`Shea and Fong are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified O`Shea to implement the system of Fong of using external data because one in ordinary skill in the art would recognize using multiple data sources to locate RF emitter can help with accuracy and mapping of the RF emitters (Fong, paragraph [0029], "a RF sniffer on the case 120 and/or as the backpack 130 attached to the case 120 can detect in-range RF emitter(s), work with other RF sniffers through the coordination by the server 140 to geolocate, track and map the in-range RF emitters and use the aggregated data from the RF sniffers and/or with external data sources (e.g. FCC cell tower DB, internal or other 3rd party DBs etc.) to assess the in-range RF emitter(s)…cloud for mapping." ).
However, O`Shea in view of Fong do not disclose and an artificial intelligence module for selectively accessing and processing said RF data, said analytical processes, said visual and analytical tools, and said external data collections; wherein said artificial intelligence module is operative to generate tracking information for said at least one RF emitter.
Johnson discloses and an artificial intelligence module for selectively accessing and processing said RF data, said analytical processes, said visual and analytical tools, and said external data collections (paragraph [0008], " iPhones (iPhone is a trademark of Apple, Inc.), various handheld mobile data processing systems, etc. MSs move freely in the environment, and are unpredictably moveable (i.e. can be moved anywhere, anytime). Many of these Mobile data processing Systems (MSs)…" and paragraph [0032], "Locating functionality may incorporate triangulated locating of the MS, for example using a class of Radio Frequency (RF) wave spectrum (cellular, WiFi (some WiFi embodiments referred to as WiMax), bluetooth, etc)," and paragraph [0263], Fig.2D, "A service connected to the antenna (or cell tower) preferably uses historical information and artificial intelligence interrogation of MS travels to determine fields 1100h and 1100i." and paragraph [0288], "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data." and paragraph [0292], "Block 364 may perform Artificial Intelligence (Al) to determine where the MS may be going by consulting queue 22 and/or history 30." and paragraph [0316], "In an alternative embodiment, MS 2 may be equipped (e.g. as part of resources 38) with its own device 702 and field of view 704 for graphically identifying recognizable environmental objects or places to determine its own whereabouts…other graphically perceptible image which can be mapped to a location, the MS would complete a WDR similarly to above."(i.e., a mobile data processing system is reading on RF emitter. An artificial intelligence consulting many or all location data as disclosed in par.288;292.));
wherein said artificial intelligence module is operative to generate tracking information for said at least one RF emitter (paragraph [0288], Fig.3:326, "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data." (i.e., determine the location of MS as in RF emitter.)).
O`Shea in view of Fong and Johnson are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified O`Shea to implement the system of Johnson of artificial intelligence in order to predict the RF emitter movement based on history data thus enabling O`Shea system a predictive map of RF emitters (Johnson, paragraph [0288], "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data. ").
Regarding Claim 9, O`Shea in view of Johnson discloses all the limitation of claim 8.
Johnson further discloses wherein said tracking information comprises at least one of static analytics, streaming analytics and predictive information (paragraph [0062], " Another advantage herein is an MS maintains history of hotspot locations detected for providing graphical indication of hotspot whereabouts. This information can be used by the MS user in guiding where a user should travel in the future for access to services at the hotspot. Hotspot growth prevents a database in being timely configured with new locations. The MS can learn where hotspots are located, as relevant to the particular MS. The hotspot information is instantly available to the MS. " and paragraph [0288], "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data." (i.e., predictive information such as the determining where the MS is going. Streaming analytics as described in par.62. The hotspot location can be static as the location don’t usually change.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 12, O`Shea in view of Fong in further view of Johnson discloses all the limitation of claim 8.
Johnson further discloses wherein said artificial intelligence module is operative to cause delivery of automated alert notifications to one or more defined users for at least one RF emitter based on the artificial intelligence module generated tracking information results (paragraph [0675], "Set Geofence arrival alert: allows an action for alerting based on arrival to a geofenced area; This privilege may be set with parameter(s) for which eligible area(s) to define geofences; An alternate embodiment will have individual privileges for each area(s);" and paragraph [1507], "It helps for a plurality of users to know what tags each other uses so that comparisons can be made on a tag to tag basis between different profiles. A plurality of MS users should be aware of profile tags in use between each other so as to provide functionality for doing comparisons, otherwise profiles that use different tags cannot be compared." (i.e., the tracking information can be shared or delivered to users of RF emitter.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 14, O`Shea in view of Fong in further view of Johnson discloses all the limitation of claim 8.
Johnson further discloses further comprising a display, wherein said RF data, said analytical processes, said visual and analytical tools, and said external data collections are accessible in real time to provide emitter tracking with geographic and contextual information about the emitter (paragraph [0316], "the MS would have access to anticipated objects, locations and dimensions much the same way described for FIGS. 7A through 7D, either locally maintained or verifiable with a connected service. Upon a successful recognition of an object, place, or other graphically perceptible image which can be mapped to a location, the MS would complete a WDR similarly to above. The MS may recognize addresses, buildings, landmarks, of other pictorial data. Thus, the MS may graphically determine its own location." and paragraph [0466], "the user may know that the whereabouts supervisor process enabled/disabled indicates whether or not to have whereabouts timeliness monitored in real time. Enabling the whereabouts supervisor process enables monitoring for the WTV in real time, and disabling the whereabouts supervisor process disables monitoring the WTV in real time." and paragraph [0898], "Thereafter, block 4306 displays acceptable methods for accepting data from other MSs, preferably in a radio button form in a visually perceptible user interface embodiment." (i.e., discloses a display and tracking information on the MS e.g. the emitter, O`Shea discloses said visual and analytical tools, and said external data collections.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 15, O`Shea discloses A method for identifying and mapping at least one RF emitter on the surface of the Earth comprising (paragraph [0032], Fig.1, "FIG. 1 illustrates an example of a system 100 for determining emitter locations, according to one or more implementations." and paragraph [0141], Fig.6, "The processors 602 output the estimates to a user or administrator, e.g., through a display coupled to the receiver station 600, and/or transmit the estimates to other devices, e.g., other receiver stations or network nodes."(i.e., discloses of a interface system for emitter locations.)):
accessing RF data collected from said at least one RF emitter (paragraph [0032], Fig.1, "The system 100 includes a sensing device 102, an area 110 that includes a plurality of emitters that are indicated by candidate emitter locations 112, 114, 116, 118 and 119, and a receiver station 120." and paragraph [0034], "The sensing device 102 includes one or more radio signal receivers, also referred to as sensors, which are configured to receive radio signals from emitters. In some implementations, the sensors correspond to radio frequency (RF) antennas coupled to transponders and/or network interfaces on board the sensing device. The sensing device 102 also includes other hardware components, such as a digitizer (e.g., an analog to digital converter, or ADC) that converts the received analog radio signals to a digital format, one or more processors, and memory that stores instructions corresponding to operations performed by the sensing device, and also stores the radio signal data and/or processed information generated based on the radio signal data." (i.e., receiving station 120 and sensing device 102 collecting RF emitter data.));
accessing analytics (paragraph [0077], Fig.1, "In some implementations, maps of emitter locations or information about specific emitters can be used as analytics that are computable using the above techniques for dealing with low SNR scenarios." (i.e., there is a source of analytics.));
accessing visual and analytical tools (paragraph [0109], "For example, a result of process 300 is that, a heat-map of distance metrics for all candidate locations on the ground is obtained using signal measurements made by one sensing device, e.g., sensing device 102. This heat map provides low distance scores at inverted peaks on the ground where emitters are estimated to be emitting the patterned signal of interest. Further, this heat map can also provide a spatial map of locations at which unknown and low SNR signals are emitting. This spatial map of locations can be used for commercial radio deployment, spectrum assignment, connectivity analytics, emergency rescue missions, or numerous other radio applications. For example, each “hot” point on the heat map can represent a location of a cellular tower, a ship at sea, or some other radio signal emitter where mapping, coordination, or rescue provides a valuable service." and paragraph [0141], Fig., "In some implementations, the one or more processors 602 execute instructions stored in the memory 604 to perform the operations of the process 300 and/or the process 400. The processors 602 output the estimates to a user or administrator, e.g., through a display coupled to the receiver station 600, and/or transmit the estimates to other devices, e.g., other receiver stations or network nodes." (i.e., par.141 discloses a display for source of visual and analytical tool also disclosed in par.109 for analysis such as a heat map.));
accessing visual and analytical tools (paragraph [0109], "For example, a result of process 300 is that, a heat-map of distance metrics for all candidate locations on the ground is obtained using signal measurements made by one sensing device, e.g., sensing device 102. This heat map provides low distance scores at inverted peaks on the ground where emitters are estimated to be emitting the patterned signal of interest. Further, this heat map can also provide a spatial map of locations at which unknown and low SNR signals are emitting. This spatial map of locations can be used for commercial radio deployment, spectrum assignment, connectivity analytics, emergency rescue missions, or numerous other radio applications. For example, each “hot” point on the heat map can represent a location of a cellular tower, a ship at sea, or some other radio signal emitter where mapping, coordination, or rescue provides a valuable service." and paragraph [0141], "In some implementations, the one or more processors 602 execute instructions stored in the memory 604 to perform the operations of the process 300 and/or the process 400. The processors 602 output the estimates to a user or administrator, e.g., through a display coupled to the receiver station 600, and/or transmit the estimates to other devices, e.g., other receiver stations or network nodes." (i.e., par.141 discloses a display for source of visual and analytical tool also disclosed in par.109 for analysis such as a heat map.)).
However, O`Shea does not explicitly disclose accessing at least one data collection from a plurality of external data collections; utilizing an artificial intelligence module for selectively accessing and processing said RF data, said analytics, said visual and analytical tools, and said external data collections; whereby tracking information is created by said artificial intelligence module for said at least one RF emitter.
Fong discloses accessing at least one data collection from a plurality of external data collections (paragraph [0029], "a RF sniffer on the case 120 and/or as the backpack 130 attached to the case 120 can detect in-range RF emitter(s), work with other RF sniffers through the coordination by the server 140 to geolocate, track and map the in-range RF emitters and use the aggregated data from the RF sniffers and/or with external data sources (e.g. FCC cell tower DB, internal or other 3rd party DBs etc.) to assess the in-range RF emitter(s)…cloud for mapping." (i.e., User external sources to obtain RF emitter(s).)).
O`Shea and Fong are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified O`Shea to implement the system of Fong of using external data because one in ordinary skill in the art would recognize using multiple data sources to locate RF emitter can help with accuracy and mapping of the RF emitters (Fong, paragraph [0029], "a RF sniffer on the case 120 and/or as the backpack 130 attached to the case 120 can detect in-range RF emitter(s), work with other RF sniffers through the coordination by the server 140 to geolocate, track and map the in-range RF emitters and use the aggregated data from the RF sniffers and/or with external data sources (e.g. FCC cell tower DB, internal or other 3rd party DBs etc.) to assess the in-range RF emitter(s)…cloud for mapping." ).
However, O`Shea in view of Fong do not explicitly disclose utilizing an artificial intelligence module for selectively accessing and processing said RF data, said analytics, said visual and analytical tools, and said external data collections; whereby tracking information is created by said artificial intelligence module for said at least one RF emitter.
Johnson discloses utilizing an artificial intelligence module for selectively accessing and processing said RF data, said analytics, said visual and analytical tools, and said external data collections (paragraph [0008], "iPhones (iPhone is a trademark of Apple, Inc.), various handheld mobile data processing systems, etc. MSs move freely in the environment, and are unpredictably moveable (i.e. can be moved anywhere, anytime). Many of these Mobile data processing Systems (MSs)…" and paragraph [0032], Fig., "Locating functionality may incorporate triangulated locating of the MS, for example using a class of Radio Frequency (RF) wave spectrum (cellular, WiFi (some WiFi embodiments referred to as WiMax), bluetooth, etc)," and paragraph [0263], Fig.2D, "A service connected to the antenna (or cell tower) preferably uses historical information and artificial intelligence interrogation of MS travels to determine fields 1100h and 1100i." and paragraph [0288], "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data." and paragraph [0292], "Block 364 may perform Artificial Intelligence (Al) to determine where the MS may be going by consulting queue 22 and/or history 30." and paragraph [0316], "In an alternative embodiment, MS 2 may be equipped (e.g. as part of resources 38) with its own device 702 and field of view 704 for graphically identifying recognizable environmental objects or places to determine its own whereabouts…other graphically perceptible image which can be mapped to a location, the MS would complete a WDR similarly to above."(i.e., a mobile data processing system is reading on RF emitter. An artificial intelligence consulting many or all location data as disclosed in par.288;292.));
whereby tracking information is created by said artificial intelligence module for said at least one RF emitter (paragraph [0288], Fig.3:326, "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data." (i.e., determine the location of MS as in RF emitter. Examiner notes this limitation was not given no patentable weight because it is a method claim that recites 'whereby' see MPEP 2111.04.)).
O`Shea in view of Fong and Johnson are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified O`Shea to implement the system of Johnson of artificial intelligence in order to predict the RF emitter movement based on history data thus enabling O`Shea system a predictive map of RF emitters (Johnson, paragraph [0288], "Block 326 may perform Artificial Intelligence (AI) to determine where the MS may be going by consulting many or all of the location history data. ").
Regarding Claim 17, which is similar in scope to claim 12, thus rejected under the same rationale.
Regarding Claim 19, which is similar in scope to claim 14, thus rejected under the same rationale.
Regarding Claim 20, which is similar in scope to claim 1, thus rejected under the same rationale.
Examiner notes O`Shea discloses a computer program product embodied in a non-transitory machine-readable medium (paragraph [0147], “one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus.”).
Claim(s) 10-11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over O`Shea (US 20190004144 A1) in view of Fong (US 20190349861 A1) in view of Johnson (US 20140080520 A1) in further view of Pandya (US 20220414527 A1).
Regarding Claim 10, O`Shea in view of Fong in further view of Johnson discloses all the limitation of claim 8.
However, O`Shea in view of Fong in further view of Johnson do not explicitly disclose wherein said artificial intelligence module comprises an unsupervised learning module providing mission thread prediction.
Pandya discloses wherein said artificial intelligence module comprises an unsupervised learning module providing mission thread prediction (paragraph [0023], Fig.1, "In FIG. 1A, in a first stage, individual end-user characteristics 110 are collected, including their persona, history, and current behavior. In this case, a user's persona type refers to their role and/or access level with respect to the platform, such as but not limited to a tester, business consultant, product retailer, call center agent, customer service representative, etc. Furthermore, in some embodiments, persona may also indicate where the user is located, as the user's site or region may affect the model that may be required (e.g., a journey for a specific product can differ from country to country). In addition, user history refers broadly to the actions and selections made by the user while accessing their computing device and/or the software application (“app”) platform. This can include discrete steps such as signing into an email system, opening an inbox, drafting an email, reviewing the sent folder, etc. This information will be used by the service to link the particular series of actions and behaviors engaged in by that user to the type of role they play, in order to more accurately predict the next step(s) that should be taken. Furthermore, a user's current behavior (while using the software platform, or “in-app behavior”) allows the service to predict the user's present needs and model requirements." and paragraph [0032], "In different embodiments, the model may include an artificial intelligence model (e.g., a multiple regression analysis model, an artificial neural networks (ANNs) model, a case-based reasoning (CBR) model, and/or the like), a machine learning model (e.g., a supervised learning model, an unsupervised learning model," (i.e., unsupervised learning model providing mission thread prediction.)).
O`Shea in view of Fong in further view of Johnson and Pandya are considered to be analogous to the claimed invention because they are in the same field Machine learning. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified O`Shea to implement the system of Pandya of predicting the user next task in order to enable efficient performance of the selected task (Pandya, paragraph [0024], "The output of the MLA and the training data are processed using computer vision techniques and a neural network that is configured to generate the predictive steps or model that is most likely to serve the user's current needs or will most likely enable efficient performance of the selected task. ").
Regarding Claim 11, O`Shea in view of Fong in view of Johnson in further view of Pandya discloses all the limitation of claim 10.
Pandya further discloses wherein user behavior is monitored and fed back to the artificial intelligence module for model training and learning (paragraph [0023], Fig.1, "In FIG. 1A, in a first stage, individual end-user characteristics 110 are collected, including their persona, history, and current behavior. In this case, a user's persona type refers to their role and/or access level with respect to the platform, such as but not limited to a tester, business consultant, product retailer, call center agent, customer service representative, etc. Furthermore, in some embodiments, persona may also indicate where the user is located, as the user's site or region may affect the model that may be required (e.g., a journey for a specific product can differ from country to country). In addition, user history refers broadly to the actions and selections made by the user while accessing their computing device and/or the software application (“app”) platform. This can include discrete steps such as signing into an email system, opening an inbox, drafting an email, reviewing the sent folder, etc. This information will be used by the service to link the particular series of actions and behaviors engaged in by that user to the type of role they play, in order to more accurately predict the next step(s) that should be taken. Furthermore, a user's current behavior (while using the software platform, or “in-app behavior”) allows the service to predict the user's present needs and model requirements." and paragraph [0024], "end-user characteristics 110 can be used to capture the steps being taken by day-to-day users in accomplishing their tasks, here identified as previously built journeys 120, which is inputted into an intuitive assistant (I.A.) model builder 130, for example as training data." and paragraph [0032], "In different embodiments, the model may include an artificial intelligence model (e.g., a multiple regression analysis model, an artificial neural networks (ANNs) model, a case-based reasoning (CBR) model, and/or the like), a machine learning model (e.g., a supervised learning model, an unsupervised learning model," (i.e., user behavior feedback for training and learning in order to predict next steps.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 16, which is similar in scope to claim 11, thus rejected under the same rationale.
Claim(s) 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over O`Shea (US 20190004144 A1) in view of Fong (US 20190349861 A1) in view of Johnson (US 20140080520 A1) in further view of CHEN (US 20160320199 A1).
Regarding Claim 13, O`Shea in view of Fong in further view of Johnson discloses all the limitation of claim 8.
Johnson further discloses wherein the tracking information (paragraph [0056], "In a preferred embodiment, permissions are maintained in a peer to peer manner prior to lookup for proper service sharing. In another embodiment, permissions are specified and used at the time of granting access to the shared services. Once granted for sharing, services can be used in a mode as if the sharing user is using the services, or in a mode as if the user accepting the share is a new user to the service. Routing paths are dynamically reconfigured and transparently used as MSs travel. Hop counts dynamically change to strive for a minimal number of hops for an MS getting access to a desirable service. Route communications depend on where the MS needing the service is located relative a minimal number of hops through other MSs to get to the service. Services can be propagated from DLMs to DLMS, DLMs to ILMs, or ILMs to ILMs." (i.e., the tracking information is exportable and shareable.)).
However, O`Shea in view of Fong in further view of Johnson do not disclose wherein the tracking information is collectable at instants of time.
Cuff discloses wherein the tracking information is collectable at instants of time and is exportable and shareable with one or more additional users for dynamic analysis (paragraph [0079], "As shown in FIGS. 4A and 4B, the user interface integrates both functions of instant messaging and geographic navigation. The mobile device is enabled to guide its own navigation, monitor its own navigation status, and track another party's navigation status, while maintain real time communication with the other party." and paragraph [0106], "Instant Messaging Application 605 that is configured to allow two or more users to exchange messages on the real time over the Internet;" and paragraph [0107], Fig.6, "Geographic Information display module 500 that obtains geographic location information of one or more users at one or more instants, marks the locations of the one or more users on a map that is displayed on the user interface, and updates the displayed map according to the geographic location variations of the one or more users, wherein a user identification associated with a user of the mobile device including module 500 is fixed at a specific location on the user interface." and paragraph [0047], "The Network Monitor 302 also preferably tallies the difference in the number of wireless devices in a given area, from one consecutive instantaneous aggregate location snapshot to the next…" (i.e., collection "snapshots" of user locations.)).
O`Shea in view of Fong in further view of Johnson and CHEN are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified O`Shea to implement the system of CHEN in order to enable of sharing user location to plurality of users in order to analyze the other user location and keep track the position of the group of users and improves navigation (CHEN, paragraph [0023], “A user logs onto a user account associated with the social network platform while the user is travelling between two locations and needs navigation service. A geographic display function is integrated into the social network platform. As a result of using the geographic display method in this application, a map is rendered to provide a better display effect based on the social network platform and thereby improves user experience with both social networking and navigation.” and paragraph [0079], " As shown in FIGS. 4A and 4B, the user interface integrates both functions of instant messaging and geographic navigation. The mobile device is enabled to guide its own navigation, monitor its own navigation status, and track another party's navigation status, while maintain real time communication with the other party.").
Regarding Claim 18, which is similar in scope to claim 13, thus rejected under the same rationale.
Other Pertinent References
The Following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Vitebsky; Stanley. "PRIMARY SIGNAL DETECTION USING DISTRIBUTED MACHINE LEARNING IN MULTI-AREA ENVIRONMENT." (US 20210274352 A1), Filed 2021-02-11.
Meiyappan; Subramanian S. "SYSTEMS AND METHODS OF RADIO FREQUENCY DATA MAPPING AND COLLECTION FOR ENVIRONMENTS." (US 20220326340 A1), Filed 2022-04-13, Priority Date 2021-04-13.
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Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Erkin S. Abdullaev whose telephone number is (571)272-4135. The examiner can normally be reached Monday - Friday - 8:00 am - 5:00 pm.
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ERKIN S. ABDULLAEV
Examiner
Art Unit 2648
/ERKIN ABDULLAEV/Examiner, Art Unit 2648 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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