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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/17/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zamat (US-2002008067-A1, published: 2002-12-21) in view of Friday et al. (US-9743254-B2, published: 2017-08-22) hereinafter Friday.
For examination purposes, claims 1-8 referring to an apparatus and claims 9-15 referring to a method are henceforth grouped together for claims mirroring the same limitations or which disclose analogous art to the invention as claimed.
Regarding Claim 1, Zamat discloses an electronic device for locating wireless signals (Zamat, fig. 2, par. 27; the antenna arrangement 108 of a user terminal 106), comprising: a plurality of antenna units (Zamat, fig. 2, par. 27; terminal 106 in this example includes two antennas 110 and 112), providing a plurality of antenna patterns (Zamat, fig. 2, par. 27; Antennas 110 and 112 can be quadrifilar antennas, and provide respective antenna patterns A and B); a switching control circuit, connected to the antenna units, wherein the switching control circuit is selectively electrically connected to at least one of the antenna units, so as to select one of the antenna patterns correspondingly (Zamat, fig. 2, par.27; antennas 110 and 112, which are arranged as separate antennas that are each coupled to a switch 114); a communication module, electrically connected to the switching control circuit (Zamat, fig. 2, par. 28; The user terminal 106 further includes, among other things, a feed assembly 115, a receiver 116 and a controller 118); and a processing circuit, electrically connected to the switching control circuit and the communication module and configured to control the switching control circuit to be electrically connected to the communication module and at least one of the antenna units, wherein the processing circuit controls the switching control circuit to switch connections of the communication module to the antenna units in sequence to collect beacon broadcast information corresponding to a plurality of access points for each of the antenna patterns (Zamat, fig. 2, par. 30; During operation, the microcontroller circuit of controller 118 compares the respective received signal strength indicators (RSSI) associated with the signals received at the respective antennas 110 and 112, and controls the switch 114 to select for signal reception the antenna having the higher RSSI value), and the processing circuit establishes a prediction model based on each of the antenna patterns and the beacon broadcast information corresponding to each of the access points, and divides the antenna patterns into a plurality of directional regions, so that the processing circuit obtains the beacon broadcast information in a current environment and selects, through the prediction model, at least one of the antenna patterns in one of the directional regions for locating (Zamat, figs. 1-2, par. 30; The microcontroller circuit can also have access to information pertaining to the location of the satellite 102 from which the user terminal 106 is to receive transmission signals, and use this information to control the switch 114 to select one of the antennas 110 and 112 that provides a more favorable antenna pattern for receiving the signals), see also par. 33 and figs. 3-5.
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Zamat does not explicitly disclose the implementation of a predictive model to process the received signal information to select one antenna pattern. However, Friday discloses a system for using received signals to determine the location of a wireless terminal and refine the location determination using machine learning models (Friday, par. 26; Machine learning is used in some embodiments to refine the path loss parameters used for different areas based on reported received signal measurements. In various embodiments a wireless terminal reports the receipt of multiple beacon signals from different access points along with the received signal strength. A location is determined initially based on the probability surfaces corresponding to the access points which transmitted the received signals and the reported signals strength).
Therefore, a person of ordinary skill in the art would be motivated to combine Zamat’s teachings for antenna switching between different antenna patterns with Friday’s teachings for wireless terminal location determination using machine learning models to enhance the antenna pattern selection determination by implementing a context based information into the algorithm.
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Regarding Claim 9, Zamat discloses a processing method for locating wireless signals, applicable to an electronic device, the processing method comprising: selectively switching a plurality of antenna units in the electronic device, and correspondingly selecting one of a plurality of antenna patterns in a current environment, so as to obtain beacon broadcast information corresponding to a plurality of access points for each of the antenna patterns (Zamat, fig. 2, par. 2; a system and method for switching between different antenna patterns to satisfy antenna gain requirements over a desired coverage angle) see also, par. 30-31 and 38; inputting the beacon broadcast information to a prediction model, so as to determine, based on the prediction model, a directional region corresponding to the beacon broadcast information (Zamat, par. 30; The microcontroller circuit can also have access to information pertaining to the location of the satellite 102 from which the user terminal 106 is to receive transmission signals, and use this information to control the switch 114 to select one of the antennas 110 and 112 that provides a more favorable antenna pattern for receiving the signals); using at least one of the antenna patterns in the directional region for locating; and determining whether the access points have significant relative movements or disappear, and when the access points have significant relative movements or disappear, reestablishing the prediction model (Zamat, par. 31; if the microcontroller circuit determines that antenna pattern A provided by antenna 110 enables the signal from satellite 102 to be received at a signal strength higher than it would be received by antenna 112, the microcontroller circuit controls switch 114 to select antenna 110 for signal reception).
Zamat does not explicitly disclose the implementation of a predictive model to process the received signal information to select one antenna pattern. However, Friday discloses a system for using received signals to determine the location of a wireless terminal and refine the location determination using machine learning models (Friday, par. 26; Machine learning is used in some embodiments to refine the path loss parameters used for different areas based on reported received signal measurements. In various embodiments a wireless terminal reports the receipt of multiple beacon signals from different access points along with the received signal strength. A location is determined initially based on the probability surfaces corresponding to the access points which transmitted the received signals and the reported signals strength).
Therefore, a person of ordinary skill in the art would be motivated to combine Zamat’s teachings for antenna switching between different antenna patterns with Friday’s teachings for wireless terminal location determination using machine learning models to enhance the antenna pattern selection determination by implementing a context based information into the algorithm.
Regarding claims 2 and 10, the combination of Zamat and Friday teach the apparatus and method of claims 1 and 9, wherein the prediction model identifies, based on the beacon broadcast information in the current environment, which access point falls in which directional region, and selects, based on one access point to be used, at least one of the antenna patterns in the corresponding directional region for locating (Friday, par. 53; some embodiments an individual beacons, e.g., transmitted using a beam of a BTLE (Blue Tooth Low Energy) base station directly communicates information, relevant to the particular geographic area to which the individual beam used to transmit the beacon corresponds, given that the beams are directional, they may cover an area extending over a long distance) see also pars. 54, 74, 158 and 242 and figs. 5 and 19.
Regarding claims 3 and 11, the combination of Zamat and Friday teach the apparatus and method of claims 1 and 9, wherein the beacon broadcast information comprises a service set identifier (SSID), a media access control (MAC) address (Friday, par. 48; The beacons are used to communicate information, either directly via the contents of the beacon or by correlating one or more identifiers in the transmitted beacons to other information) see also par. 128, fig. 9A, step 904, and a received signal strength indication (RSSI) that correspond to the access point (Friday, par. 9; The wireless terminal may report such RSSI values for each beacon which it receives) see also fig. 9A step 912.
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Regarding claims 4 and 12, the combination of Zamat and Friday teach the apparatus and method of claims 3 and 11, wherein the beacon broadcast information further comprises channel state information (CSI) (Friday, fig. 8, step 804, par. 118; Signal information may be measured by a wireless receiver which receives and measures signaling transmitted by neighboring APs) examiners note, although not explicitly mentioned by Zamat or Friday, the use of CSI is a well-known method in the art to obtain signal characteristics to aid in the location of terminal devices, one of ordinary skill in the art would be prompted to try to implement CSI to enhance the location determination of beacon signals and terminals.
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Regarding claims 5 and 13, the combination of Zamat and Friday teach the apparatus and method of claims 1 and 9, wherein the directional regions are four quadrant regions divided with the electronic device as a center (Friday, fig. 13, par. 156; an exemplary coverage area diagram 1300 where three different base stations 1314, 1316, 1318 transmit beacons which can be detected at a zone (Zone 1 1308) corresponding to the intersection of the overlapping coverage areas of the three different base stations), see also Zamat par. 27, for the use of a quadrifilar antenna. Examiner notes: Friday’s methods are not limited to a 3 beacon setup, as observed in figure 19, where there are four zones (i.e. regions) with the option of adding more beacons or zones.
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Regarding claims 6 and 14, the combination of Zamat and Friday teach the apparatus and method of claims 1 and 9, wherein the communication module performs locating through at least one of the antenna pattern in the directional region, so as to transmit or track a wireless signal (Friday, par. 80; APs monitor for signals form users and report detection of signals to a server or other central processing site. This allows movement of one or more individuals with a wireless device to be tracked as they move through a store or other environment) see also, figs. 3-6.
Regarding claims 7, the combination of Zamat and Friday teach the apparatus of claim 1; further comprising a storage unit electrically connected to the processing circuit to store all the antenna patterns and the corresponding beacon broadcast information at each of the access points (Friday, fig. 11, par. 143; The user device 1100, includes a processor 1106 and memory 1112 coupled together) see also par. 93 and fig. 10 step 1051.
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Regarding claims 8, the combination of Zamat and Friday teach the apparatus of claim 1, wherein the processing circuit is further connected to the switching control circuit through a serial peripheral interface (Friday, fig. 11, par. 143; The user device 1100, includes a processor 1106 and memory 1112 coupled together via a bus 1109 over which the various elements may interchange data and information), see also Zamat, pars. 28-29 .
Regarding claims 15, the combination of Zamat and Friday teach the method of claim 9, wherein in the step of determining whether the access points have significant relative movements or disappear, when the access points have no significant relative movements or do not disappear, whether one of the access points has a relative movement is further determined (Friday, par. 80; APs monitor for signals form users and report detection of signals to a server or other central processing site. This allows movement of one or more individuals with a wireless device to be tracked as they move through a store or other environment) see also pars. 81 and 163, and if so, the directional region is redetermined based on the prediction model, or if not, at least one of the antenna patterns in the directional region continues to be used for locating. (Friday, figs. 29A-C, par. 40; location determination and machine learning method which can be used to determine the location of a wireless terminal based on reported received signals strengths of beacon signals received by the wireless terminal and which can also be used to refine parameters used in a path loss model used as part of the location determination operation) Examiner notes: applicant is reminded to look at MPEP 2111.04 for contingent limitations
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It is noted that any citations to specific pages, columns, lines or figures in the prior art
references and any interpretation of the reference should not be considered limiting in any way. A
reference is relevant for all it contains and may be relied upon for all that it would have reasonably
suggested to a person of ordinary skill in the art. See MPEP 2123
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kenington (US-20210270926-A1), Three-Dimensional Geolocation System, 2021.
Askar et al. (US-20240040648-A1), Flexible Antenna Port Mapping For Retaining Channel Reciprocity In Full-Duplex Wireless Communication Systems, 2024.
Liu et al. (US-20140146902-A1), Antenna Pattern Matching And Mounting, 2014.
Buel et al. (US-20190150004-A1), Electronic Device With Configurable Antenna-Pattern Group, 2019.
Hoffmann et al. (US-20050063343-A1), Antenna Steering For An Access Point Based Upon Control Frames, 2005.
Swope et al. (US-20150016554-A1), Context Aware Multiple-Input And Multiple-Output Antenna Systems And Methods, 2015.
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Ho et al. (US-10826587-B2), Antenna Diversity For Beacon Broadcasting In Directional Wireless Network, 2020.
Friday et al. (US-7260408-B2), Wireless Node Location Mechanism Using Antenna Pattern Diversity To Enhance Accuracy Of Location Estimates, 2007.
Lien et al. (US-10795009-B2), Digital Beamforming For Radar Sensing Using Wireless Communication Chipset, 2020.
Shu et al. (CN-113972995-A), Distance Determining Method, Device, Electronic Device And Computer Readable Storage Medium, 2022.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO R CAMPERO MIRAMONTES whose telephone number is (571)272-5792. The examiner can normally be reached Monday -Thursday 0600 - 1600.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yuwen (Kevin) Pan can be reached at (571) 272-7855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MRCM/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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