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 .
DETAILED ACTION
This Office Action is in response to the Applicants’ communication filed on 03/03/2026. In virtue of this communication, claims 1-28 are currently pending in the instant application.
Response to Arguments
Applicant’s arguments with respect to claims 1-28 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Kantor does show “For example, parameters from a first read event of the first RFID reader may be normalized based at least on parameters from a second read event of the adjacent RFID reader, and vice versa, to generate scores for each of the two read events. The back-end system may compare the scores to identify which of the two zones may contain the item.” (par. 0016). This is locating items in a volume using RSSI. Please see revised rejection below in view of the amendments made. “In an example, the RFID tag 232 may be adhesively attached to the item 230 or to a container containing the item 230.” (see par. 0028). Further, by determining the location of the RF reader closest to the item, the system determines the location of the tagged container. (i.e. which volume). Still further, par. 0016 shows “both RFID readers may simultaneously, or nearly simultaneously, read the RFID tag as the item leaves the sortation facility through the first exit door. Hence, there may be a risk of inaccurately tracking the location of the item to the adjacent exit door. To mitigate this risk and improve the accuracy, a back-end system may analyze relevant read events of the two RFID readers. For example, parameters from a first read event of the first RFID reader may be normalized based at least on parameters from a second read event of the adjacent RFID reader, and vice versa, to generate scores for each of the two read events. The back-end system may compare the scores to identify which of the two zones may contain the item.” Here, the back end system determines specifically which zone the item is in. Exit door can be equated to rooms. Items are attached to containers and therefore, the claims are still rejected as revised below.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-12, 24-25, 27-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kantor et al. (US 2017/0364720 A1).
Regarding Claim 1 Kantor teaches the limitations "A method of determining a location of an RFID tag, the method comprising: (see abstract, “Techniques for determining an item location based on multiple RFID parameters from multiple read events...”);
obtaining first received signal strength data associated with a first tag volume and relating to signals received from the tag at a first plurality of locations relative to the tag, and second received signal strength data associated with a second tag volume different from the first tag volume and relating to signals received from the tag at a second plurality of locations relative to the tag; (see fig. 3, par. and 0039, where “A first RFID reader may be associated with a first zone 310, may read an RFID tag during a read cycle, and may generate a first read event. Similarly, a second RFID reader may be associated with a second zone 320, may read the RFID tag during a read cycle, and may generate a second read event. The two read events may be generated within a short period of time of each other. For instance, the two read cycles may overlap entirely or partially in time. Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps.”);
determining, depending on the first and second received signal strength data, point of peak data indicative of a location with respect to the first and second tag volumes of peak received signal strength from the tag; (see par. 0039 “Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps.” Here the max signal strength is equated to point of peak and is associated with tag location.);
determining value(s) of one or more further received signal parameters depending on the first and second received signal strength data; and (see par. 0039 “time stamps”);
determining which of the first and second tag volumes the RFID tag is more likely to be located in depending on the point of peak data and on the determined values of the one or more further received signal parameters" (see par. 0042-0043, where “ the analysis may include generating a score per read event based on the respective normalized parameters. For example, normalized parameters corresponding to a zone (or, equivalently, to a RFID reader) may be summed 340. Hence, a first score for the first event may be generated by adding the first normalized count, the first normalized average RSSI, and the first normalized maximum RSSI (the first score is shown with a value of one). Similarly, a second score for the second event may be generated by adding the second normalized count, the second normalized average RSSI, and the second normalized maximum RSSI (the second score is shown with a value of two). Further, the analysis may compare the scores of the read events. Depending on the results of the comparison, a determination may be made as to which of the RFID readers is closest to the item.” Also see par. 0044 where “the analysis may further involve other parameters from the read events. These parameters may include time stamps. For example, the first last time stamp 318 and the second last time stamp 328 may be compared 360 to determine a timing order between the two. If the first last time stamp 318 is the latest of the two, the first RFID reader may be determined as being the closest 352. Otherwise, the second RFID reader may be determined as being the closest 354.” Using the values in addition to RSSI for zone determination).
Wherein the first and second tag volumes comprise: first and second rooms; or first and second containers“ (see par. 0016 “For example, parameters from a first read event of the first RFID reader may be normalized based at least on parameters from a second read event of the adjacent RFID reader, and vice versa, to generate scores for each of the two read events. The back-end system may compare the scores to identify which of the two zones may contain the item.” This is locating items in a volume using RSSI. Also see par. 0028 “In an example, the RFID tag 232 may be adhesively attached to the item 230 or to a container containing the item 230.”
Claims 24-25 and 27-28 are rejected for the same reasons set forth above because the claims have similar limitations or have been addressed.
Regarding Claim 2 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise a plurality of received signal parameters, the said plurality of received signal parameters relating to a plurality of different features of the first received signal strength data and a corresponding plurality of features of the second received signal strength data" (see par. 0039 “Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps. In particular, two read events are generated by two RFID readers, respectively.”).
Regarding Claim 3 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise any two or more of: one or more further received signal parameters selectively based on the first received signal strength data; one or more further received signal parameters selectively based on the second received signal strength data; and one or more further received signal parameters based on the combination of the first and second received signal strength data" (see par. 0039 “Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps. In particular, two read events are generated by two RFID readers, respectively.” Also see par. 0043-0044 “…the analysis may compare the scores of the read events. Depending on the results of the comparison, a determination may be made as to which of the RFID readers is closest to the item.”).
Regarding Claim 4 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more comparative received signal parameters, each of the one or more comparative received signal parameters being based on a respective comparison of the first and second received signal strength data" (see par. 0043-0044 “…the analysis may compare the scores of the read events. Depending on the results of the comparison, a determination may be made as to which of the RFID readers is closest to the item.”).
Regarding Claim 5 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more read count parameters relating to a number of reads of the tag" (par. 0039 “count of times”).
Regarding Claim 6 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more read count parameters relating to a number of reads of the tag per unit time" (see par. 0039 “The two read events may be generated within a short period of time of each other. For instance, the two read cycles may overlap entirely or partially in time. Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps. In particular, two read events are generated by two RFID readers, respectively.”).
Regarding Claim 7 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more average received signal strength parameters relating to an average received signal strength of signals received from the tag" (see par. 0039 “Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI)…”).
Regarding Claim 8 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more parameters relating to a variation of received signal strengths of signals received from the tag" (see par. 0014 “These parameters (e.g., RFID parameters) may include a count of how many times the RFID tag was read (e.g., the number of RF responses), an average received signal strength, the maximum received signal strength, time stamps of the RF response signals, electrical angle, and/or other RF-related parameters.” Here, the subsequent readings of the same tag relate to variation of the RSSI).
Regarding Claim 9 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more signal strength parameters relating to a peak signal strength received from the tag" (see par. 0014 “…the maximum received signal strength…”).
Regarding Claim 10 Kantor teaches the limitations "The method of claim 1 wherein the one or more further received signal parameters comprise one or more parameters relating to a time or location at which a peak received signal strength was received from the tag" (see par. 0014 “…the maximum received signal strength, time stamps of the RF response signals…”).
Regarding Claim 11 Kantor teaches the limitations "The method of claim 1 wherein the first and second received signal strength data is based on time stamped received signal strength data relating to signals received from the tag at the said first and second pluralities of locations relative to the tag, and wherein the point of peak data comprises data relating to a time at which a signal having the peak received signal strength of the first and second received signal strength data was received" (see par. 0014 “the read event may identify the item from the information stored in the RF tag and may include multiple RFID parameters measured during the reading cycle (generally referred to herein as “parameters”). These parameters (e.g., RFID parameters) may include a count of how many times the RFID tag was read (e.g., the number of RF responses), an average received signal strength, the maximum received signal strength, time stamps of the RF response signals, electrical angle, and/or other RF-related parameters.” The maximum signal strength is time stamped);
Regarding Claim 12 Kantor teaches the limitations "The method of claim 1 wherein determining which of the first and second tag volumes the RFID tag is more likely to be located in comprises
causing a comparison of input data to predetermined reference data, the input data being based on the point of peak data and the determined value(s) of the one or more further received signal parameters" (see par. 0064 “At operation 608, the computer system may determine whether the first score and a second score generated for the second event fall within a threshold amount of each other. The second score may be similarly generated for the second read event based on the parameters of the second read event. The threshold amount may be a predefined deviation. If the difference between the two scores is too large (e.g., exceeding the threshold amount), operation 610 may be performed, where the computer system may rely on one of the two scores to make the location determination. Otherwise, operation 612 may be performed, where the computer system may rely on additional parameters to make the location determination.”).
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 13-23, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Kantor et al. (US 2017/0364720 A1) in view of Tang et al. (US 10,586,082 B1).
Regarding Claim 17 Kantor teaches the limitations "A computer-implemented method of
However, Kantor does not explicitly disclose the limitation “training a machine learning model for determining a location of an RFID tag, generating a training data set by, for each of the tags and training the machine learning model based on the training data set.”
In the same field of endeavor Tang discloses an advanced system for micro-location of RFID tags in spatial environments, where “RFID tag localization includes utilizing a machine learning model that takes as its inputs key features of the received signal and the location of the receiving RFID antenna 50 and maps out possible locations of the RFID tags 40 within the 3D space 30. For example, using deep learning techniques, the machine learning model may be fed the data from the RFID antennas 50 as well as the real position of the RFID tags 40 in order to effectively train the machine learning model. The machine learning model learns to recognize key patterns in the data such as the correlation between peak RSI and/or change in Doppler shift as the mobile platform 20 moves past the RFID tag 40, allowing it to estimate the location of the RFID tag 40.” (see abstract, fig. 1-3, 11 and col. 6 lines 37-49 and col. 6 line 62-col. 7 line 16)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to train a machine learning model based on RFID tag training data set to locate tags as taught by Tang in the system of Kantor, in order to narrow down the space for locating tags, and efficiently determine location without user manual input (see col. 6 line 62-col. 7 line 16).
Claims 22 and 26 are rejected for the same reasons set forth above because the claims have similar limitations or have been addressed.
Regarding Claim 13 Kantor teaches the limitations "The method of claim 1 wherein determining which of the first and second tag volumes the RFID tag is more likely to be located in comprises:
causing input data to be input
Tang discloses an advanced system for micro-location of RFID tags in spatial environments, where “RFID tag localization includes utilizing a machine learning model that takes as its inputs key features of the received signal and the location of the receiving RFID antenna 50 and maps out possible locations of the RFID tags 40 within the 3D space 30. For example, using deep learning techniques, the machine learning model may be fed the data from the RFID antennas 50 as well as the real position of the RFID tags 40 in order to effectively train the machine learning model. The machine learning model learns to recognize key patterns in the data such as the correlation between peak RSI and/or change in Doppler shift as the mobile platform 20 moves past the RFID tag 40, allowing it to estimate the location of the RFID tag 40.” (see abstract, fig. 1-3, 11 and col. 6 lines 37-49 and col. 6 line 62-col. 7 line 16)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to train a machine learning model based on RFID tag training data set to locate tags as taught by Tang in the system of Kantor, in order to narrow down the space for locating tags, and efficiently determine location without user manual input (see col. 6 line 62-col. 7 line 16).
Regarding Claim 14 Kantor and Tang teach the limitations "The method of claim 13 further comprising obtaining from the trained machine learning model a probability that the RFID tag is located in the said tag volume of the first and second tag volume” (see Tang col. 9 lines 32-54 “his can be used to provide a probability that a specific RFID tag is in a particular 3D space.”). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to obtain a probability of RFID tag location as taught by Tang in the system of Kantor, in order to efficiently determine location without user manual input (see col. 6 line 62-col. 7 line 16).
Regarding Claim 15 Kantor teaches the limitations "The method of claim 13 wherein the machine learning model is trained based on training data at least some of which is associated with tag volumes different from the first and second tag volumes" (see Tang fig. 2, col. 5 lines 25-33 and col. 6 lines 37-45, showing RFID tags sending RFID data to reader from different locations or volumes which is used to train model). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to train the model based tag data from different locations as taught by Tang in the system of Kantor, in order to efficiently determine location (see col. 6 line 62-col. 7 line 16).
Regarding Claim 16 Kantor and Tang teach the limitations "The method of claim 13 wherein the machine learning model is trained based on one or more training data sets, (see Tang col. 6 lines 37-45) but
each of the one or more training data sets being based on a known one of first and second training tag volumes in which an RFID tag is located, (see col. 3 line 63-col. 4 line 3);
point of peak data indicative of a location with respect to the first and second training tag volumes of peak received signal strength from the RFID tag and training value(s) of the one or more further received signal parameters" (see Kantor par. 0039 “Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use a machine learning model using known locations as training data as taught by Tang in the system of Kantor which uses maximum signal strength and time stamps and use that data to train Tang’s model, in order to efficiently determine location without user manual input (see col. 6 line 62-col. 7 line 16 of Tang).
Claims 18 and 23 are rejected for the same reasons set forth above in claim 2 because the claims have similar limitations and have been addressed.
Claim 19 is rejected for the same reasons set forth above in claim 3 because the claims have similar limitations and have been addressed.
Claim 20 is rejected for the same reasons set forth above in claim 4 because the claims have similar limitations and have been addressed.
Regarding Claim 21 Kantor and Tang teach the limitations "The computer-implemented method of claim 17 further comprising:
obtaining third received signal strength data associated with a third tag volume and relating to signals received from one or more RFID tags at a third plurality of locations relative to the tag(s), and fourth received signal strength data associated with a fourth tag volume different from the third tag volume and relating to signals received from one or more RFID tags at a fourth plurality of locations relative to the tag(s); (see Kantor par. 0020 “The RFID reader 120 may generate and send read events for items progressing through the zone 122 to the back-end system 130” (i.e. receiving multiple read events from tags at different locations” and par. 0045 “In addition, the analysis may be repeated for more than two read events and/or for more than two RFID readers, when such events fall within a predefined period of time from each other (e.g., by overlapping partially or fully).”);
fourth received signal strength data, and identifying a respective known one of the third and fourth tag volumes in which the respective RFID tag is located; 310, may read an RFID tag during a read cycle, and may generate a first read event. Similarly, a second RFID reader may be associated with a second zone 320, may read the RFID tag during a read cycle, and may generate a second read event. The two read events may be generated within a short period of time of each other. For instance, the two read cycles may overlap entirely or partially in time. Each of the read events may include measured values for four parameters: count of times, average signal strength (e.g., RSSI), maximum signal strength, and time stamps” and par. 0038 “multiple parameters across multiple read events are analyzed to mitigate the risk of cross-read and more accurately determine the location of the item.”);
Tang discloses “RFID tag localization includes utilizing a machine learning model that takes as its inputs key features of the received signal and the location of the receiving RFID antenna 50 and maps out possible locations of the RFID tags 40 within the 3D space 30. For example, using deep learning techniques, the machine learning model may be fed the data from the RFID antennas 50 as well as the real position of the RFID tags 40 in order to effectively train the machine learning model. The machine learning model learns to recognize key patterns in the data such as the correlation between peak RSI and/or change in Doppler shift as the mobile platform 20 moves past the RFID tag 40, allowing it to estimate the location of the RFID tag 40.” (see abstract, fig. 1-3, 11 and col. 6 lines 37-49 and col. 6 line 62-col. 7 line 16)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to train a machine learning model based on RFID tag training data from 1st, 2nd, 3rd, and 4th readings set to locate tags as taught by the modified system of Tang and Kantor, in order to narrow down the space for locating tags, and efficiently determine location without user manual input (see col. 6 line 62-col. 7 line 16).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID BILODEAU whose telephone number is (571)270-3192. The examiner can normally be reached Monday-Thursday 6:00am-4:00pm Eastern Standard Time.
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, Wesley Kim can be reached at (571) 272-7867. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/David Bilodeau/
Primary Examiner, Art Unit 2648