DETAILED ACTION
This office action is a response to the application filed on 11/4/2024. Claims 1-20 are pending and ready for examination.
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 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.
Claims 1-7, 10-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Marzban et al. (US 2024/0089769; provided in Applicant’s IDS dated 3/3/2026, hereinafter Marzban) in view of Marzban et al. (US 2024/0340125, hereinafter Marzban_125).
Regarding claim 1, Marzban discloses a wireless transmit/receive unit (WTRU) comprising: a processor, wherein the processor is configured to [Marzban Figure 3 discloses a base station 310 and a UE 350 comprising processors, memory, etc. (Marzban Figure 3, paragraphs 0059-0068. Also see Marzban Figure 15, paragraphs 0137-0138)]:
Receive configuration information for interference prediction, wherein the configuration information comprises an indication of a first set of reference signal (RS) resources and a second set of RS resources, wherein the first set of RS resources is for interference measurement, and wherein the second set of RS resources is for interference prediction [Marzban discloses that the network entity (e.g. a base station) may transmit configuration for reporting interference. The network entity may transmit a set of interference measurement reference signals on the set of interference measurement resources (Marzban Figure 8, paragraph 0080). In some aspects, the network may configure the UE with two groups of interference measurement resources. The NW may configure the UE to process the measurements in the first group of reference signals (Marzban paragraph 0081). The network may configure the UE to process the second group of reference signals to generate the ground-truth output of the ML model (Marzban paragraph 0082)];
Determine interference measurements for one or more RSs indicated by the first set of RS resources [Marzban discloses that the UE may measure interference on each interference measurement resource of the set of interference measurement reference resources to obtain the interference measurement information (Marzban Figure 8, paragraph 0080)];
Determine an interference prediction for a RS resource in the second set of RS resources based on the interference measurements [Marzban discloses that the set of interference measurement resources may include a first set of interference measurement resources associated with inference measurements for inputs to the ML-based interference prediction algorithm, and a second set of interference measurement resources associated with interference measurements for predicted interference outputs of the ML-based interference prediction algorithm (Marzban paragraph 0099)].
Marzban does not expressly disclose the features of determining an interference prediction for a RS resource in the second set of RS resources based on the interference measurements; and sending an interference prediction report, the interference prediction report indicating the interference prediction.
However, in the same or similar field of invention, Marzban_125 discloses that a network entity may transmit a message indicating a set of multiple resources for which the UE may predict interference information (Marzban_125 paragraph 0158). The UE may predict interference information for the set of multiple resources based on the indication in the control message (Marzban_125 paragraph 0159). The UE may compress the predicted interference information using a codebook selected based on the set of multiple resources associated with the interference information. In some examples, the codebook may include one or more codewords that represents the set of multiple resources selected to predict interference information. Further, the UE may transmit a report to reduce overhead for the wireless system and may efficiently transmit predicted interference information to the network entity (Marzban_125 paragraphs 0160 and 0161).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Marzban and Marzban_125 to have the features of determining an interference prediction for a RS resource in the second set of RS resources based on the interference measurements; and sending an interference prediction report, the interference prediction report indicating the interference prediction. The suggestion/motivation would have been to provide a method for efficiently transmitted predicted interference information and reduce overhead (Marzban_125 paragraphs 0161 and 0163).
Regarding claim 2, Marzban and Marzban_125 disclose the WTRU of claim 1. Marzban and Marzban_125 further disclose wherein the processor is configured to determine the interference prediction using an artificial intelligence or machine learning (AIML) model, wherein the interference measurements are used as input to the AIML model [Marzban_125 discloses that the communication system may use an AI or ML approach for interference prediction algorithms (Marzban_125 paragraphs 0125-0126)]. In addition, the same motivation is used as the rejection of claim 1.
Regarding claim 3, Marzban and Marzban_125 disclose the WTRU of claim 1. Marzban and Marzban_125 further disclose wherein the interference measurements are based on a reference signal received power (RSRP) measurement, a received signal strength indicator (RSSI) measurement, or a signal to noise and interference ratio (SINR) measurement [Marzban discloses that the network may configure the UE to process the measurements in the first group of reference signals to generate the interference characteristics inputs of the ML model. For example, the interference characteristics inputs may include, but are not limited to, interference power, interference-plus-noise power, signal to interference plus noise ratio (SINR), CSI estimations (e.g., CQI, PMI, RI, etc.) on a specific set of resources, and/or a processed version of reference signal measurements (Marzban paragraph 0081)]. In addition, the same motivation is used as the rejection of claim 1.
Regarding claim 4, Marzban and Marzban_125 disclose the WTRU of claim 1. Marzban and Marzban_125 further disclose wherein the one or more RSs indicated by the first set of RS resources are channel state information RSs for interference (CSI-IMs) or non-zero power channel state information RSs (NZP CSI-RSs) for interference [Marzban discloses that the network may configure the UE with interference measurement resources such as CSI-RS, CSI-IM, IMR (Marzban paragraph 0081)]. In addition, the same motivation is used as the rejection of claim 1.
Regarding claim 5, Marzban and Marzban_125 disclose the WTRU of claim 1. Marzban and Marzban_125 further disclose wherein the processor is configured to determine an interference prediction for each RS resource in the second set of RS resources [Marzban_125 discloses that a network entity may transmit a message indicating a set of multiple resources for which the UE may predict interference information (Marzban_125 paragraph 0158). The UE may predict interference information for the set of multiple resources based on the indication in the control message (Marzban_125 paragraph 0159); indicating that the interference prediction is determined for each RS resource in the resource set]. In addition, the same motivation is used as the rejection of claim 1.
Regarding claim 6, Marzban and Marzban_125 disclose the WTRU of claim 5. Marzban and Marzban_125 further disclose wherein the interference prediction report includes all of the interference predictions determined for each RS resource in the second set of RS resources [Marzban_125 discloses that the UE may predict interference information for the set of multiple resources based on the indication in the control message (Marzban_125 paragraph 0159). The UE may compress the predicted interference information using a codebook selected based on the set of multiple resources associated with the interference information. Further, the UE may transmit a report to reduce overhead for the wireless system and may efficiently transmit predicted interference information to the network entity (Marzban_125 paragraphs 0160 and 0161); indicating that the report includes all of the interference predictions for each RS resource in the set]. In addition, the same motivation is used as the rejection of claim 5.
Regarding claim 7, Marzban and Marzban_125 disclose the WTRU of claim 5. Marzban and Marzban_125 further disclose wherein the interference prediction report includes a subset of the interference predictions determined for each RS resource in the second set of RS resources [Marzban_125 discloses that the network entity may configure the UE with a set of L resources for which the UE may predict interference information and report the interference information (Marzban_125 paragraphs 0148 and 0149)]. In addition, the same motivation is used as the rejection of claim 5.
Regarding claim 10, Marzban and Marzban_125 disclose the WTRU of claim 1. Marzban and Marzban_125 further disclose wherein the interference prediction is in decibels (dB) [Marzban_125 discloses that the system may integrate the ML or AI-based model to output probabilistic interference predictions. That is, the ML or AI-based model may quantize the interference and noise power into ordered classes with a step in decibel milliwatts (dBm) starting from a start point in dBm and ending at an end point in dBm (Marzban_125 paragraph 0126)]. In addition, the same motivation is used as the rejection of claim 1.
Regarding claim 11, Marzban discloses a method for use by a wireless transmit/receive unit (WTRU), the method comprising: receiving configuration information for interference prediction, wherein the configuration information comprises an indication of a first set of reference signal (RS) resources and a second set of RS resources, wherein the first set of RS resources is for interference measurement, and wherein the second set of RS resources is for interference prediction [Marzban discloses that the network entity (e.g. a base station) may transmit configuration for reporting interference. The network entity may transmit a set of interference measurement reference signals on the set of interference measurement resources (Marzban Figure 8, paragraph 0080). In some aspects, the network may configure the UE with two groups of interference measurement resources. The NW may configure the UE to process the measurements in the first group of reference signals (Marzban paragraph 0081). The network may configure the UE to process the second group of reference signals to generate the ground-truth output of the ML model (Marzban paragraph 0082)];
Determining interference measurements for one or more RSs indicated by the first set of RS resources [Marzban discloses that the UE may measure interference on each interference measurement resource of the set of interference measurement reference resources to obtain the interference measurement information (Marzban Figure 8, paragraph 0080)];
Determining an interference prediction for a RS resource in the second set of RS resources based on the interference measurements [Marzban discloses that the set of interference measurement resources may include a first set of interference measurement resources associated with inference measurements for inputs to the ML-based interference prediction algorithm, and a second set of interference measurement resources associated with interference measurements for predicted interference outputs of the ML-based interference prediction algorithm (Marzban paragraph 0099)].
Marzban does not expressly disclose the features of determining an interference prediction for a RS resource in the second set of RS resources based on the interference measurements; and sending an interference prediction report, the interference prediction report indicating the interference prediction.
However, in the same or similar field of invention, Marzban_125 discloses that a network entity may transmit a message indicating a set of multiple resources for which the UE may predict interference information (Marzban_125 paragraph 0158). The UE may predict interference information for the set of multiple resources based on the indication in the control message (Marzban_125 paragraph 0159). The UE may compress the predicted interference information using a codebook selected based on the set of multiple resources associated with the interference information. In some examples, the codebook may include one or more codewords that represents the set of multiple resources selected to predict interference information. Further, the UE may transmit a report to reduce overhead for the wireless system and may efficiently transmit predicted interference information to the network entity (Marzban_125 paragraphs 0160 and 0161).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Marzban and Marzban_125 to have the features of determining an interference prediction for a RS resource in the second set of RS resources based on the interference measurements; and sending an interference prediction report, the interference prediction report indicating the interference prediction. The suggestion/motivation would have been to provide a method for efficiently transmitted predicted interference information and reduce overhead (Marzban_125 paragraphs 0161 and 0163).
Regarding claim 12, Marzban and Marzban_125 disclose the method of claim 11. Marzban and Marzban_125 further disclose regarding determining the interference prediction using an artificial intelligence or machine learning (AIML) model, wherein the interference measurements are used as input to the AIML model [Marzban_125 discloses that the communication system may use an AI or ML approach for interference prediction algorithms (Marzban_125 paragraphs 0125-0126)]. In addition, the same motivation is used as the rejection of claim 11.
Regarding claim 13, Marzban and Marzban_125 disclose the method of claim 11. Marzban and Marzban_125 further disclose wherein the interference measurements are based on a reference signal received power (RSRP) measurement, a received signal strength indicator (RSSI) measurement, or a signal to noise and interference ratio (SINR) measurement [Marzban discloses that the network may configure the UE to process the measurements in the first group of reference signals to generate the interference characteristics inputs of the ML model. For example, the interference characteristics inputs may include, but are not limited to, interference power, interference-plus-noise power, signal to interference plus noise ratio (SINR), CSI estimations (e.g., CQI, PMI, RI, etc.) on a specific set of resources, and/or a processed version of reference signal measurements (Marzban paragraph 0081)]. In addition, the same motivation is used as the rejection of claim 11.
Regarding claim 14, Marzban and Marzban_125 disclose the method of claim 11. Marzban and Marzban_125 further disclose wherein the one or more RSs indicated by the first set of RS resources are channel state information RSs for interference (CSI-IMs) or non-zero power channel state information RSs (NZP CSI-RSs) for interference [Marzban discloses that the network may configure the UE with interference measurement resources such as CSI-RS, CSI-IM, IMR (Marzban paragraph 0081)]. In addition, the same motivation is used as the rejection of claim 11.
Regarding claim 15, Marzban and Marzban_125 disclose the method of claim 11. Marzban and Marzban_125 further disclose regarding determining an interference prediction for each RS resource in the second set of RS resources [Marzban_125 discloses that a network entity may transmit a message indicating a set of multiple resources for which the UE may predict interference information (Marzban_125 paragraph 0158). The UE may predict interference information for the set of multiple resources based on the indication in the control message (Marzban_125 paragraph 0159); indicating that the interference prediction is determined for each RS resource in the resource set]. In addition, the same motivation is used as the rejection of claim 11.
Regarding claim 16, Marzban and Marzban_125 disclose the method of claim 15. Marzban and Marzban_125 further disclose wherein the interference prediction report includes all of the interference predictions determined for each RS resource in the second set of RS resources [Marzban_125 discloses that the UE may predict interference information for the set of multiple resources based on the indication in the control message (Marzban_125 paragraph 0159). The UE may compress the predicted interference information using a codebook selected based on the set of multiple resources associated with the interference information. Further, the UE may transmit a report to reduce overhead for the wireless system and may efficiently transmit predicted interference information to the network entity (Marzban_125 paragraphs 0160 and 0161); indicating that the report includes all of the interference predictions for each RS resource in the set]. In addition, the same motivation is used as the rejection of claim 15.
Regarding claim 17, Marzban and Marzban_125 disclose the method of claim 15. Marzban and Marzban_125 further disclose wherein the interference prediction report includes a subset of the interference predictions determined for each RS resource in the second set of RS resources [Marzban_125 discloses that the network entity may configure the UE with a set of L resources for which the UE may predict interference information and report the interference information (Marzban_125 paragraphs 0148 and 0149)]. In addition, the same motivation is used as the rejection of claim 15.
Regarding claim 20, Marzban and Marzban_125 disclose the method of claim 11. Marzban and Marzban_125 further disclose wherein the interference prediction is in decibels (dB) [Marzban_125 discloses that the system may integrate the ML or AI-based model to output probabilistic interference predictions. That is, the ML or AI-based model may quantize the interference and noise power into ordered classes with a step in decibel milliwatts (dBm) starting from a start point in dBm and ending at an end point in dBm (Marzban_125 paragraph 0126)]. In addition, the same motivation is used as the rejection of claim 11.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Marzban in view of Marzban_125, and further in view of Marzban et al. (US 2026/0067014, hereinafter Marzban_014).
Regarding claim 8, Marzban and Marzban_125 disclose the WTRU of claim 7. Marzban and Marzban_125 further disclose wherein the subset of the interference predictions included in the interference prediction report comprises a quantity, L, of interference predictions [Marzban_125 discloses that the network entity may configure the UE with a set of L resources for which the UE may predict interference information and report the interference information (Marzban_125 paragraphs 0148 and 0149)].
Marzban and Marzban_125 do not expressly disclose wherein the interference predictions comprised in the report are the best L interference predictions or the worst L interference predictions.
However, in the same or similar field of invention, Marzban_014 discloses that the reporting of predicted interference may be done on a subset of resources such that the reporting of predicted interference may account for instances of high interference and/or low interference (i.e. best or worst interference predictions) (Marzban_014 paragraphs 0031 and 0032).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Marzban, Marzban_125 and Marzban_014 to have the features of wherein the interference predictions comprised in the report are the best L interference predictions or the worst L interference predictions. The suggestion/motivation would have been to improve signal quality and efficient usage of network resources (Marzban_014 paragraph 0031).
Regarding claim 9, Marzban and Marzban_125 disclose the WTRU of claim 7. Marzban and Marzban_125 do not expressly disclose wherein the processor is configured to determine the subset of interference predictions to be included in the interference prediction report based on a comparison of the interference predictions determined for each RS resource in the second set of RS resources with a threshold value for reporting.
However, in the same or similar field of invention, Marzban_014 discloses that the UE may transmit predicted interference information for a subset of resources. The UE may be configured to report predicted interference information where interference prediction values satisfy a threshold and/or predicted interference variations satisfy a threshold when compared to the last reported interference information (Marzban_014 paragraph 0115).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Marzban, Marzban_125 and Marzban_014 to have the features of wherein the processor is configured to determine the subset of interference predictions to be included in the interference prediction report based on a comparison of the interference predictions determined for each RS resource in the second set of RS resources with a threshold value for reporting. The suggestion/motivation would have been to improve signal quality and efficient usage of network resources (Marzban_014 paragraph 0031).
Regarding claim 18, Marzban and Marzban_125 disclose the method of claim 17. Marzban and Marzban_125 further disclose wherein the subset of the interference predictions included in the interference prediction report comprises a quantity, L, of interference predictions [Marzban_125 discloses that the network entity may configure the UE with a set of L resources for which the UE may predict interference information and report the interference information (Marzban_125 paragraphs 0148 and 0149)].
Marzban and Marzban_125 do not expressly disclose wherein the L interference predictions comprised in the interference prediction report are the best L interference predictions or the worst L interference predictions.
However, in the same or similar field of invention, Marzban_014 discloses that the reporting of predicted interference may be done on a subset of resources such that the reporting of predicted interference may account for instances of high interference and/or low interference (i.e. best or worst interference predictions) (Marzban_014 paragraphs 0031 and 0032).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Marzban, Marzban_125 and Marzban_014 to have the features of wherein the L interference predictions comprised in the interference prediction report are the best L interference predictions or the worst L interference predictions. The suggestion/motivation would have been to improve signal quality and efficient usage of network resources (Marzban_014 paragraph 0031).
Regarding claim 19, Marzban and Marzban_125 disclose the method of claim 17. Marzban and Marzban_125 do not expressly disclose the features of determining the subset of interference predictions to be included in the interference prediction report based on a comparison of the interference predictions determined for each RS resource in the second set of RS resources with a threshold value for reporting.
However, in the same or similar field of invention, Marzban_014 discloses that the UE may transmit predicted interference information for a subset of resources. The UE may be configured to report predicted interference information where interference prediction values satisfy a threshold and/or predicted interference variations satisfy a threshold when compared to the last reported interference information (Marzban_014 paragraph 0115).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Marzban, Marzban_125 and Marzban_014 to have the features of determining the subset of interference predictions to be included in the interference prediction report based on a comparison of the interference predictions determined for each RS resource in the second set of RS resources with a threshold value for reporting. The suggestion/motivation would have been to improve signal quality and efficient usage of network resources (Marzban_014 paragraph 0031).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAUMIT SHAH whose telephone number is (571)272-6959. The examiner can normally be reached Monday - Friday 9 am - 6 pm.
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, EDAN ORGAD can be reached at (571) 272-7884. 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.
/SAUMIT SHAH/Primary Examiner, Art Unit 2414
Prosecution Insights