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 § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 - 4, 6, 7, and 13 - 16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pazeshki, U.S. Patent Publication No. 2021/0336683.
Pazeshki teaches:
1. (Original) A method for detecting an artificial intelligence (Al) prediction beam (currently, AI is defined simply as software capable of learning and decision making (see dictionary.com), any generic computing device which runs appropriate software is capable at some level of learning and decision making, the term AI in and of itself does not add any patentability to any claims, additionally, the cited patent discusses machine learning which is considered functionally equivalent to AI), comprising:
receiving, by a first communication node, beam detection configuration information sent by a second communication node (a network entity, such as a gNB (first communication node) receives information of a UE (second communication node) on location and orientation, [0059]);
performing, by the first communication node, Al prediction beam quality detection according to the beam detection configuration information to obtain a beam quality value (network entity predicts best beams based on UE location and orientation, [0059]); and
re-determining, by the first communication node, a target beam in a case where the first communication node determines that the beam quality value does not meet a beam quality requirement (network entity receives reports to determine if a mismatch occurs between best beam and predicted best beam, [0072]).
2. (Original) The method of claim 1, wherein performing, by the first communication node, the Al prediction beam quality detection according to the beam detection configuration information comprises:
performing, by the first communication node, beam quality detection on a to-be-detected beam in the beam detection configuration information (network entity predicts best beams based on UE location and orientation, [0059]);
wherein the beam detection configuration information comprises a pattern for beam detection, and the pattern comprises the to-be-detected beam and time of the to-be-detected beam (network entity predicts best beams based on UE location and orientation, [0059]);
the to-be-detected beam is associated with an Al prediction beam of the second communication node (network entity predicts best beams based on UE location and orientation, [0059]).
3. (Original) The method of claim 1, wherein performing, by the first communication node, the Al prediction beam quality detection according to the beam detection configuration information comprises:
receiving, by the first communication node, an Al prediction beam set sent by the second communication node (network entity predicts best beams based on UE location and orientation, [0059]); and
detecting, by the first communication node, a beam in the Al prediction beam set according to the beam detection configuration information (network entity predicts best beams based on UE location and orientation, [0059]);
wherein the beam detection configuration information comprises one of:
a detection cycle;
an indication identifier configured to indicate a measurement mode;
an indication bit configured to select a pattern of a to-be-measured beam; or
an AI detection mode (network entity predicts best beams based on UE location and orientation, [0059]).
4. (Original) The method of claim 1, wherein determining, by the first communication node, that the beam quality value does not meet the beam quality requirement comprises:
determining, by the first communication node, one AI prediction beam quality detection failure in a case of determining that the beam quality value is smaller than a beam quality threshold (beams are reported that diverge from a predicted measurement by more than a threshold amount, [0076]); and
determining, by the first communication node, that the beam quality value does not meet the beam quality requirement in a case of determining that a distribution of a number of AI prediction beam quality detection failures satisfies a detection termination condition (beams are reported that diverge from a predicted measurement by more than a threshold amount, [0076]).
6. (Original) The method of claim 1, wherein re-determining, by the first communication node, the target beam comprises:
determining, by the first communication node, a first new beam as the target beam;
wherein the first new beam comprises one of the following:
an adjacent beam of a current beam; a beam indicated by media access control control element (MAC CE) signaling (MAC-CE signaling utilized, [0092]); or
a beam within a time window indicated by higher-layer signaling.
7. (Original) The method of claim 6, wherein re-determining, by the first communication node, the target beam comprises:
determining, by the first communication node, a second new beam as the target beam in a case where a beam quality value of the first new beam does not meet the beam quality requirement (predicted beams are measured as well as other possible selected beams, [0085] – [0087]);
wherein the second new beam comprises a default beam configured by the second communication node (predicted beams are measured as well as other possible selected beams, [0085] – [0087]).
13. (Currently Amended) A communication node, comprising a processor which, when executing a computer program, implements the following:
receiving beam detection configuration information sent by a second communication node (a network entity, such as a gNB (first communication node) receives information of a UE (second communication node) on location and orientation, [0059]);
performing artificial intelligence (AI) prediction beam quality detection according to the beam detection configuration information to obtain a beam quality value (network entity predicts best beams based on UE location and orientation, [0059]); and
re-determining a target beam in a case where the communication node determines that the beam quality value does not meet a beam quality requirement (predicted beams are measured as well as other possible selected beams, [0085] – [0087]).
14. (Currently Amended) A non-transitory readable and writeable storage medium storing a computer program which, when executed by a processor, implements the following:
receiving beam detection configuration information sent by a second communication node (a network entity, such as a gNB (first communication node) receives information of a UE (second communication node) on location and orientation, [0059]);
performing artificial intelligence (AI) prediction beam quality detection according to the beam detection configuration information to obtain a beam quality value (network entity predicts best beams based on UE location and orientation, [0059]); and
re-determining a target beam in a case where a first communication node determines that the beam quality value does not meet a beam quality requirement (predicted beams are measured as well as other possible selected beams, [0085] – [0087]).
15. (New) The communication node of claim 13, wherein the processor, when executing the computer program, implements performing the AI prediction beam quality detection according to the beam detection configuration information by:
performing beam quality detection on a to-be-detected beam in the beam detection configuration information a network entity, such as a gNB (first communication node) receives information of a UE (second communication node) on location and orientation, [0059]);
wherein the beam detection configuration information comprises a pattern for beam detection, and the pattern comprises the to-be-detected beam and time of the to-be-detected beam (network entity predicts best beams based on UE location and orientation, [0059]);
the to-be-detected beam is associated with an AI prediction beam of the second communication node (network entity predicts best beams based on UE location and orientation, [0059]).
16. (New) The communication node of claim 13, wherein the processor, when executing the computer program, implements performing the AI prediction beam quality detection according to the beam detection configuration information by:
receiving an AI prediction beam set sent by the second communication node (network entity predicts best beams based on UE location and orientation, [0059]); and
detecting a beam in the AI prediction beam set according to the beam detection configuration information (network entity predicts best beams based on UE location and orientation, [0059]);
wherein the beam detection configuration information comprises one of:
a detection cycle (network entity predicts best beams based on UE location and orientation, [0059]);
an indication identifier configured to indicate a measurement mode;
an indication bit configured to select a pattern of a to-be-measured beam; or
an AI detection mode.
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.
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.
Claim(s) 5, 8 - 12, and 17 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pazeshki as applied to claims 1 and 13 above, and further in view of Ryden, U.S. Patent Publication No. 2022/0217556.
Pazeshki teaches a method of machine learning to determine the best transmission beam. Pazeshki does not teach switching to a different base station due to carrier failure. However, Ryden teaches switching to a new station due to carrier failure. It would have been obvious to one skilled in the art at the time of filing to modify the teachings of Pazeshki to incorporate the known technique of switching stations when carriers fail in order to obtain the predictable result of less dropped calls.
The combination teaches:
5. (Original) The method of claim 4, wherein determining, by the first communication node, that the distribution of the number of detection failures satisfies the detection termination condition comprises:
in a case where a detection time threshold is a time interval between two AI prediction beam quality detection failures, in response to the first communication node determining the one AI prediction beam quality detection failure, re-timing and recording accumulatively the number of AI prediction beam quality detection failures (beams are reported that diverge from a predicted measurement by more than a threshold amount, [0076], Pazeshki), or in response to the first communication node determining one AI prediction beam quality detection success, clearing the accumulatively recorded number of AI prediction beam quality detection failures and timing time; and
in response to the first communication node determining that the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold and the timing time does not exceed the detection time threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden);
in a case where a detection time threshold is a time interval between two AI prediction beam quality detection failures, in response to the first communication node determining the one AI prediction beam quality detection failure, recording accumulatively the number of AI prediction beam quality detection failures and re-timing, or in response to the first communication node determining one AI prediction beam quality detection success, not recording the number of AI prediction beam quality detection failures and continuing timing (beam quality may trigger beam failure detection parameters, Ryden); and
in response to the first communication node determining that timing time reaches the detection time threshold and the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden);
in a case where a detection time threshold is a length of one AI prediction beam quality detection, in response to the first communication node determining the one AI prediction beam quality detection failure, recording accumulatively the number of AI prediction beam quality detection failures and continuing timing (beam quality may trigger beam failure detection parameters, Ryden), or in response to the first communication node determining one AI prediction beam quality detection success, clearing the accumulatively recorded number of number of AI prediction beam quality detection failures and timing time; and
in response to the first communication node determining that the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold and the timing time does not exceed the detection time threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden); or
in a case where a detection time threshold is a length of one AI prediction beam quality detection, in response to the first communication node determining the one AI prediction beam quality detection failure, recording accumulatively the number of AI prediction beam quality detection failures and continuing timing (beam quality may trigger beam failure detection parameters, Ryden), or in response to the first communication node determining one AI prediction beam quality detection success, not recording the accumulatively recorded number of AI prediction beam quality detection failures and continuing timing; and
in response to the first communication node determining that timing time reaches the detection time threshold and the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden, 2022);
wherein the one AI prediction beam quality detection success comprises the first communication node determining that the beam quality value is greater than or equal to the beam quality threshold (beam quality may trigger beam failure detection parameters, Ryden).
8. (Currently Amended) The method of claim 1, wherein after re-determining, by the first communication node, the target beam, the method further comprises:
reporting, by the first communication node, an AI mechanism exit request message to the second communication node and receiving a response message fed back by the second communication node according to the AI mechanism exit request message (beam quality may trigger beam failure detection parameters, Ryden); and
performing, by the first communication node, beam switching according to the response message (beam quality may trigger beam failure detection parameters, Ryden).
9. (Original) The method of claim 8, wherein reporting, by the first communication node, the AI mechanism exit request message to the second communication node comprises:
reporting, by the first communication node, the AI mechanism exit request message to the second communication node in a case where the first communication node determines that a time point whose beam quality value does not meet the beam quality requirement belongs to a reporting time range (beam quality may trigger beam failure detection parameters, Ryden).
10. (Original) The method of claim 8, wherein reporting, by the first communication node, the AI mechanism exit request message to the second communication node comprises:
reporting, by the first communication node, the AI mechanism exit request message to the second communication node in a case where the first communication node determines for a first time that the beam quality value is smaller than a beam quality threshold (beam quality may trigger beam failure detection parameters, Ryden).
11. (Original) The method of claim 8, wherein the AI mechanism exit request message comprises at least one of:
an index of the switched target beam;
a current movement speed of the first communication node (network entity predicts best beams based on UE location and orientation, [0059], Pazeshki); or
detection failure time.
12. (Original) The method of claim 8, wherein the response message comprises one of the following:
reconfigured beam information, wherein the reconfigured beam information comprises a beam index and switching time corresponding to the beam index;
a scaling factor of a pattern of the AI prediction beam and start time of the pattern of the AI prediction beam; or
default beam information (network entity predicts best beams based on UE location and orientation, [0059], Pazeshki).
17. (New) The communication node of claim 13, wherein the processor, when executing the computer program, implements determining that the beam quality value does not meet the beam quality requirement by:
determining one AI prediction beam quality detection failure in a case of determining that the beam quality value is smaller than a beam quality threshold (beam quality may trigger beam failure detection parameters, Ryden); and
determining that the beam quality value does not meet the beam quality requirement in a case of determining that a distribution of a number of AI prediction beam quality detection failures satisfies a detection termination condition (beam quality may trigger beam failure detection parameters, Ryden).
18. (New) The communication node of claim 17, wherein the processor, when executing the computer program, implements determining that the distribution of the number of detection failures satisfies the detection termination condition by:
in a case where a detection time threshold is a time interval between two AI prediction beam quality detection failures, in response to the communication node determining the one AI prediction beam quality detection failure, re-timing and recording accumulatively the number of AI prediction beam quality detection failures (beams are reported that diverge from a predicted measurement by more than a threshold amount, [0076], Pazeshki), or in response to the communication node determining one AI prediction beam quality detection success, clearing the accumulatively recorded number of AI prediction beam quality detection failures and timing time; and
in response to the communication node determining that the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold and the timing time does not exceed the detection time threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden);
in a case where a detection time threshold is a time interval between two AI prediction beam quality detection failures, in response to the communication node determining the one AI prediction beam quality detection failure, recording accumulatively the number of AI prediction beam quality detection failures and re-timing, or in response to the communication node determining one AI prediction beam quality detection success, not recording the number of AI prediction beam quality detection failures and continuing timing (beam quality may trigger beam failure detection parameters, Ryden); and
in response to the communication node determining that timing time reaches the detection time threshold and the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden);
in a case where a detection time threshold is a length of one AI prediction beam quality detection, in response to the communication node determining the one AI prediction beam quality detection failure, recording accumulatively the number of AI prediction beam quality detection failures and continuing timing, or in response to the communication node determining one AI prediction beam quality detection success, clearing the accumulatively recorded number of number of AI prediction beam quality detection failures and timing time (beam quality may trigger beam failure detection parameters, Ryden); and
in response to the communication node determining that the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold and the timing time does not exceed the detection time threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition; or in a case where a detection time threshold is a length of one AI prediction beam quality detection, in response to the communication node determining the one AI prediction beam quality detection failure, recording accumulatively the number of AI prediction beam quality detection failures and continuing timing, or in response to the communication node determining one AI prediction beam quality detection success, not recording the accumulatively recorded number of AI prediction beam quality detection failures and continuing timing (beam quality may trigger beam failure detection parameters, Ryden); and
in response to the communication node determining that timing time reaches the detection time threshold and the accumulatively recorded number of AI prediction beam quality detection failures reaches a failure number threshold, determining that the distribution of the number of AI prediction beam quality detection failures satisfies the detection termination condition (beam quality may trigger beam failure detection parameters, Ryden);
wherein the one AI prediction beam quality detection success comprises the communication node determining that the beam quality value is greater than or equal to the beam quality threshold (beam quality may trigger beam failure detection parameters, Ryden).
19. (New) The communication node of claim 13, wherein the processor, when executing the computer program, implements re-determining the target beam by:
determining a first new beam as the target beam (beam quality may trigger beam failure detection parameters, Ryden);
wherein the first new beam comprises one of the following:
an adjacent beam of a current beam (without further clarification, adjacent beam simply means a nearby beam, any detectable beam could be considered adjacent);
a beam indicated by media access control control element (MAC CE) signaling (MAC-CE signaling utilized, [0092], Pazeshki); or
a beam within a time window indicated by higher-layer signaling.
20. (New) The communication node of claim 19, wherein the processor, when executing the computer program, implements re-determining the target beam by:
determining a second new beam as the target beam in a case where a beam quality value of the first new beam does not meet the beam quality requirement (beam quality may trigger beam failure detection parameters, Ryden);
wherein the second new beam comprises a default beam configured by the second communication node (network entity predicts best beams based on UE location and orientation, [0059], Pazeshki).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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PETER G. SOLINSKY
Examiner
Art Unit 2463
/Peter G Solinsky/Primary Examiner, Art Unit 2463