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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 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.
The previous 112(b) rejection has also been withdrawn based on amendment.
Allowable Subject Matter
As another way to possibly move this case forward and place it in condition for potential allowance, separate from the way mentioned in the non-final mailed 06/24/2025 which still stands, is to further include the score concept (e.g., claim 4) in combination with a circular array codebook (e.g., claim 6). While using a circular/ring/rotational codebook structure is generally known in the art, the examiner did not appear to find the concept of using the score with a circular array codebook in combination. See applicant’s own specification at paragraph [0077] for support and context.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
The independent claims 1, 8, and 14 are very broad and applicant’s specification at paragraphs [0076] and [0077] (Fig. 4) only provides a single embodiment(s) for a “codebook index distance” and where this specific embodiment is not clearly claimed. The full scope of the claimed class, not just one example, requires more than reasonable experimentation for a person skilled in the art to practice the entire invention. The single embodiment does not provide enough guidance for the skilled person to understand and practice the entire broad claim without undue effort given it is unclear how this would work with something other than an angle of arrival (AoA) and a difference that is not a “codebook index distance”.
[0076] In at least one embodiment, a codebook index distance is determined 403, that is a comparison of a beam selected during an action 402 and a best beam for that user location. In at least one embodiment, a distance will be computed by comparing an angle of arrival relative to a new action compared to a best action. In at least one embodiment, a comparison is computed preemptively at each channel realization in a training phase.
[0077] In at least one embodiment, a score may assigned 404 to a selected action based on a calculated distance between a selected code index compared to a best code index. In at least one embodiment, a score of 0, +1 or +5 may be assigned, wherein if an action index equals a best beam index, a score of +5 is assigned, if an action index is a direct neighbor of a best action index (either the previous or next index in the circular array), then a score assigned is +1, and every other action will be assigned a score of 0. In at least one embodiment, a codebook is modeled as a circular array of options, due to symmetrical nature of beams, and an calculated as a direct neighbor of a best action may be either a previous or next index in a circular codebook array. In at least one embodiment, a best code is a beam that maximizes the overall observed RSRP and a best action indicates a best beam alignment has been achieved.
To overcome the rejection, applicant is encouraged to amend the claim to be more inline with the specific embodiment disclosed in paragraphs [0076] and [0077] of their own specification.
Claims 2-7, 9-13, and 15-20 are rejected as they do not cure the above cited deficiency.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, and 14 is/are rejected as missing essential omitted elements/steps as knowing the angle of arrival, or equivalent measure of direction, is essential for performing the steps in applicant’s specification at paragraphs [0076] and [0077]. The claim is incomplete and thus indefinite. For example, without AoA or equivalent, you cannot determine which codebook beam is “best,” and therefore cannot calculate the difference or assign the correct score.
It is recommended to include the AOA component with respect to the beams as disclosed in [0076] and [0077].
Claims 2-7, 9-13, and 15-20 are rejected as they do not cure the above cited deficiency.
Claims 1, 8, and 14 is directed as being indefinite because it does not describe what kind of “difference” is being identified. The claim does not specify what kind of “difference”. The specification appears to only enable “difference” as codebook index distance. However, this is not expressly clear from the claim. To overcome the rejection, applicant, is recommended to further clarify that a “codebook index distance” is needed.
Claims 2-7, 9-13, and 15-20 are rejected as they do not cure the above cited deficiency.
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.
Claim(s) 1, 2, 8, 9, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0094418 A1 to POLESE et al. (“POLESE”) in view of US 20220352960 A1 to PARK et al. (“PARK”).
Examiner note: All citations drawn to POLESE unless otherwise mentioned.
With respect to the independent claims:
Regarding claim 1, One or more processors, comprising circuity to (see Fig. 2:processing system 20 and [0061-62]), comprising: one
use one or more neural networks ([0045] with respect to deep neural network) to select one or more wireless signal beams (see e.g., [0046] with respect to waveform-level deep learning and receiving beams. [0047] with respect to angle of arrival engine 34. [0052] with respect to best beam selected.).
POLESE doesn’t expressly disclose a “difference” based on:
identifying a difference between a first beam and second beam of the codebook, wherein the second beam is adjacent to the first beam according to the codebook; and selecting the one or more wireless signal beams based on the identified difference between the first beam and the second beam.
POLESE does disclose identifying a beam between beams, i.e., best beam selected between at least two beams which are ranked and, thus, adjacent [0052]. POLSE doesn’t go into any details on using a codebook for beam selection as claimed.
PARK teaches the further missing limitation(s). PARK discloses that deep learning / neural networks can be performed by device 101 and processor 120 [0033-34]. The codebook is described as part of memory 130 (Fig. 4:130). Here AoA information is included with beam set information which further captures a difference in directional information [0079]. As the information is captured in a table, it is also broadly adjacent to one another. Thus, PARK broadly discloses identifying a difference between a first beam and a second beam of a codebook as claimed.
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can include algorithm(s) as taught by
PARK and codebook as part of memory 130. There is a reasonable expectation of success given POLESE at [0056] discloses that any type of machine learning algorithm can be applied to support deep learning.
Regarding claim 8, a system, comprising: one or more processors to use one or more neural
networks to identify one or more wireless signal beams based, at least in part, on one or more angles of
one or more received wireless signals (See similar rejection claim 1 where system is taught as Fig. 2:
system 10).
Regarding claim 14, a method, comprising: identifying one or more wireless signal beams using
a neural network, based, at least in part, on one or more angles of one or more received wireless signals
(See similar rejection claim 1 where system is taught as Fig. 2: system 10 performs the method as
claimed).
With respect to the dependent claims:
Regarding claim 2, 9, and 15, the one or more processors of claim 1, wherein one or more circuits are to
select the one or more wireless signal beams based, at least in part, on a measured signal power of one or more received wireless signals (see e.g., [0043] with respect to received power value and [0048] with
respect to RSRP).
Claim(s) 3, 7, 10, 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0094418 A1 to POLESE et al. (“POLESE”) in view of US 20220352960 A1 to PARK et al. (“PARK”) in further view of US 9989633 B1 to PANDEY et al. (“PANDEY”).
With respect to the dependent claims:
Regarding claim 3, 10, and 16, the one or more processors of claim 1, wherein the identified difference between the beam and second beam is indicated, at least in part, by using a multiple signal classification (MUSIC) algorithm.
POLESE disclose that machine learning can be used with respect to AoA inference Engine 34
[0050] and [0056]. POLESE does not expressly teach a MUSIC algorithm.
PANDEY discloses that MUSIC algorithm can be used to estimate angle of arrival for machine
learning (Col 13, ll. 1-23). In particular, note that the multi-angle source tracking tool 120 can be in the
receiver 104 (Fig. 1). The multi-angel source tracking tool 120 can use machine learning algorithms used
for estimating/prediction (Col. 6, ll. 50-57).
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can include the MUSIC algorithm as taught by
PANDEY. There is a reasonable expectation of success given POLESE at [0056] discloses that other
machine learning algorithms can be applied to support deep learning.
Regarding claim 7 and 20, the one or more processors o claim 1, wherein the one or more neural networks selects a beamforming vector from a codebook based, at least in part, on output from a multiple signal classification (MUSIC) algorithm and a received wireless signal transmitted from a device within a wireless network.
POLESE disclose that machine learning can be used with respect to AoA inference Engine 34
[0050] and [0056]. POLESE further teaches that the selection is based on beam forming for a codebook
[0016] and [0052] which can also be based on vectors [0004]. POLESE does not expressly teach a MUSIC
algorithm.
PANDEY discloses that MUSIC algorithm can be used to estimate angle of arrival for machine
learning (Col 13, ll. 1-23). In particular, note that the multi-angle source tracking tool 120 can be in the
receiver 104 (Fig. 1). The multi-angel source tracking tool 120 can use machine learning algorithms used
for estimating/prediction (Col. 6, ll. 50-57). The multi-angel source tracking tool 120 further also uses
other types of vectors which also broadly read on the claim, e.g., Col 8, ll. 26-52.
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can include the MUSIC algorithm as taught by
PANDEY. There is a reasonable expectation of success given POLESE at [0056] discloses that other
machine learning algorithms can be applied to support deep learning.
Claim(s) 5, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0094418 A1 to POLESE et al. (“POLESE”) in view of US 20220352960 A1 to PARK et al. (“PARK”) in further view of US 20240072952 A1 to ROM et al. (“ROM”).
With respect to the dependent claims:
Regarding claim 5, 12, and 18 the one or more processors of claim 1, wherein one or more circuits are to select the one or more wireless signal beams based, at least in part, on a comparison between a received beam and a training beam (POLESE teaches training with respect to multiple beams [0050]), wherein the training beam is a wireless signal beam with a signal power above a threshold amount (POLESE teaches making a determination based on power [0048] and also power level [0057])
POLESE teaches a transmit beam inference engine 32 [0050] and where any suitable training can
be performed [0056]. POLESE teaches using a training module but may not expressly teach using a
received beam and training beam comparison.
ROM teaches a comparison using RSRP between a first beam, second beam, and a threshold
(Fig. 25 and [0261). Here the best value (threshold) is achieved in comparison of various first and
second beams (received and training beams) as illustrated in Fig. 25.
It is further worth mentioning that a quick review of applicant’s own specification fails to provide
any additional description for this claimed feature with respect to context and understanding. Thus, the
examiner has taken a reasonable but broader interpretation of the claimed language. Applicant is
further encouraged to note above potential allowable subject matter for what is disclosed in applicant’s
specification at e.g., paragraph [0077].
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can a data received beam and training beam
comparison as taught by ROM. There is a reasonable expectation of success given POLESE at [0056]
discloses that other machine learning algorithms can be applied to support deep learning.
Claim(s) 5, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0094418 A1 to POLESE et al. (“POLESE”) in view of US 20220352960 A1 to PARK et al. (“PARK”) in further view of US 20200328794 A1 to LEE et al. (“LEE”).
With respect to the dependent claims:
Regarding claim 5, 12, and 18 the one or more processors of claim 1, wherein one or more circuits are to select the one or more wireless signal beams based, at least in part, on a comparison between a received beam and a training beam (POLESE teaches training with respect to multiple beams [0050]), wherein the training beam is a wireless signal beam with a signal power above a threshold amount (POLESE teaches making a determination based on power [0048] and also power level [0057])
POLESE teaches a transmit beam inference engine 32 [0050] and where any suitable training can
be performed [0056]. POLESE teaches using a training module but may not expressly teach using a
received beam and training beam comparison.
LEE teaches a comparison using received beam (== data beam) and training beams for AOA, e.g.
Fig. 5 + Fig. 8/9 [0069]. The ranges in Fig. 5 denote a threshold. Fig. 8/9 also specifically note the data
beam (== received beams) with training beams. The “correlation” and “similarity” further show a
comparison with threshold. It order for training to occur, it must be above the amount to be trained,
e.g., lower error is greater in similarity which equates to above a threshold amount as claimed [0069].
Highest correlation can also be selected [0074] which also broadly equates to above a threshold as
claimed.
It is further worth mentioning that a quick review of applicant’s own specification fails to provide
any additional description for this claimed feature with respect to context and understanding. Thus, the
examiner has taken a reasonable but broader interpretation of the claimed language. Applicant is
further encouraged to note above potential allowable subject matter for what is disclosed in applicant’s
specification at e.g., paragraph [0077].
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can a data received beam and training beam
comparison as taught by LEE. There is a reasonable expectation of success given POLESE at [0056]
discloses that other machine learning algorithms can be applied to support deep learning.
Claim(s) 6, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0094418 A1 to POLESE et al. (“POLESE”) in view of US 20220352960 A1 to PARK et al. (“PARK”) in further view of US 20200007216 A1 to NASIRI et al. (“NASIRI”).
With respect to the dependent claims:
Regarding claim 6, 13 and 19, the one or more processors of claim 1, wherein the one or more neural networks select the one or more wireless signal beams from a circular array codebook (see e.g., [0016] with respect to 2. Codebook. [0052] with respect to DeepBeam learning from codebook).
POLSE teaches using a codebook but does not expressly mention a circular array codebook as claimed. In particular, POLSE does disclose chaining codebooks but doesn’t expressly teach creating a circular codebook.
NASIRI teaches a circular array codebook (see e.g., [0170-172] where rotational codebook == circular array codebook).
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can use a rotational or circular array codebook. There is a reasonable expectation of success given both references uses codebooks for beam tracking (NASIRI Abstract).
Claim(s) 1, 4, 8, 11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0094418 A1 to POLESE et al. (“POLESE”) in view of US 20220159684 A1 to MO et al. (“MO”).
With respect to the independent claims:
Regarding claim 1, One or more processors, comprising circuity to (see Fig. 2:processing system 20 and [0061-62]), comprising: one
use one or more neural networks ([0045] with respect to deep neural network) to select one or more wireless signal beams (see e.g., [0046] with respect to waveform-level deep learning and receiving beams. [0047] with respect to angle of arrival engine 34. [0052] with respect to best beam selected.).
POLESE doesn’t expressly disclose:
identifying a difference between a first beam and second beam of the codebook, wherein the second beam is adjacent to the first beam according to the codebook; and selecting the one or more wireless signal beams based on the identified difference between the first beam and the second beam.
POLESE does disclose identifying a difference between beams, i.e., best beam selected [0052]. POLSE doesn’t go into any details on using a codebook for beam selection as claimed.
MO teaches the further missing limitation(s). MO describes selecting a best beam (i.e., second beam) using a codebook and AoA and based on a similarity score or score [0130] (Fig. 14).
Thus, it would have been obvious to one of ordinary skill in the art at the time of effective filing
to clarify that the machine learning algorithms of POLESE can include algorithm(s) as taught by
MO. There is a reasonable expectation of success given POLESE at [0008] discloses beams and beam selection.
Regarding claim 8, a system, comprising: one or more processors to use one or more neural
networks to identify one or more wireless signal beams based, at least in part, on one or more angles of
one or more received wireless signals (See similar rejection claim 1 where system is taught as Fig. 2:
system 10).
Regarding claim 14, a method, comprising: identifying one or more wireless signal beams using
a neural network, based, at least in part, on one or more angles of one or more received wireless signals
(See similar rejection claim 1 where system is taught as Fig. 2: system 10 performs the method as
claimed).
With respect to the dependent claims:
Regarding claim 4, 11and 17, the one or more processors of claim 1, wherein one or more circuits are to select the one or more wireless signal beams based, at least in part, one a score assigned to the first beam and the second beam (see e.g., [0016] with respect to 2. Codebook. [0052] with respect to DeepBeam learning from codebook).
As noted above in the rejection for claim 1, MO teaches a beam score [0130] (Fig. 14). Same motivation applies as in claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 DERRICK W FERRIS whose telephone number is (571)272-3123. The examiner can normally be reached Mon. - Fri. 7 AM-3 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Deborah Reynolds can be reached at (571) 272-0734. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DERRICK W FERRIS/Supervisory Patent Examiner, Art Unit 2411