DETAILED ACTION
Claim Objections
Claim 18 is objected to because of the following informalities:
In claim 18, the first instance of “AI” should be changed to “Artificial Intelligence (AI)” for consistency with the other independent claims.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a) – Enablement
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 1-15, 18, 19, and 22-24 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 claims contain 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. That is, 35 U.S.C. 112(a) requires “[t]he 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.” However, as noted in detail below, key elements of the invention are described only in very high-level and general terms and do not provide a full, clear, concise, and exact description of how the invention is implemented.
Regarding claims 1-15, 18, 19, and 22-24: the claims are directed towards methods, devices, and media for determining “an AI beam model” that include the steps of receiving (or sending) this AI beam model and determining a characteristic corresponding to the AI beam model.
Thus, a key element of the invention is the claimed AI beam model. However, the specification does not specifically define or even describe in any level of detail the AI beam model. One of the more detailed explanations appears in [0041] of the specification, which indicates “after receiving the AI beam model sent by the network side device, the method includes: determining an AI beam model input parameter and inputting the input parameter into the AI beam model to obtain a prediction result of a beam measurement quality, where the input parameter includes at least one of the following: a second receive beam characteristic; a beam pair characteristic corresponding to a third number of beam pairs; or a beam measurement quality corresponding to the third number of beam pairs”. This description suggests that the AI beam model is something that can be sent by a network side device and received by a terminal, and can also receive “an input parameter” and output “a prediction result of a beam measurement quality”.
An AI model typically refers to a computer program that is trained on large amounts of data that can take an input and generate a meaningful output. An AI model typically recognizes patterns or makes predictions based on the input and the model’s internal weights. However, the specification does not mention the words “train”, “training”, or “weights” typically associated with this definition of an AI model. In the passage above, the output is very generally described as “a prediction result of a beam measurement quality” and the “beam measurement quality” is also an input to the model. It is not clear what the model is predicting or how (even at a high level) it is performing these predictions.
Stated another way, AI beam model is essentially described as a “black box” that is labeled as a “model”. The details regarding the function and the outputs of the AI beam model are not explained in the original disclosure.
Further, even assuming that the second part of paragraph [0041] (describing the input and output process) suggests such a computer program, such programs are typically quite large. It is not typically practical for a network to transmit different entire models to multiple UEs as appears to be claimed in independent claims 1, 10, and 18. In particular, it is unclear how a large AI model can be transmitted to a terminal in a timely fashion to aid in the terminal’s beam processing.
It is possible that a system in which a network updates a model executed locally on a terminal device operates by either (a) sending certain parameters and/or weights of the model to the terminal or (b) sending an index identifying a particular pre-configured model to be used. However, the specification merely indicates that the terminal receives an “AI beam model” from a network side device without providing further details as to what information is transmitted over the wireless channel.
Further, using the description of paragraph [0041] as an example, if the “model” received by the terminal is parameters, weights, or an index, these values cannot accept an input parameter and generate a prediction result of a beam measurement quality.
With regard to MPEP 2164.01(a), the examiner notes the following regarding In re Wands, 858 F.2d 731,737,8 USPQ2d 1400, 1404 (Fed. Cir. 1988):
The breadth of the claims regarding the above-cited limitation is broad. As noted above, the claims merely describe where the AI beam model is transmitted (or received) and describe characteristics “corresponding to” the model. The original disclosure is lacking in any details or examples of the behavior of the claimed AI beam model. The claims and original disclosure also fail to specify the specific elements of an AI beam model that are transmitted/received. Clearly, the claims are quite broad in their description of the AI beam model.
The nature of the invention applies to “determining an Artificial Intelligence (AI) beam model” including receiving (or transmitting) the model and determining a “characteristic corresponding to” the model. An AI model typically refers to a computer program that is trained on large amounts of data that can take an input and generate a meaningful output. An AI model typically recognizes patterns or makes predictions based on the input and the model’s internal weights. Computer programs comprising AI models are typically quite large.
The state of the prior art indicates that the use of artificial intelligence models in beam management is known. Consider Sun et al (US 2026/0181406), which discloses a method for the management and distribution of artificial intelligence models for beam management. Sun discloses specific details regarding receiving beam-related measurements from one or more terminals/user equipments (UEs) and using these measurements to select an artificial intelligence model for the UE. The UE is notified of the model selection via indication information associated with the determined model. The prior art does not appear to discuss sending an entire artificial intelligence model to a terminal or using the parameters of the model alone to predict beam behavior.
The level of one of ordinary skill in the art for the claims and specification is a person having at least a Bachelor’s Degree (and preferably a Graduate Degree) in Electrical, Computer, or Telecommunications engineering (or the like). The examiner possesses the skills of at least one skilled in the art. That is, the Examiner possesses a graduate degree in the above field and has worked and examined in this area for many years. From the perspective of one skilled in the art, the examiner does not believe that the applicant’s original disclosure satisfies the enablement requirement, as it would require undue experimentation.
The level of predictability within the art requires that the applicant provide an enabling disclosure. In particular, as noted above, the prior art discloses the use of AI models in beam management similar in some ways to the present application. The prior art provides quite a bit of specificity regarding the use of such an AI model. This suggests that there is not a high level of predictability regarding merely using an AI model for beam management or for transmitting entire AI models to terminals. That is, significantly more detail than the term “AI beam model” is required such that the level of predictability of such a model is known. The original disclosure does not provide such detail and the level of predictability suggests that the present disclosure is not enabling.
There is little direction provided by the inventor. As noted above, the inventor’s claims, specification, and drawings merely suggest that an AI beam model is used and received/transmitted between a network side device and a terminal. The model is described at a very high level with a broadly worded output of “a prediction result”. However, the inventor’s disclosure does not provide details as to how this model would be implemented or how it would be received/transmitted between a network and a terminal. The Examiner argues that this level of direction is not sufficient to enable one skilled in the art to make and/or use the invention without undue experimentation.
There is significant question regarding the existence of working examples. The specification does not provide specific details regarding either (a) the parameters used within or the configuration of the claimed “AI beam model”, or (b) how such an AI model would be practically transmitted to a terminal in a manner timely enough to use in beam management. The original disclosure only describes the AI beam model and its transmission to a terminal in very high level terms. The specific hardware components that may be used in the devices of the system are described, but there are no specific working examples of the AI beam model itself, or of the transmission of the AI model. This lack of working models suggests the disclosure does not enable one skilled in the art to make and/or use the invention without undue experimentation.
Given the analysis above, undue experimentation is needed to make or use the invention based on the content of the disclosure. In order to implement the claimed invention, a significant amount of experimentation would be required by one skilled in the art. Examiner notes that the inputs to the AI beam model are indicated in the inventor’s original disclosure. The disclosure then indicates that the model generates “a prediction result” related to beam measurement quality. However, the details of this output/result and how it is obtained are completely lacking. Further, AI models are typically quite large. The original disclosure indicates that an AI model is transmitted to a terminal. There are no details indicating how this can be practically performed in a system to enable the model to be used for beam management. One skilled in the art would be required to experiment with AI model design to create such a system. This quantity of experimentation is not reasonable and Examiner maintains that the generic suggestion to use an AI beam model and to transmit it between a network-based device and a terminal is not enabling to one of skill in the art to make and/or use the invention.
MPEP 2164.01(a) reminds the examiner that, “The determination that "undue experimentation" would have been needed to make and use the claimed invention is not a single, simple factual determination. Rather, it is a conclusion reached by weighing all the above noted factual considerations. In re Wands, 858 F.2d at 737, 8 USPQ2d at 1404. These factual considerations are discussed more fully in MPEP § 2164.08 (scope or breadth of the claims), § 2164.05(a) (nature of the invention and state of the prior art), § 2164.05(b) (level of one of ordinary skill), § 2164.03 (level of predictability in the art and amount of direction provided by the inventor), § 2164.02 (the existence of working examples) and § 2164.06 (quantity of experimentation needed to make or use the invention based on the content of the disclosure).”.
Examiner notes that the above factual considerations have been weighed in the analysis above. This analysis clearly suggests that the claimed invention is not enabled.
Claim Rejections - 35 USC § 112(b)
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 1-9, 11-15, 19, and 22-24 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.
Regarding claims 1-9, 11-15, 19, and 22-24: the intended claim scope of the phrase “AI beam model” is unclear. One potential interpretation is that the AI beam model is a computer program that is trained on large amounts of data that can take an input and generate a meaningful output. However, this definition does not make sense in the context of the claims. That is, it is unclear how this entire computer program (which is typically quite large) can be received by a terminal from a network side device. This is particularly true in the context of some of the dependent claims where the transmission/reception of the AI beam model is performed in response to a request from the terminal.
The specification does not provide a specific definition of the phrase “AI beam model” and does not provide specific examples of how this model is transmitted from a network side device to a terminal.
It is possible that a system in which a network updates a model executed locally on a terminal device operates by either (a) sending certain parameters and/or weights of the model to the terminal or (b) sending an index identifying a particular pre-configured model to be used. However, the specification merely indicates that the terminal receives an “AI beam model” from a network side device without providing further details as to what information is transmitted over the wireless channel. It is not clear how the large quantity of data comprising an “AI beam model” can be practically sent from a network to a terminal in a timely fashion such that the model can be used for predicting beam performance. It is thus unclear what is the intended scope of the term “AI beam model” that can practically be transmitted between a network side device and a terminal.
Independent claims 1, 10, and 18 all include the phrase “AI beam model” and are thus rejected under 35 U.S.C. 112(b) as being indefinite. Dependent claims 2-9, 11-15, 19, and 22-24 depend from one of the above claims and are thus similarly rejected under 35 U.S.C. 112(b) as being indefinite.
Regarding claims 4 and 14: the phrase “the first receive beam” (see lines 7, 11, and 12 of claim 4) does not have sufficient antecedent basis in the claims. The claims disclose “a second number of second receive beams” and thus have antecedent basis for the limitations “the second receive beams” in claims 4 and 14. However, a specific “second receive beam” is not recited in the claims prior to the above limitations.
Regarding claims 5 and 12: the phrase “the second receive beam” (see lines 7, 11, and 12 of claim 5) does not have sufficient antecedent basis in the claims. The claims disclose “a second number of second receive beams” and thus have antecedent basis for the limitations “the second receive beams” in claims 5 and 12. However, a specific “second receive beam” is not recited in the claims prior to the above limitations.
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)(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.
Claims 1-5, 10-14, 18, 19, and 22-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sun et al (US 2026/0181406).
Regarding claim 1: Sun discloses a method for determining an Artificial Intelligence (AI) beam model, performed by a terminal, the method comprising:
receiving an AI beam model sent by a network side device (disclosed throughout; see [0026], for example, which describes that a UE receives “indication information associated with an artificial intelligence model for beam prediction for the UE”; as indicated in [0162], this can involve sending “the artificial intelligence model to the UE”), and
determining a first receive beam characteristic corresponding to the AI beam model (disclosed throughout; see steps 4 and 5 of Figure 9, for example, which disclose a UE receiving a model (via the model parameter set) and further determining a first receive beam characteristic (the serving beam used by the base station that is notified by the base station); this serving beam is used to perform measurements and input to the model in steps 5 and 6 and is the corresponding to the received model).
Regarding claim 10: Sun discloses a method for determining an Artificial Intelligence (AI) beam model, performed by a network side device, the method comprising:
sending an AI beam model to a terminal (disclosed throughout; see [0016], for example, which describes that a network device sends “indication information associated with the artificial intelligence model” and that this model is “for beam prediction”; as indicated in [0162], this can involve sending “the artificial intelligence model to the UE”).
Regarding claim 18: Sun discloses a communication device, comprising:
a processor (disclosed throughout; see processor 1601 of Figure 14, for example); and
a memory for storing, a computer program executable by the processor (disclosed throughout; see memory 1602 of Figure 14, for example);
wherein the processor is configured to perform following acts:
receiving an AI beam model sent by a network side device (disclosed throughout; see [0026], for example, which describes that a UE receives “indication information associated with an artificial intelligence model for beam prediction for the UE”; as indicated in [0162], this can involve sending “the artificial intelligence model to the UE”), and
determining a first receive beam characteristic corresponding to the AI beam model (disclosed throughout; see steps 4 and 5 of Figure 9, for example, which disclose a UE receiving a model (via the model parameter set) and further determining a first receive beam characteristic (the serving beam used by the base station that is notified by the base station); this serving beam is used to perform measurements and input to the model in steps 5 and 6 and is the corresponding to the received model).
Regarding claims 2, 11, and 24: Sun discloses the limitations of sending (or receiving) an Al beam model request to the network side device (see [0031], for example, which discloses that the UE may send “a request for the artificial intelligence model for beam prediction”); or sending (or receiving) a second receive beam characteristic to the network side device (disclosed throughout; see [0026], for example, which discloses that the UE reports a “beam measurement result” to the network device; the details of the report including this beam measurement result are included throughout Sun).
Regarding claim 3: Sun discloses the limitations that determining the first receive beam characteristic corresponding to the Al beam model comprises: receiving indication information sent by the network side device, wherein the indication information is configured to indicate the first receive beam characteristic corresponding to the Al beam model (disclosed throughout; see steps 4 and 5 of Figure 9, for example, which disclose a UE receiving a model (via the model parameter set) and further determining a first receive beam characteristic (the serving beam used by the base station that is notified by the base station)); or determining the first receive beam characteristic corresponding to the Al beam model based on a default rule.
Regarding claim 4: Sun discloses the limitations that the first receive beam characteristic comprises at least one of:
a first number of first receive beams supported by the Al beam model;
a number of first dimensional direction angles corresponding to the first receive beams in the Al beam model;
a first angular value of the first dimensional direction angle corresponding to the first receive beam in the Al beam model;
a number of second dimensional direction angles corresponding to the first receive beams in the Al beam model;
a second angular value of the second dimensional direction angle corresponding to the first receive beam in the Al beam model; or
a first beam identifier corresponding to the first receive beam in the Al beam model (disclosed throughout; see steps 4 and 5 of Figure 9, for example, as indicated in [0213], “the base station notifies the UE1 of a serving beam used by the base station”, which is at least a first beam identifier corresponding to a first beam).
Regarding claims 5 and 12: Sun discloses the limitations that the second receive beam characteristic comprises at least one of:
a second number of second receive beams supported by the terminal;
a number of first dimensional direction angles corresponding to the second receive beams in the terminal;
a first angular value of the first dimensional direction angle corresponding to the second receive beam in the terminal;
a number of second dimensional direction angles corresponding to the second receive beams in the terminal;
a second angular value of the second dimensional direction angle corresponding to the second receive beam in the terminal; or
a second beam identifier corresponding to the second receive beam in the terminal (disclosed throughout; see [0075], for example, which discloses that the beam measurement result may include “indexes of a strongest beam and several secondary strong beams”, which is at least a first beam identifier corresponding to a first receive beam).
Regarding claim 13: Sun discloses the limitations of sending indication information to the terminal, wherein the indication information is configured to indicate a first receive beam characteristic corresponding to the Al beam model (disclosed throughout; see steps 4 and 5 of Figure 9, for example, which disclose a UE receiving a model (via the model parameter set) and further determining a first receive beam characteristic (the serving beam used by the base station that is notified by the base station)).
Regarding claim 14: Sun discloses the limitations that the first receive beam characteristic comprises at least one of:
a first number of first receive beams supported by the Al beam model;
a number of first dimensional direction angles corresponding to the first receive beams in the Al beam model;
a first angular value of the first dimensional direction angle corresponding to the first receive beam in the Al beam model;
a number of second dimensional direction angles corresponding to the first receive beams in the Al beam model;
a second angular value of the second dimensional direction angle corresponding to the first receive beam in the Al beam model; or
a first beam identifier corresponding to the first receive beam in the Al beam model (disclosed throughout; see steps 4 and 5 of Figure 9, for example, as indicated in [0213], “the base station notifies the UE1 of a serving beam used by the base station”, which is at least a first beam identifier corresponding to a first beam).
Regarding claim 19: Sun discloses a communication device, comprising: a processor (disclosed throughout; see controller 1421 of Figure 12, for example); and a memory for storing, a computer program executable by the processor (disclosed throughout; see memory 1422 of Figure 12, for example); wherein the processor is configured to perform the method according to claim 10 (see the rejection of claim 10 above).
Regarding claim 22: Sun discloses a non-transitory computer-readable storage medium (disclosed throughout; see memory 1602 of Figure 14, for example), configured to store instructions which, when the instructions are executed by a processor (disclosed throughout; see processor 1601 of Figure 14, for example), cause the processor to perform the method according to claim 1 (see the rejection of claim 1 above).
Regarding claim 23: Sun discloses a non-transitory computer-readable storage medium (disclosed throughout; see memory 1422 of Figure 12, for example), configured to store instructions which, when the instructions are executed by a processor (disclosed throughout; see controller 1421 of Figure 12, for example), cause the processor to perform the method according to claim 10 (see the rejection of claim 10 above).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6-9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al (US 2026/0181406) in view of Shi (US 2025/0016760).
Regarding claim 6: Sun discloses the limitations of parent claim 1 as indicated above. Sun does not explicitly disclose the limitations of claim 6 of determining an Al beam model input parameter and inputting the input parameter into the Al beam model to obtain a prediction result of a beam measurement quality, wherein the input parameter comprises at least one of: a second receive beam characteristic; a beam pair characteristic corresponding to a third number of beam pairs; or a beam measurement quality corresponding to the third number of beam pairs. However, Shi discloses a method using an artificial intelligence model configured for a beam-related function (see the abstract, for example). Shi further discloses in [0027]-[0042], that “the input information of the AI model includes at least one of the following: expected information; beam quality-related information; first beam-related information; or time-related information” [0027]-[0031]. Further, the first beam-related information may be “transmitting beam-related information, reception beam-related information, or beam pair-related information” [0036] and may include “beam identification-related information, beam angle-related information, or beam gain-related information” [0038]. As indicated in [0039], this may include “a beam pair ID”, for example. Further, this may include “beam pair angle-related information, transmitting angle-related information, or reception angle-related information” [0040].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Sun to modify the performing of beam prediction using the artificial intelligence model discussed throughout with the specific inputs described in Shi. In particular, this includes at least a beam pair characteristic corresponding to a third number of beam pairs or a beam measurement quality corresponding to a third number of beam pairs. The rationale for doing so would have been to “obtain better performance” as suggested by Shi in [0003].
Regarding claim 7: Sun, modified, discloses the limitations of parent claim 6 as indicated above. Sun does not explicitly disclose the limitations wherein the beam pair characteristic comprises at least one of: a beam pair identity (ID) corresponding to the beam pair; a reference signal ID corresponding to the beam pair, wherein a reference signal comprises at least one of: a synchronization signal block (SSB) or a channel state information reference signal (CSI-RS); a second beam identifier corresponding to a second receive beam, the second receive beam corresponding to the beam pair; a first angular value of a first dimensional direction angle of the second receive beam corresponding to the beam pair; or a second angular value of a second dimensional direction angle of the second receive beam corresponding to the beam pair. However, Shi discloses a method using an artificial intelligence model configured for a beam-related function (see the abstract, for example). Shi further discloses in [0027]-[0042], that “the input information of the AI model includes at least one of the following: expected information; beam quality-related information; first beam-related information; or time-related information” [0027]-[0031]. Further, the first beam-related information may be “transmitting beam-related information, reception beam-related information, or beam pair-related information” [0036] and may include “beam identification-related information, beam angle-related information, or beam gain-related information” [0038]. As indicated in [0039], this may include “a beam pair ID”, for example. Further, this may include “beam pair angle-related information, transmitting angle-related information, or reception angle-related information” [0040].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Sun to modify the performing of beam prediction using the artificial intelligence model discussed throughout with the specific inputs described in Shi. In particular, this includes at least a beam pair identity and first angular value corresponding to a beam pair. The rationale for doing so would have been to “obtain better performance” as suggested by Shi in [0003].
Regarding claim 8: Sun, modified, discloses the limitations of parent claim 6 as indicated above. Sun does not explicitly disclose the limitations wherein the beam measurement quality comprises at least one of: a layer one reference signal receiving power (L1-RSRP) and/or a layer one signal to interference plus noise ratio (L1-SINR). However, Shi discloses a method using an artificial intelligence model configured for a beam-related function (see the abstract, for example). Shi further discloses in [0027]-[0042], that “the input information of the AI model includes at least one of the following: expected information; beam quality-related information; first beam-related information; or time-related information” [0027]-[0031]. Further, as indicated in [0035], the beam quality-related information includes at least L1-SINR or L1-RSRP.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Sun to modify the performing of beam prediction using the artificial intelligence model discussed throughout with the specific inputs described in Shi. In particular, this includes at least a beam pair characteristic corresponding to a third number of beam pairs or a beam measurement quality corresponding to a third number of beam pairs. The rationale for doing so would have been to “obtain better performance” as suggested by Shi in [0003].
Regarding claims 9 and 15: Sun discloses the limitations of parent claims 1 and 10 as indicated above. Sun does not explicitly disclose the limitations of claims 9 and 15 that the first receive beam characteristic is a number of at least one receive beams supported by the Al beam model, a second receive beam characteristic is a number of at least one receive beams supported by the terminal, and the first receive beam characteristic comprises a number of a plurality of receive beams; or wherein the number of at least one receive beams included in the first receive beam characteristic is less than or equal to the number of at least one receive beams included in the second receive beam characteristic. However, Shi discloses a method using an artificial intelligence model configured for a beam-related function (see the abstract, for example). Shi further discloses a first beam characteristic that is a number of receive beams supported by the AI beam model. Shi discloses throughout that the AI model expects quality information for a particular number of beams. For example, see [0071], which indicates “an input of the AI model needs to include eight pieces of beam quality-related information”. Further, the terminal measures (and thus supports) a second number of 10 beams that it receives (see the “10 RSRPs obtained from the 10 transmitting beams corresponding to the CSI-RSs 1 to 10” in [0071]). The first number is a plurality of receive beams (eight). Further, the first receive beam characteristic (eight) is less than or equal to the second receive beam characteristic (ten).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Sun such that the AI beam model utilizes a particular number of inputs and the terminal supports a second number of receive beams to measure. The rationale for doing so would have been to “obtain better performance” as suggested by Shi in [0003].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al (US 2026/0172089) discloses a system for beam prediction using AI/ML models.
Lu et al (US 12,089,064) discloses a method for beamforming using a machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert C Scheibel whose telephone number is (571)272-3169. The examiner can normally be reached Monday-Friday 8:00 AM - 5:00 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, Hassan A Phillips can be reached at 571-272-3940. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Robert C. Scheibel
Primary Examiner
Art Unit 2467
/Robert C Scheibel/Primary Examiner, Art Unit 2467 October 14, 2026
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