Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This action is in response to the Request for Continued Examination filed August 12, 2026 and the Amendment filed July 13, 2026.
3. Claims 1, 6-7, 13-15, and 17-20 have been amended and claims 3-4 have been cancelled.
4. Claims 1-2 and 5-20 have been examined and are pending with this action.
Specification
5 The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Response to Arguments
6. Applicant's arguments filed July 13, 2026 with respect to the rejection of claims 1-20, previously 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), have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant's arguments filed July 13, 2026 with respect to the rejection of claims 1-12, 16-19, and 20, previously rejected under 35 U.S.C. 102(a)(1), have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Soldati et al. (US 2024/0205101 A1). Please see rejections set forth below.
For at least these reasons and the rejections set forth below, claims 1-2 and 5-20 remain rejected and pending.
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
7. Claims 1-2 and 5-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Soldati et al. (US 2024/0205101 A1).
INDEPENDENT:
As per claim 1, Soldati teaches a communication method, comprising:
receiving, by a second communication device from a first communication device, first indication information, wherein the first indication information is used to indicate a processing mode of a target object at a reference point, the first communication device and the second communication device are different communication devices with sending and receiving functions, and the target object comprises at least one of data, a signal, or a service (see Soldati, FIG. 6; [0021]: “In one embodiment, a method performed by a first network node comprises receiving a first message from a second network node, the first message comprising information about how to format data for execution of at least a ML or AI process, or ML or AI model thereof, that is available for execution at the first network node.”’; and [0046]: “a non-limiting example of the operation of a first network node and a second network node in which these two network nodes exchange information for scaling, de-scaling, or formatting data associated to a ML/AI algorithm, or ML/AI model thereof, executed by the first network node in accordance with one embodiment of the present disclosure”); and
processing, by the second communication device, the target object at the reference point based on the processing mode (see Soldati, [0023]: “In one embodiment, executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message comprises formatting at least one input data provided to the ML or AL process, or the ML or AI model thereof, and/or at least one output data provided by the ML or AL process, or the ML or AI model thereof, based on the information comprised in the first message.”);
wherein the processing mode comprises either of the following:
an artificial intelligence (AI) model based processing model (see Soldati, Abstract: “The method further comprises executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message”); or
a processing mode determined by a target communication device, wherein the target communication device comprises the second communication device;
wherein the Al model based processing mode based on the Al mode is indicated by at least one of the following:
a model connection relationship between Al models;
a startup mode of an Al model (see Soldati, [0022]: “In one embodiment, the ML or AI process, or the ML or AI model thereof, is trained at the second network node and provided to the first network node”); or
information about a processing domain corresponding to an Al model, wherein the processing domain comprises the reference point.
As per claim 17, Soldati teaches a communication method, comprising:
exchanging, by a first communication device with at least one second communication device, first indication information corresponding to a reference point, wherein the first indication information is used to indicate a processing mode of a target object at the reference point, the first communication device and the second communication device are different communication devices with sending and receiving functions, and the target object comprises at least one of data, a signal, or a service (see Claim 1 rejection above);
wherein the processing mode comprises either of the following:
an artificial intelligence (AI) model based processing mode; or
a processing mode determined by a target communication device, wherein the target communication device comprises the second communication device (see Claim 1 rejection above);
wherein the processing mode based on the AI model is indicated by at least one of the following:
a model connection relationship between AI models;
a startup mode of an AI model; or
information about a processing domain corresponding to an AI model, wherein the processing domain comprises the reference point (see Claim 1 rejection above).
As per claim 19, Soldati teaches a second communication device, comprising a processor and a memory, wherein the memory stores a program or instructions capable of running on the processor, wherein the program or instructions, when executed by the processor, cause the second communication device to perform (see Soldati, [0023]: “In one embodiment, executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message comprises formatting at least one input data provided to the ML or AL process, or the ML or AI model thereof, and/or at least one output data provided by the ML or AL process, or the ML or AI model thereof, based on the information comprised in the first message”; and [0232]: “The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory)”):
receiving first indication information from a first communication device, wherein the first indication information is used to indicate a processing mode of a target object at a reference point, the first communication device and the second communication device are different communication devices with sending and receiving functions, and the target object comprises at least one of data, a signal, or a service (see Claim 1 rejection above); and
processing the target object at the reference point based on the processing mode (see Claim 1 rejection above);
wherein the processing mode comprises either of the following:
an artificial intelligence (AI) model based processing mode; or
a processing mode determined by a target communication device, wherein the target communication device comprises the second communication device (see Claim 1 rejection above);
wherein the processing mode based on the AI model is indicated by at least one of the following:
a model connection relationship between AI models;
a startup mode of an AI model; or
information about a processing domain corresponding to an AI model, wherein the processing domain comprises the reference point (see Claim 1 rejection above).
DEPENDENT:
As per claims 2 and 18, which respectively depend on claims 1 and 17, Soldati further teaches wherein the reference point is determined based on at least one of the following: a predefinition; a protocol agreement; or second indication information sent by a third end, wherein the second indication information is used to indicate the reference point (see Soldati, [0029]: “The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory)”; and [0068]: “Certain aspects of the present disclosure and their embodiments may provide solutions to the aforementioned or other challenges. According to at least some embodiments of the present disclosure, to properly execute a ML/AI algorithm (e.g., after a policy/model update received by another network node) and to properly interpret the output returned by the ML/AI algorithm for a given set of input information, it may be advantageous for the RAN node to be aware of the scaling criteria that needs to be applied to the data used as input to the ML/AI algorithm as well as the de-scaling or re-formatting criteria that needs to be applied to the output of the ML/AI algorithm before it is used in the RAN”).
As per claim 5, which depends on claim 1, Soldati further teaches wherein the model connection relationship between the AI models comprises at least one of the following: a processing order of the AI models; or an input-output relationship between the AI models (see Soldati, [0215]: “It should be noted that the names used for the messages exchanged between the first network node 600 and the second network node 602 do not imply any chronological order. In one example, for instance, the first network node 600 receives the SECOND MESSAGE prior to transmitting the FIRST MESSAGE. In this case, the method may further comprise the steps of:”; [0216]: “determining one or more information associated to how to format data for executing at least a ML/AI algorithm, or a ML/AI model thereof, available at the first network node based on the SECOND MESSAGE; and”; and [0217]: “transmitting the FIRST MESSAGE to the first network node, the FIRST MESSAGE comprising the data formatting information determined based on the SECOND MESSAGE”).
As per claim 6, which depends on claim 5, Soldati further teaches wherein the processing, by the second communication device, the target object at the reference point based on the processing mode comprises at least one of the following:
in a case that the model connection relationship between the AI models comprises the processing order of the AI models, determining, by the second communication device, a first application AI model according to the processing order of the AI models, and starting the first application AI model according to the processing order of the AI models to process the target object (see Soldati, [0022]: “In one embodiment, the ML or AI process, or the ML or AI model thereof, is trained at the second network node and provided to the first network node”; [0215]: “It should be noted that the names used for the messages exchanged between the first network node 600 and the second network node 602 do not imply any chronological order. In one example, for instance, the first network node 600 receives the SECOND MESSAGE prior to transmitting the FIRST MESSAGE. In this case, the method may further comprise the steps of:”; [0216]: “determining one or more information associated to how to format data for executing at least a ML/AI algorithm, or a ML/AI model thereof, available at the first network node based on the SECOND MESSAGE; and”; and [0217]: “transmitting the FIRST MESSAGE to the first network node, the FIRST MESSAGE comprising the data formatting information determined based on the SECOND MESSAGE”); or
in a case that the model connection relationship between the AI models comprises the input-output relationship between the AI models, determining, by the second communication device, a second application AI model based on the input-output relationship between the AI models, and processing the target object by using the second application AI model.
As per claim 7, which depends on claim 1, Soldati further teaches wherein the first indication information comprises first target information used to indicate the model connection relationship between the AI models, and the first target information comprises at least one of the following:
a model identifier used to indicate an AI model used by the target communication device, wherein the target communication device comprises the second communication device (see Soldati, [0164]: “In one example, a ML/AI algorithm, or a model thereof, available at the first network node 600, may be associated with a unique identifier which may be known at the second network node 602. Therefore, the first network node 600 may use such identifier to indicate for which ML/AI algorithm, or ML/AI model thereof, requests data formatting information to the second network node 602”); or
a model connection identifier used to indicate a connection relationship between different AI models.
As per claim 8, which depends on claim 7, Soldati further teaches wherein the first target information comprises model configuration information, and the model configuration information comprises the model identifier (see Soldati, [0140]: “According to embodiments, a model could be signaled using a high-level model description, plus a detailed information regarding for example the weights of each layer of the NN. According to other embodiments, a model could be signaled by transmitting a model parameter vector. A NN model parameter vector may for example comprise parameters defining the structure and characteristics of the model, such as for example number of layers, activation function of respective layer, nature of connections between nodes of respective layer, weights, loss function, just to mention a few.”); and
the model configuration information is further used to configure an input of the AI model to cite a first object, and the first object comprises at least one of the following: a preset reference point or an output of another AI model (see Soldati, [0023]: “In one embodiment, executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message comprises formatting at least one input data provided to the ML or AL process, or the ML or AI model thereof, and/or at least one output data provided by the ML or AL process, or the ML or AI model thereof, based on the information comprised in the first message.”).
As per claim 9, which depends on claim 1, Soldati further teaches wherein the startup mode of the AI model comprises at least one of the following:
startup based on a preset start time;
startup in a case that data of at least one reference point corresponding to the AI model arrives;
startup in a case that data of at least one reference point in the data of the at least one reference point corresponding to the AI model is updated;
startup in a case that all inputs required by the AI model arrive (see Soldati, [0023]: “In one embodiment, executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message comprises formatting at least one input data provided to the ML or AL process, or the ML or AI model thereof, and/or at least one output data provided by the ML or AL process, or the ML or AI model thereof, based on the information comprised in the first message”);
startup in a case that at least one preset AI model completes processing;
startup in a case that an output of at least one preset AI model is updated;
startup in a case that the AI model receives an input from a previous AI model; or
startup in a case that the AI model receives a preset output value from another AI model;
wherein at least one AI model is started.
As per claim 10, which depends on claim 1, Soldati further teaches wherein there is at least one processing domain, and the processing domain is determined by at least one of the following: a function of the reference point; a relationship between an input/output of the AI model and the reference point; and the model connection relationship between the AI models (see Soldati, [0022]: “In one embodiment, the ML or AI process, or the ML or AI model thereof, is trained at the second network node and provided to the first network node”; and [0027]: “the information comprised in the first message comprises information that indicates a linear or non-linear scaling function to be utilized by the first network node to scale at least one input data to the ML or AI process, or the ML or AI model thereof, and/or to descale at least one output data of the ML or AI process, or the ML or AI model thereof”).
As per claim 11, which depends on claim 1, Soldati further teaches wherein the information about the processing domain comprises at least one of the following: a processing time constraint corresponding to the processing domain; or a processing mode corresponding to the processing domain (see Soldati, [0135]: “(a) an extended validity period, such as a time period, associated to the data formatting information available at the first network node 600”).
As per claim 12, which depends on claim 11, Soldati further teaches wherein the processing time constraint corresponding to the processing domain or the processing mode corresponding to the processing domain comprises at least one of the following:
the startup mode of the AI model, a start time of the AI model, an end time of the AI model, or processing duration of the AI model; or, wherein different processing domains correspond to respective processing time constraints; or, wherein different processing domains correspond to respective processing modes (see Soldati, [0062]: “different types of input data that can be simultaneously used to train a ML/AI algorithm (or a ML/AI model) can have rather different domains of values, which often can differ by several order of magnitudes”).
As per claim 13, which depends on claim 1, Soldati further teaches wherein the method further comprises:
sending, by the second communication device to a fourth communication device, a first service request for requesting a first function (see Soldati, [0185]: “Therefore, in response to the SECOND MESSAGE, the second network node 602 may transmit a THIRD MESSAGE to the first network node 600”);
receiving, by the second communication device, description information that is of the first function and that is sent by the fourth communication device (see Soldati, [0185]: “the THIRD MESSAGE may indicate which data formatting information requested by the first network node 600 via the SECOND MESSAGE can be provided by the second network node 602.”); and
processing, by the second communication device, the target object based on the description information of the first function, wherein the description information of the first function comprises at least one of the following: the reference point corresponding to the first communication device, the processing mode, a relationship between the reference point and the processing mode, information about a processing domain, AI model information, a model connection relationship between AI models, or a startup mode of an AI model (see Claim 1 and Claim 6 rejections above).
As per claim 14, which depends on claim 13, Soldati further teaches wherein the processing, by the second communication device, the target object based on the description information of the first function comprises at least one of the following:
in a case that the description information of the first function comprises the reference point corresponding to the first communication device, processing, by the second communication device, the target object at the reference point;
in a case that the description information of the first function comprises the relationship between the reference point and the processing mode, processing, by the second communication device, the target object at the reference point based on the corresponding processing mode;
in a case that the description information of the first function comprises the information about the processing domain, obtaining, by the second communication device, a target reference point comprised in the processing domain, and processing the target object at the target reference point;
in a case that the description information of the first function comprises the processing mode, processing, by the second communication device, the target object based on the processing mode;
in a case that the description information of the first function comprises the AI model information, determining, by the second communication device, a target AI model based on the AI model information, and processing the target object based on the target AI model;
in a case that the description information of the first function comprises the model connection relationship between the AI models, determining, by the second communication device, the target AI model based on the connection relationship between the AI models, and processing the target object based on the target AI model; or
in a case that the description information of the first function comprises the startup mode of the AI model, determining, by the second communication device, the target AI model based on the startup mode of the AI model, and starting the target AI model based on the startup mode of the AI model to process the target object; or, wherein the method further comprises: receiving, by the second communication device, updated description information that is of the first function and that is sent by at least one of the first communication device or the fourth communication device (see Soldati, [0092]: “the FIRST MESSAGE comprising information associated to how to format data for executing at least a ML/AI algorithm, or a ML/AI model thereof, available at the first network node”; [0114]: “the FIRST MESSAGE received by the first network node 600 may indicate a linear scaling function to be utilized by the first network node 600 for at least one input data (or input element) and/or at least one output data (or output element) of a ML/AI algorithm, or the ML/AI model thereof, available at the first network node 600”; [0134]: “the FIRST MESSAGE may further indicate:”; [0135]: “(a) an extended validity period, such as a time period, associated to the data formatting information available at the first network node 600”; [0136]: “(b) a level of accuracy associated to the data formatting information available at the first network node 600”; [0137]: “(c) an indication of an expected performance degradation associated to the data formatting information available at the first network node 600; or [0138] (d) a combination of any two or more of (a)-(c)”; and [0140]: “According to embodiments, a model could be signaled using a high-level model description, plus a detailed information regarding for example the weights of each layer of the NN”).
As per claim 15, which depends on claim 1, Soldati further teaches wherein the method further comprises:
sending, by the second communication device to a fifth end, a second service request for requesting a first mode (see Claim 1 and Claim 13 rejections above. NOTE: repeating the sending step to another node does not add any further functional limitations);
receiving, by the second communication device, description information that is of the first mode and that is sent by the fifth end (see Soldati, [0140]: “According to embodiments, a model could be signaled using a high-level model description, plus a detailed information regarding for example the weights of each layer of the NN. According to other embodiments, a model could be signaled by transmitting a model parameter vector. A NN model parameter vector may for example comprise parameters defining the structure and characteristics of the model, such as for example number of layers, activation function of respective layer, nature of connections between nodes of respective layer, weights, loss function, just to mention a few.”); and
processing, by the second communication device, the target object based on the description information of the first mode, wherein the description information of the first mode comprises at least one of the following: the reference point corresponding to the first mode, the processing mode, a relationship between the reference point and the processing mode, information about a processing domain, AI model information, a model connection relationship between AI models, or a startup mode of an AI model (see Claim 1 and Claim 6 rejections above).
As per claim 16, which depends on claim 1, Soldati further teaches wherein the first indication information is further used to indicate at least one of a test mode or a test requirement for the reference point (see Soldati, [0010]: “In O-RAN Working Group 2, “AI/ML workflow description and requirements”, February 2021, the O-RAN Working Group 2 has provided an updated overview of the AI/ML workflow description and requirements for supporting ML/AI-driven operations in RANs”; [0139]: “In one example, the second network node 602 may, via the FIRST MESSAGE, indicate that the data formatting information available at the first network node 600 associated to a ML/AI algorithm, or ML/AI model thereof, is still valid.”).
As per claim 20, Soldati further teaches first communication device, comprising a processor and a memory, wherein the memory stores a program or instructions capable of running on the processor, and when the program or instructions are executed by the processor, the steps of the communication method according to claim 17 are implemented (see Soldati, [0232]: “The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory)”).
Conclusion
9. For the reasons above, claims 1-2 and 5-20 have been rejected and remain pending.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL Y WON whose telephone number is (571)272-3993. The examiner can normally be reached on Wk.1: M-F: 8-5 PST & Wk.2: M-Th: 8-7 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached on 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael Won/Primary Examiner, Art Unit 2443
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