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 .
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)(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.
Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tian (US 2024/0381059).
Regarding claim 1, Tian teaches a communication method, comprising: obtaining, by a first device, first semantic information corresponding to data (i.e., a terminal device performs semantic acquisition processing on an information source to obtain semantic information [0096]-[0097], [0127]-[0128]); converting, by the first device, the first semantic information into second semantic information, wherein the second semantic information belongs to common semantic information, and the common semantic information is a unified description of same semantics that are provided by different devices (i.e., the terminal device determines, based on the semantic information, first information to be transmitted [0098]-[0099]. Through extraction of the extended semantics, the extended semantic information is extracted from the original information, so that a case that a receive end (or a decoder) cannot acquire complete information can be avoided, thereby improving transmission accuracy and information integrity ([0111], [0122], [0132]). The device may reconstruct the original information source and then perform semantic acquisition again, or it may directly recover the semantic information from the transmitted data [0114]-[0117], [0130], data is converted from encoder format to decoder format (corresponding to converting) [0139]-[0144]); and sending, by the first device, the second semantic information to a second device (i.e., the terminal device may send, to a receive end, the first information as sending information to be transmitted, and the receive end may be a network device (for example, a base station), or may be a terminal device serving as a receive end ([0100]-[0102], [0131]-[0132]-[0133]).
Regarding claim 2, Tian further teaches wherein the converting the first semantic information into second semantic information comprises: performing semantic conversion on the first semantic information based on a semantic conversion model to obtain the second semantic information (i.e., a first receiving unit, configured to receive second information; a second processing unit, configured to restore an information source from the second information; and a second semantic acquiring unit, configured to perform semantic acquisition processing on the information source to obtain semantic information [0024]-[0027], [0247-[0253]).
Regarding claim 3, Tian further teaches inputting training data into the semantic extraction model to obtain training semantic information (i.e., the information source can be input into the semantic acquiring unit, and the semantic information is output and obtained after the semantic acquisition processing [0097], [0108], [0115]-[0116], 0120]); inputting the training data into a common semantic extraction model to obtain label semantic information, wherein the label semantic information belongs to the common semantic information (i.e., information source is input into a first trained network model [0115]-[0116], the core semantic acquisition processing may be added to the terminal device, so that the information source can be input into the combined semantic acquiring unit, and the extended semantic information and the core semantic information are output and obtained after the extended semantic and core semantic acquisition processing being performed (the extended semantic information and the core semantic information constitute the combined semantic information [0120], [0240]-[0242]); and performing training based on the training semantic information and the label semantic information to obtain the semantic conversion model ([0115]-[0116]).
Regarding claim 4, Tian further teaches wherein the common semantic extraction model is configured by a network device (i.e., As shown in FIG. 18, when a semantic acquiring unit is configured to acquire core semantic information, the semantic acquiring unit may be a core semantic acquiring unit. Output information of the core semantic acquiring unit includes core semantic information expressed by original information. As compared with the original information, the core semantic information is obtained from the original information with an aim to semantics that a receive end desires to receive. Through extraction of this part of core semantics, transmission of unnecessary information for the receive end can be avoided, thereby improving transmission efficiency [0223]-[0225]).
Regarding claim 5, Tian further teaches performing semantic conversion on the first semantic information based on a representation manner of the common semantic information to obtain the second semantic information (i.e., terminal device performs extended semantic acquisition processing on an information source to obtain extended semantic information [0102], [0127]-[0128]).
Regarding claim 6, Tian further teaches the representation manner of the common semantic information is configured by one of: a network device or is pre- specified in a protocol (i.e., a network device includes a processing unit, configured to restore semantic information from the first information, where the semantic information is information obtained by performing semantic acquisition processing on an information source on the side of a terminal device ([0028]-[0031]).
Regarding claim 8, Tian further teaches wherein the second semantic information and the first semantic information correspond to a same semantic level (i.e., the second information and the first information in the foregoing embodiment may be same information (for example, both the first information and the second information include only the semantic information [0149]).
Regarding claim 9, Tian teaches a communication method, comprising: receiving, by a second device from a first device, second semantic information, wherein the second semantic information belongs to common semantic information, and the common semantic information is a unified description of same semantics that are provided by different devices (i.e., a terminal device performs semantic acquisition processing on an information source to obtain semantic information [0096]-[0097], [0127]-[0128]); converting, by the second device, the second semantic information into third semantic information (i.e., the terminal device determines, based on the semantic information, first information to be transmitted [0098]-[0099]. Through extraction of the extended semantics, the extended semantic information is extracted from the original information, so that a case that a receive end (or a decoder) cannot acquire complete information can be avoided, thereby improving transmission accuracy and information integrity ([0111], [0122], [0132]). The device may reconstruct the original information source and then perform semantic acquisition again, or it may directly recover the semantic information from the transmitted data [0114]-[0117], [0130], data is converted from encoder format to decoder format (corresponding to converting) [0139]-[0144]); and processing, by the second device, the third semantic information to obtain target information (i.e., the terminal device may send, to a receive end, the first information as sending information to be transmitted, and the receive end may be a network device (for example, a base station), or may be a terminal device serving as a receive end ([0100]-[0102], [0131]-[0132]-[0133]).
Regarding claim 10, Tian further teaches the converting the second semantic information into third semantic information comprises: performing semantic conversion on the second semantic information based on a semantic conversion model to obtain the third semantic information (i.e., a first receiving unit, configured to receive second information; a second processing unit, configured to restore an information source from the second information; and a second semantic acquiring unit, configured to perform semantic acquisition processing on the information source to obtain semantic information [0024]-[0027], [0247-[0253]).
Regarding claim 11, Tian further teaches inputting training data into a semantic extraction model to obtain label semantic information, wherein the semantic extraction model and the semantic understanding model are jointly trained (i.e., the information source can be input into the semantic acquiring unit, and the semantic information is output and obtained after the semantic acquisition processing… information source is input into a first trained network model (the first network model may be deployed in the foregoing extended semantic acquiring unit), to obtain at least one of emotion information [0097], [0108], [0115]-[0116], 0120], [0225]); inputting the training data into a common semantic extraction model to obtain training semantic information corresponding to the training data (i.e., information source is input into a second trained network model [0115]-[0116], the core semantic acquisition processing may be added to the terminal device, so that the information source can be input into the combined semantic acquiring unit, and the extended semantic information and the core semantic information are output and obtained after the extended semantic and core semantic acquisition processing being performed (the extended semantic information and the core semantic information constitute the combined semantic information [0120], [0240]-[0242]); and performing training based on the training semantic information and the label semantic information to obtain the semantic conversion model ([0114]-[0116]).
Regarding claim 12, Tian further teaches wherein the common semantic extraction model is configured by a network device (i.e., As shown in FIG. 18, when a semantic acquiring unit is configured to acquire core semantic information, the semantic acquiring unit may be a core semantic acquiring unit. Output information of the core semantic acquiring unit includes core semantic information expressed by original information. As compared with the original information, the core semantic information is obtained from the original information with an aim to semantics that a receive end desires to receive. Through extraction of this part of core semantics, transmission of unnecessary information for the receive end can be avoided, thereby improving transmission efficiency [0223]-[0225]).
Regarding claim 13, Tian further teaches performing semantic conversion on the second semantic information based on a representation manner of the common semantic information to obtain the second semantic information (i.e., terminal device performs extended semantic acquisition processing on an information source to obtain extended semantic information [0102], [0127]-[0128]).
Regarding claim 15, Tian further teaches wherein the third semantic information and the second semantic information correspond to a same semantic level (i.e., the second information and the first information in the foregoing embodiment may be same information (for example, both the first information and the second information include only the semantic information [0149]).
Regarding claim 16, Tian teaches a communication apparatus, comprising at least one processor and at least one memory storing programming (typical feature of terminal device) instructions, wherein the at least one processor is coupled to the at least one memory and the processor is configured executes the programming instructions (typical feature/function of terminal device) to: obtain, by a first device, first semantic information corresponding to data (i.e., a terminal device performs semantic acquisition processing on an information source to obtain semantic information [0096]-[0097], [0127]-[0128]); convert, by the first device, the first semantic information into second semantic information, wherein the second semantic information belongs to common semantic information, and the common semantic information is a unified description of same semantics that are provided by different devices (i.e., the terminal device determines, based on the semantic information, first information to be transmitted [0098]-[0099]. Through extraction of the extended semantics, the extended semantic information is extracted from the original information, so that a case that a receive end (or a decoder) cannot acquire complete information can be avoided, thereby improving transmission accuracy and information integrity ([0111], [0122], [0132]). The device may reconstruct the original information source and then perform semantic acquisition again, or it may directly recover the semantic information from the transmitted data [0114]-[0117], [0130], data is converted from encoder format to decoder format (corresponding to converting) [0139]-[0144]); and send, by the first device, the second semantic information to a second device (i.e., the terminal device may send, to a receive end, the first information as sending information to be transmitted, and the receive end may be a network device (for example, a base station), or may be a terminal device serving as a receive end ([0100]-[0102], [0131]-[0132]-[0133]).
Regarding claim 17, Tian further teaches wherein the converting the first semantic information into second semantic information comprises: performing semantic conversion on the first semantic information based on a semantic conversion model to obtain the second semantic information (i.e., a first receiving unit, configured to receive second information; a second processing unit, configured to restore an information source from the second information; and a second semantic acquiring unit, configured to perform semantic acquisition processing on the information source to obtain semantic information [0024]-[0027], [0247-[0253]).
Regarding claim 18, Tian further teaches inputting training data into the semantic extraction model to obtain training semantic information (i.e., the information source can be input into the semantic acquiring unit, and the semantic information is output and obtained after the semantic acquisition processing [0097], [0108], [0115]-[0116], 0120]); inputting the training data into a common semantic extraction model to obtain label semantic information, wherein the label semantic information belongs to the common semantic information (i.e., information source is input into a first trained network model [0115]-[0116], the core semantic acquisition processing may be added to the terminal device, so that the information source can be input into the combined semantic acquiring unit, and the extended semantic information and the core semantic information are output and obtained after the extended semantic and core semantic acquisition processing being performed (the extended semantic information and the core semantic information constitute the combined semantic information [0120], [0240]-[0242]); and performing training based on the training semantic information and the label semantic information to obtain the semantic conversion model ([0115]-[0116]).
Regarding claim 19, Tian further teaches wherein the common semantic extraction model is configured by a network device (i.e., As shown in FIG. 18, when a semantic acquiring unit is configured to acquire core semantic information, the semantic acquiring unit may be a core semantic acquiring unit. Output information of the core semantic acquiring unit includes core semantic information expressed by original information. As compared with the original information, the core semantic information is obtained from the original information with an aim to semantics that a receive end desires to receive. Through extraction of this part of core semantics, transmission of unnecessary information for the receive end can be avoided, thereby improving transmission efficiency [0223]-[0225]).
Regarding claim 20, Tian further teaches wherein the second semantic information and the first semantic information correspond to a same semantic level (i.e., the second information and the first information in the foregoing embodiment may be same information (for example, both the first information and the second information include only the semantic information [0149]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tian (US 2024/0381059) in view of Guangming et al. (US CN112800247B).
Regarding claim 7, Tian further teaches all the limitations above except wherein the representation manner of the common semantic information comprises a semantic vector representation manner, a triplet representation manner, or a directed graph representation manner.
However, the preceding limitation is known in the art of communications. Guangming teaches A knowledge graph is a multiple graph structure composed of nodes (entities) and edges (relationships), and generally adopts a schemeTriple (subject, predicate, object). Applying a knowledge graph offers significant advantages for managing, analyzing, and scaling complex data systems. By organizing data into triples (subject, predicate, object), organizations can move past traditional silos to uncover hidden insights ([0044]). The knowledge-graph (common knowledge-graph library) shared by the semantic encoder and the semantic decoder may be stored locally at the semantic encoder and the semantic decoder. In addition, the shared knowledge-graph may also be stored at an edge-computing server or cloud-computing center near the semantic encoder or semantic decoder. In addition, the edge computing server or cloud computing center should provide a trained knowledge-graph search and recognition algorithm model in addition to storing the knowledge-graph ([0059]-[0061]). Therefore, it would have been obvious to one of ordinary skill in the art, at the time of the invention, to have implemented the technique of Guangming within the system of Tian in order to streamline data from diverse sources into a unified, reusable, and organization-wide semantic layer.
Regarding claim 14, Tian further teaches all the limitations above except wherein the representation manner of the common semantic information comprises a semantic vector representation manner, a triplet representation manner, or a directed graph representation manner.
However, the preceding limitation is known in the art of communications. Guangming teaches A knowledge graph is a multiple graph structure composed of nodes (entities) and edges (relationships), and generally adopts a schemeTriple (subject, predicate, object). Applying a knowledge graph offers significant advantages for managing, analyzing, and scaling complex data systems. By organizing data into triples (subject, predicate, object), organizations can move past traditional silos to uncover hidden insights ([0044]). The knowledge-graph (common knowledge-graph library) shared by the semantic encoder and the semantic decoder may be stored locally at the semantic encoder and the semantic decoder. In addition, the shared knowledge-graph may also be stored at an edge-computing server or cloud-computing center near the semantic encoder or semantic decoder. In addition, the edge computing server or cloud computing center should provide a trained knowledge-graph search and recognition algorithm model in addition to storing the knowledge-graph ([0059]-[0061]). Therefore, it would have been obvious to one of ordinary skill in the art, at the time of the invention, to have implemented the technique of Guangming within the system of Tian in order to streamline data from diverse sources into a unified, reusable, and organization-wide semantic layer.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN ALLAND GELIN whose telephone number is (571)272-7842. The examiner can normally be reached MON-FR 9-6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JINSONG HU can be reached at 571-272-3965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN A GELIN/Primary Examiner, Art Unit 2643
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