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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-12 and 14-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kano et al (US 20220301716).
As to claim 1, Kano discloses a data processing method, comprising:
obtaining a plurality of types of data, wherein all of the plurality of types of data have different sources and different data types (see [0055]-[0056] and [0105]);
performing a knowledge extraction on the plurality of types of data to obtain a knowledge graph, wherein the knowledge graph comprises a plurality of knowledge entities and an association relationship between the plurality of knowledge entities, the plurality of knowledge entities comprising different data types (FIG. 5 and [0057]-[0058]:patient graph 20B; see [0106]); and
performing a knowledge representation on each knowledge entity of the plurality of knowledge entities by using a knowledge representation algorithm corresponding to a data type of each knowledge entity, and initializing a weight of the association relationship between the plurality of knowledge entities in the knowledge graph, to obtain a vector graph to train an artificial intelligence (AI) task model (FIG. 6, [0068] and [0071]: feature vector 20D; see [0108]).
As to claim 2, Kano further discloses wherein the performing the knowledge extraction on the plurality of types of data to obtain the knowledge graph comprises:
performing the knowledge extraction on the plurality of types of data based on a plurality of knowledge levels, to obtain the knowledge graph of the plurality of knowledge levels (FIG. 2 and [0059], Symptom, Findings, Treatment, and Reaction are knowledge levels; see [0062], generates a patient graph 20B; see FIG. 7 and [0080], partial graph).
As to claim 3, Kano further discloses wherein there is an association relationship between knowledge entities from the plurality of knowledge levels, and the association relationship is obtained from the plurality of types of data, or the association relationship is obtained according to a preset rule (see FIG. 2 and [0059]: The medical care events are classified into four categories of the symptom, findings, treatment and reaction, and concrete medical care events are allocated to the respective nodes 21. The edges 22 represent the relationship between medical care events. When two medical care events have a relation, two nodes 21 corresponding to the two medical care events are connected by the edge 22. When two medical care events have no relation, two nodes 21 corresponding to the two medical care events are not connected by the edge 22).
As to claim 5, Kano further discloses wherein the AI task model is an AI model used for disease diagnosis (see [0067]: trained model 60), and the plurality of types of data comprise at least two of: medical record data, an image check report, a gene regulatory network, or a metabolic network (see [0037]).
As to claim 6, Kano further discloses the method further comprising:
training the AI task model based on the vector graph, to obtain a trained AI task model (see [0066] and [0098]).
As to claim 7, Kano further discloses wherein the training the AI task model based on the vector graph comprises: updating a weight in the vector graph (see [0107]-[0108]).
As to claim 8, Kano further discloses the method further comprising:
performing a task prediction by using the trained AI task model, to obtain a prediction result (see [0066]); and
identifying, based on an updated vector graph, at least one of a key knowledge entity or a key association relationship in a knowledge graph corresponding to the task prediction, to obtain an explainable knowledge graph (see [0047] and [0079]: visualization graph 81).
As to claim 9, Kano further discloses the method further comprising: outputting the explainable knowledge graph through a graphical user interface (GUI) (see FIG. 7).
As to claim 10, Kano discloses a computer device, comprising a processor coupled to a memory, the memory is configured to store instructions, and the processor execute the instructions to enable the processor to perform (FIG. 3 and [0043]):
obtaining a plurality of types of data, wherein all of the plurality of types of data have different sources and different data types (see [0055]-[0056] and [0105]);
performing a knowledge extraction on the plurality of types of data to obtain a knowledge graph, wherein the knowledge graph comprises a plurality of knowledge entities and an association relationship between the plurality of knowledge entities, the plurality of knowledge entities comprising different data types (FIG. 5 and [0057]-[0058]:patient graph 20B; see [0106]); and
performing a knowledge representation on each knowledge entity of the plurality of knowledge entities by using a knowledge representation algorithm corresponding to a data type of each knowledge entity, and initializing a weight of the association relationship between the plurality of knowledge entities in the knowledge graph, to obtain a vector graph to train an artificial intelligence (AI) task model (FIG. 6, [0068] and [0071]: feature vector 20D; see [0108]).
As to claims 11-12 and 14-18, claims 11-12 and 14-18 recite the same features as those recited in method claims 2-3 and 5-9, respectively, and are therefore rejected for the same reasons as used above in rejecting claims 2-3 and 5-9.
As to claim 19, Kano discloses a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor (see [0043]), cause the processor to perform:
obtaining a plurality of types of data, wherein all of the plurality of types of data have different sources and different data types (see [0055]-[0056] and [0105]);
performing a knowledge extraction on the plurality of types of data to obtain a knowledge graph, wherein the knowledge graph comprises a plurality of knowledge entities and an association relationship between the plurality of knowledge entities, the plurality of knowledge entities comprising different data types (FIG. 5 and [0057]-[0058]:patient graph 20B; see [0106]); and
performing a knowledge representation on each knowledge entity of the plurality of knowledge entities by using a knowledge representation algorithm corresponding to a data type of each knowledge entity, and initializing a weight of the association relationship between the plurality of knowledge entities in the knowledge graph, to obtain a vector graph to train an artificial intelligence (AI) task model (FIG. 6, [0068] and [0071]: feature vector 20D; see [0108]).
As to claim 20, claim 20 recites the same features as those recited in method claim 2 and is therefore rejected for the same reasons as used above in rejecting claim 2.
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.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kano et al (US 20220301716) in view of Yuan (US 20210375479).
As to claims 4 and 13, Kano fails to explicitly disclose wherein the performing the knowledge representation on each knowledge entity by using the knowledge representation algorithm corresponding to the data type of each knowledge entity comprises:
determining, from a preset algorithm library based on the data type of each knowledge entity and a preset relationship, the knowledge representation algorithm corresponding to the data type of the knowledge entity, and performing the knowledge representation on the knowledge entity based on the corresponding knowledge representation algorithm, to obtain a representation vector corresponding to the knowledge entity; or determining, based on the data type of each knowledge entity, the knowledge representation algorithm corresponding to the data type and input by a user, and performing the knowledge representation on the knowledge entity based on the corresponding knowledge representation algorithm, to obtain a representation vector corresponding to the knowledge entity.
However, Yuan teaches determining, from a preset algorithm library based on the data type of each knowledge entity and a preset relationship, the knowledge representation algorithm corresponding to the data type of the knowledge entity, and performing the knowledge representation on the knowledge entity based on the corresponding knowledge representation algorithm, to obtain a representation vector corresponding to the knowledge entity; or determining, based on the data type of each knowledge entity, the knowledge representation algorithm corresponding to the data type and input by a user, and performing the knowledge representation on the knowledge entity based on the corresponding knowledge representation algorithm, to obtain a representation vector corresponding to the knowledge entity (see [0046]: NNLM (neural network language model), word2vec, glove, ELMo, etc.; [0092]-[0094]: S303, acquiring natural text representation data corresponding to the electronic medical record and patient information representation data corresponding to the electronic medical record … such as chief complaint information, current medical history information, physique examination information, and auxiliary examination information … the natural text information and the patient information in the electronic medical record are respectively input into a neural network pre-obtained by training, to obtain natural text representation data corresponding to the electronic medical record and patient information representation data corresponding to the electronic medical record).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Kano using Huan’s teachings to include determining, from a preset algorithm library based on the data type of each knowledge entity and a preset relationship, the knowledge representation algorithm corresponding to the data type of the knowledge entity, and performing the knowledge representation on the knowledge entity based on the corresponding knowledge representation algorithm, to obtain a representation vector corresponding to the knowledge entity; or determining, based on the data type of each knowledge entity, the knowledge representation algorithm corresponding to the data type and input by a user, and performing the knowledge representation on the knowledge entity based on the corresponding knowledge representation algorithm, to obtain a representation vector corresponding to the knowledge entity in order to improve the accuracy of expressing symptom information in electronic medical records and to make a final disease prediction result more accurate (Yuan; [0032]).
Conclusion
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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