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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is 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.
Claim 1 introduces “an output generator configured to output data,” then later introduces another “output generator configured to output data in “the phenotype engine.”
Applicant should refer to distinct claim elements in ways that distinguish the claim elements. In this case, because both “an output generator” and “data” are introduced twice, the claim is unclear.
“A collaboration platform” is also introduced twice.
The hardware elements introduced after “the reference database” and “the phenotype engine” are introduced twice.
After the language “the phenotype engine comprising a server; the server comprising a microprocessor, storage media, system memory, computer executable code, and networking hardware; the computer executable code configured to cause the microprocessor to implement the reference data processor, output generator, and collaboration platform,” the claim ends with a period.
However, there are several limitation after this period. This is not allowed. It is unclear when the claim actually ends.
Claim 5 introduces “a reference source.” However, this element has already been introduced in claim 1.
Claim 6 introduces “a server.” However, this element has already been introduced in claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (US Pre-Grant Publication 2023/0004574) in view of McNair et al. (US Patent 12,020,814).
As to claim 1, Hu teaches a phenotype engine comprising:
a reference data processor comprising:
a natural language processor, phenotype processor … and interoperability processor (see Hu paragraphs [0010] and [0037]. Hu shows a natural language processor that performs phenotyping, see [0037]. Hu shows an interoperability processor, [0071]);
the reference data processor configured to access a behavior database and an interoperability map (see Hu paragraph [0071]);
a reference database configured to receive processed data from the reference data processor (see Hu paragraph [0071], which shows exchanging data between medical systems. Also see Hu paragraphs [0036]-[0037], which shows processing and exchanging data);
said reference database comprising syntax data storage, phenotype data storage, deep learning data storage, and dimension data storage (see Hu paragraph [0037]. Database transactions, computations, and phenotype data are stored in a central repository. It is noted that none of the types of stored data appear to have claimed functions. They appear to be nonfunctional descriptive material);
an output generator configured to output data (see Hu paragraph [0037] and [0047]-[0048]);
said output generator comprising an information retriever, workflow support, pattern detector, predictive modeler, data visualizer, and user experience optimizer (see Hu paragraph [0037] and [0047]-[0048]. The output relies upon an ETL system and produces visual displays for a user of graph data);
the phenotype engine comprising:
an output generator configured to output data, an information retriever, a workflow support, pattern detector, predictive modeler, data visualizer, a user experience optimizer (see Hu paragraph [0037] and [0047]-[0048] and [0065]);
a code repository configured to receive output date from the output generator (see Hu paragraph [0037] and [0047]-[0048]);
a collaboration platform connected to an access platform (see Hu paragraphs [0039], [0041] and [0073]. Multiple users may use the system);
the collaboration platform and access platform configured to provide access to the output data to a user (see Hu paragraphs [0039], [0041] and [0073]. Also see paragraphs [0046]-[0047] for an output);
the interoperability processor configured to receive utilization and pattern data from the collaboration platform (see Hu paragraphs [0039], [0041] and [0073]. Users may input plans into the system);
the phenotype engine comprising a reference source, wherein the reference source is a computer database (see Hu paragraphs [0010] and [0037]);
the reference database comprising a microprocessor, storage media, system memory, computer executable code, and networking hardware (see Hu paragraphs [0091]-[0095] and [0099]);
said computer executable code configured to cause the reference source to provide information to a reference database processor (see Hu paragraphs [0010] and [0037]);
the phenotype engine comprising a server (see Hu paragraph [0092]);
the server comprising a microprocessor, storage media, system memory, computer executable code, and networking hardware (see Hu paragraphs [0091]-[0095] and [0099]);
the computer executable code configured to cause the microprocessor to implement the reference data processor, output generator, and collaboration platform (see Hu paragraphs [0091]-[0095] and [0099] for a microprocessor).
a code repository configured to receive output data from the output generator (see Hu paragraph [0037] and [0047]-[0048]);
a collaboration platform connected to an access platform (see Hu paragraphs [0039], [0041] and [0073]. Multiple users may use the system);
said collaboration platform and access platform configured to provide access to the output data to a user (see Hu paragraphs [0039], [0041] and [0073]. Also see paragraphs [0046]-[0047] for an output); and
the interoperability processor configured to receive utilization and pattern data from the collaboration platform (see Hu paragraphs [0037], [0039], [0041], and [0071]).
Hu does not teach:
an artificial intelligence data processor;
McNair teaches:
an artificial intelligence data processor (see McNair 23:65-20. McNair uses machine learning algorithms to support caregivers when analyzing data from multiple sources);
a collaboration platform connected to an access platform (see McNair 11:40-46 and 14:43-67 and 22:36-51. McNair supports multiple users in a platform for viewing data);
the collaboration platform and access platform configured to provide access to the output data to a user (see McNair 11:40-46 and 14:43-67 and 22:36-51)
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Hu by the teachings of McNair because both references are directed toward drawing data from multiple sources for analysis. McNair provides additional utility to Hu by providing improved support for users in a medical context, which will help various users to improve the lives of users (see McNair 12:19-38).
As to claim 2, Hu as modified by McNair teaches the phenotype engine of Claim 1 comprising a first reference source including a first set of terminology, ontology, and encoding data (see McNair 7:35-43, which shows that McNair relies upon multiple Electronic Health Record systems. McNair 8:6-38 for a description on how different EHRs have distinct vocabularies, ontologies, and encoding schemes), and
a first reference source including a second set of terminology, ontology, and encoding data (see McNair 7:35-43 and 8:6-38).
As to claim 3, Hu as modified by McNair teaches the phenotype engine of Claim 1 comprising a first reference source including a set of medical terminology, ontology, and encoding data (see McNair 7:35-43, which shows that McNair relies upon multiple Electronic Health Record systems. McNair 8:6-38 for a description on how different EHRs have distinct vocabularies, ontologies, and encoding schemes), and
a second reference source includes a set of laboratory terminology, ontology, and encoding data (see McNair 7:35-43 and 8:6-38).
As to claim 4, Hu as modified teaches the phenotype engine of Claim 1 wherein:
the behavior database comprises user data, input and behavior data, social network performance, and social network characteristics (see McNair 25:60-26:4. It is noted that these are merely non-functional descriptions of different data types being stored. No particular values of any of these types of data affect the functioning of the claimed subject matter. Because this limitation is directed towards non-functional descriptive material and because McNair teaches storing such data, this limitation is obvious);
the dimension data storage comprises common confirmed, fuzzy, and crisp dimension data (see McNair 25:60-26:4 for confirmed and crisp data. McNair also considers fuzzy data algorithms, see 19:4-57. It is noted that these are merely non-functional descriptions of different data types being stored. No particular values of any of these types of data affect the functioning of the claimed subject matter. Because this limitation is directed towards non-functional descriptive material and because McNair teaches storing such data, this limitation is obvious);
the syntax data storage comprises text annotation, indexing, winnowing, and accruing corpus characteristic data (see McNair 13:57-14:19. It is noted that these are merely non-functional descriptions of different data types being stored. No particular values of any of these types of data affect the functioning of the claimed subject matter. Because this limitation is directed towards non-functional descriptive material and because McNair teaches storing such data, this limitation is obvious);
the phenotype data storage comprises an integrated phenotype reference model (see Hu paragraphs [0010] and [0037]); and
the deep learning data storage comprises deep learning and machine learning data (see McNair 5:34-56).
As to claim 5, Hu as modified teaches the phenotype engine of Claim 1 comprising a reference source (see Hu paragraphs [0010] and [0037]);
wherein the reference source is a computer database containing a microprocessor, storage media, system memory, computer executable code, and networking hardware; (see Hu paragraphs [0091]-[0095] and [0099])
said computer executable code configured to cause the reference source to provide information to the reference data processor (see Hu paragraphs [0091]-[0095] and [0099]).
As to claim 6, Hu as modified teaches the phenotype engine of Claim 1 comprising a server (see Hu paragraph [0092]);
wherein the server comprises a microprocessor, storage media, system memory, computer executable code, and networking hardware; said computer executable code configured to cause the microprocessor to implement the reference data processor, output generator, and collaboration platform (see Hu paragraphs [0091]-[0095] and [0099]).
As to claim 7, Hu as modified teaches the phenotype engine of Claim 1 wherein:
the information retriever is configured to perform information retrieval and push processing (see Hu paragraph [0037] and [0047]-[0048]);
the workflow support is configured to provide clinical decision support and workflow support (see Hu paragraph [0037] and [0047]-[0048] and McNair 11:40-46 and 14:43-67 and 22:36-51);
the pattern detector is configured to provide pattern detection and anomaly detection (see McNair 25:28-42);
the predictive modeler is configured to generate spatiotemporal predicative models (see McNair 47:19-30);
the data visualizer is configured to provide data visualization and manipulation tools (see Hu paragraph [0037] and [0047]-[0048]); and
the user experience optimizer is configured to optimize user experience and provide collaboration tools (see Hu paragraph [0037] and [0047]-[0048] and McNair 11:40-46 and 14:43-67 and 22:36-51).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at 5712701760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2165
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