DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 02/28/2025.
Claims 1-20 are currently pending and have been examined.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-7 are directed to non-transitory computer readable medium (i.e., a manufacture), claims 8-13 are directed to a method (i.e., a process) and claims 14-20 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 8 includes limitations that recite an abstract idea. Note that independent claim 8 is the system claim, while claim 1 covers the matching computer readable medium and claim 14 covers a method claim.
Specifically, independent claim 8 recites:
A computer-implemented method, comprising:
accessing via at least one processor of a set of hardware processors that is associated with an electronic digital memory at a medical records computer system, a first feature-set of historical text elements and a second feature-set of historical contextual elements;
utilizing a machine learning engine to build a composite feature-set data structure that comprises the first feature-set of historical text elements and the second feature-set of historical contextual elements, the first feature-set of historical text elements differing from the second feature-set of historical contextual elements, wherein building the composite feature-set data structure corresponds at least partially to trimming a first portion of content relating to the composite feature-set data structure and augmenting a second portion of content relating to the composite feature-set data structure;
determining a plurality of clusters based on an electronic machine-learning clustering model and based further on the composite feature-set data structure, wherein the electronic machine- learning clustering model is trained to generate clusters representing historical text data and historical contextual data; and
electronically generating, via the at least one processor of the set of hardware processors and in response to generating the plurality of clusters via the electronic machine-learning clustering model, encoded data to a memory associated with the electronic digital memory at the medical records computer system, the encoded data indicating or corresponding to at least one text block of a plurality of text blocks associated with at least one cluster of the plurality of clusters.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because building a composite feature-set data structure that comprises the first feature-set of historical text elements and the second feature-set of historical contextual elements, the first feature-set of historical text elements differing from the second feature-set of historical contextual elements is equivalent to organizing clinical narrative notes, current/past data gathered during an encounter with a medical patient are all parts of a medical workflow and determines factually relevant information about the medical patient, which relate to managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mental process” because determining historical text elements differing from the second feature-set of historical contextual elements, indicating and corresponding text block clusters are equivalent to reviewing clinical notes, narratives and patient encounters, which are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper. The foregoing underlined limitations also relate to claim 8 (similarly to claims 1 and 14).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-7, 9-13 and 15-20, these claims merely recite determining steps such as: claims 2, 9 & 15 - the trimming of the first portion of content includes filtering a set of information associated with the composite feature-set data structure to reduce a dimensionality of the set of information, claims 3, 10 & 16 - the augmenting of the second portion of content includes supplementing the composite feature-set data structure with a third feature-set of elements, claims 4, 11 & 17 - the operations further comprise identifying a primary cluster in the plurality of clusters that is a best match to content associated with the composite feature-set data structure, claims 5, 12 & 18 - the operations further comprise assigning a relevance score, for at least a portion of the plurality of text blocks, corresponding to a respective relevance of at least a portion of the plurality of text blocks to information associated with at least a portion of the composite feature-set data structure, claims 6 & 1 - identifying a primary text block having a highest relevance score relative to a set of text blocks of the plurality of text blocks, and wherein the operations further comprise communicating the primary text block for display as a recommended selection for automatic population into at least a narrative field, of a healthcare related electronic document, configured to receive free-form narrative text, and claims 7, 13 & 20 - a primary cluster having a highest relevance score among a set of clusters of the plurality of clusters, and wherein the operations further comprise identifying a primary text block among text blocks in the primary cluster having a highest relevance score.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 8 and 14, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components. That is, other than reciting a system, one or more hardware processors, a medical records computer system, an electronic digital memory, and one or more non-transitory media having instructions that, when executed by one or more hardware processors to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the human mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system, one or more hardware processors, medical records computer system, electronic digital memory, and one or more non-transitory media having instructions that, when executed by one or more hardware processors are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “an electronic machine-learning clustering model”, the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see 2019 PEG and MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 8, regarding the additional limitations of the system, one or more hardware processors, medical records computer system, electronic digital memory, and one or more non-transitory media having instructions that, when executed by one or more hardware processors, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 8 and analogous independent claims 1 and 14 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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 1-5, 8-12 and 14-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang (US 2023/0377748 A1).
Claim 1:
Yang discloses one or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations, the operations (See Fig. 7, P0016-P0017 processor, memory and the non-transitory computer-readable medium with computer-readable program instructions.) comprising:
accessing via at least one processor of the one or more hardware processors that is associated with an electronic digital memory at a medical records computer system, a first feature-set of historical text elements and a second feature-set of historical contextual elements (Besides the clinical information from electronic health records (EHR), SOAP (Subjective, Objective, Assessment, and Plan) structure and notes 100 include history of present illness and symptoms, current medications, and allergies mentioned in P0030-P0031, P0061, shown in Fig. 1, serves as text elements, see Relations extracted from the SOAP note mentioned in P0051, shown in Fig. 3 including Patient-specific information 331 and Common medical knowledge 332 serves as text elements historical text elements.);
utilizing a machine learning engine to build a composite feature-set data structure that comprises the first feature-set of historical text elements and the second feature-set of historical contextual elements, the first feature-set of historical text elements differing from the second feature-set of historical contextual elements (Besides a supervised machine learning model trained based on the SOAP-structured EHR notes as a text to text generation natural language processing (NLP) application mentioned in P0034, see building concept graph from natural language processing in P0072-P0073 and [P0077] To build a concept graph, for instance at step 222 of FIG. 2, an embodiment first builds a patient specific information graph by extracting concept-relation-concept triples from text in the subjective and objective sections for assessment.), wherein building the composite feature-set data structure corresponds at least partially to trimming a first portion of content relating to the composite feature-set data structure and augmenting a second portion of content relating to the composite feature-set data structure (See P0096-P0097 where minimizing loss serves as trimming a portion of content relating to the composite feature-set data structure and augmenting. Besides the graph union 562 for graphing exemplary patient similar diagnosis codes in P0109-P0112, see the exemplary Concept Graph 330 shown in Fig. 3 mentioned in P0051, see Graph Transformer shown in Fig. 6, mentioned in P0119-P0120.);
determining a plurality of clusters based on an electronic machine-learning clustering model and based further on the composite feature-set data structure, wherein the electronic machine- learning clustering model is trained to generate clusters representing historical text data and historical contextual data (See exemplary similar patient clusters for meta-learning framework for graph-to-text applications mentioned in P0128-P0129.); and
electronically generating, via the at least one processor of the one or more hardware processors and in response to generating the plurality of clusters via the electronic machine-learning clustering model, encoded data to a memory associated with the electronic digital memory at the medical records computer system, the encoded data indicating or corresponding to at least one text block of a plurality of text blocks associated with at least one cluster of the plurality of clusters (See Input SOAP notes are mapped via MetaMap into exemplary Entities with corresponding Concept Unique Identifiers (CUIs) encoded data shown in Fig. 5, mentioned in P0080-P0081, P0108-P0112.).
Claim 8:
Yang discloses A computer-implemented method, comprising:
accessing via at least one processor of a set of hardware processors that is associated with an electronic digital memory at a medical records computer system, a first feature-set of historical text elements and a second feature-set of historical contextual elements (Besides the clinical information from electronic health records (EHR), SOAP (Subjective, Objective, Assessment, and Plan) structure and notes 100 include history of present illness and symptoms, current medications, and allergies mentioned in P0030-P0031, P0061, shown in Fig. 1, serves as text elements, see Relations extracted from the SOAP note mentioned in P0051, shown in Fig. 3 including Patient-specific information 331 and Common medical knowledge 332 serves as text elements historical text elements.);
utilizing a machine learning engine to build a composite feature-set data structure that comprises the first feature-set of historical text elements and the second feature-set of historical contextual elements, the first feature-set of historical text elements differing from the second feature-set of historical contextual elements (Besides a supervised machine learning model trained based on the SOAP-structured EHR notes as a text to text generation natural language processing (NLP) application mentioned in P0034, see building concept graph from natural language processing in P0072-P0073 and [P0077] To build a concept graph, for instance at step 222 of FIG. 2, an embodiment first builds a patient specific information graph by extracting concept-relation-concept triples from text in the subjective and objective sections for assessment.), wherein building the composite feature-set data structure corresponds at least partially to trimming a first portion of content relating to the composite feature-set data structure and augmenting a second portion of content relating to the composite feature-set data structure (See P0096-P0097 where minimizing loss serves as trimming a portion of content relating to the composite feature-set data structure and augmenting. Besides the graph union 562 for graphing exemplary patient similar diagnosis codes in P0109-P0112, see the exemplary Concept Graph 330 shown in Fig. 3 mentioned in P0051, see Graph Transformer shown in Fig. 6, mentioned in P0119-P0120.);
determining a plurality of clusters based on an electronic machine-learning clustering model and based further on the composite feature-set data structure, wherein the electronic machine- learning clustering model is trained to generate clusters representing historical text data and historical contextual data (See exemplary similar patient clusters for meta-learning framework for graph-to-text applications mentioned in P0128-P0129.); and
electronically generating, via the at least one processor of the set of hardware processors and in response to generating the plurality of clusters via the electronic machine-learning clustering model, encoded data to a memory associated with the electronic digital memory at the medical records computer system, the encoded data indicating or corresponding to at least one text block of a plurality of text blocks associated with at least one cluster of the plurality of clusters (See Input SOAP notes are mapped via MetaMap into exemplary Entities with corresponding Concept Unique Identifiers (CUIs) encoded data shown in Fig. 5, mentioned in P0080-P0081, P0108-P0112.).
Claim 14:
Yang discloses A system having one or more hardware processors configured to facilitate a plurality of operations (See Fig. 7, P0016-P0017 processor.) the operations comprising:
accessing via at least one processor of the one or more hardware processors that is associated with an electronic digital memory at a medical records computer system, a first feature-set of historical text elements and a second feature-set of historical contextual elements (Besides the clinical information from electronic health records (EHR), SOAP (Subjective, Objective, Assessment, and Plan) structure and notes 100 include history of present illness and symptoms, current medications, and allergies mentioned in P0030-P0031, P0061, shown in Fig. 1, serves as text elements, see Relations extracted from the SOAP note mentioned in P0051, shown in Fig. 3 including Patient-specific information 331 and Common medical knowledge 332 serves as text elements historical text elements.);
utilizing a machine learning engine to build a composite feature-set data structure that comprises the first feature-set of historical text elements and the second feature-set of historical contextual elements, the first feature-set of historical text elements differing from the second feature-set of historical contextual elements (Besides a supervised machine learning model trained based on the SOAP-structured EHR notes as a text to text generation natural language processing (NLP) application mentioned in P0034, see building concept graph from natural language processing in P0072-P0073 and [P0077] To build a concept graph, for instance at step 222 of FIG. 2, an embodiment first builds a patient specific information graph by extracting concept-relation-concept triples from text in the subjective and objective sections for assessment.), wherein building the composite feature-set data structure corresponds at least partially to trimming a first portion of content relating to the composite feature-set data structure and augmenting a second portion of content relating to the composite feature-set data structure (See P0096-P0097 where minimizing loss serves as trimming a portion of content relating to the composite feature-set data structure and augmenting. Besides the graph union 562 for graphing exemplary patient similar diagnosis codes in P0109-P0112, see the exemplary Concept Graph 330 shown in Fig. 3 mentioned in P0051, see Graph Transformer shown in Fig. 6, mentioned in P0119-P0120.);
determining a plurality of clusters based on an electronic machine-learning clustering model and based further on the composite feature-set data structure, wherein the electronic machine- learning clustering model is trained to generate clusters representing historical text data and historical contextual data (See exemplary similar patient clusters for meta-learning framework for graph-to-text applications mentioned in P0128-P0129.); and
electronically generating, via the at least one processor of the one or more hardware processors and in response to generating the plurality of clusters via the electronic machine-learning clustering model, encoded data to a memory associated with the electronic digital memory at the medical records computer system, the encoded data indicating or corresponding to at least one text block of a plurality of text blocks associated with at least one cluster of the plurality of clusters (See Input SOAP notes are mapped via MetaMap into exemplary Entities with corresponding Concept Unique Identifiers (CUIs) encoded data shown in Fig. 5, mentioned in P0080-P0081, P0108-P0112.).
Regarding claims 2, 9 and 15, Yang discloses the one or more non-transitory media of claim 1, the computer-implemented method of claim 8 and the system of claim 14, wherein the trimming of the first portion of content includes filtering a set of information associated with the composite feature-set data structure to reduce a dimensionality of the set of information (See P0096-P0097 where minimizing loss serves as trimming a portion of content including exemplary augmenting predicted, diagnosis and drug codes as filtering a set of information associated with the composite feature-set data structure.).
Regarding claims 3, 10 and 16, Yang discloses the one or more non-transitory media of claim 1, the computer-implemented method of claim 8 and the system of claim 14 wherein the augmenting of the second portion of content includes supplementing the composite feature-set data structure with a third feature-set of elements (Taught in P0062 as augmenting common medical knowledge data 443 shown in Fig. 3-4.).
Regarding claims 4, 11 and 17, Yang discloses the one or more non-transitory media of claim 1, the computer-implemented method of claim 8 and the system of claim 14 wherein the operations further comprise identifying a primary cluster in the plurality of clusters that is a best match to content associated with the composite feature-set data structure (See Fig. 4, P0089 item 442 an P0128-P0129 where clusters patients is based on similarity.).
Regarding claims 5, 12 and 18, Yang discloses the one or more non-transitory media of claim 4, the computer-implemented method of claim 11 and the system of claim 17 wherein the operations further comprise assigning a relevance score, for at least a portion of the plurality of text blocks, corresponding to a respective relevance of at least a portion of the plurality of text blocks to information associated with at least a portion of the composite feature-set data structure (See [P0089] the data 442 and ranking patient encounters by relevancy where a higher probability score represents a higher relevancy between a potential candidate patient encounter and original query patient encounter. Also, see identifying key medical phrases and similar subjective and objective sections as text blocks associated with feature-set data structure in P0108-P0110.).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-7, 13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 2023/0377748 A1) in view of Sadeghi (US 11,024,406 B2).
Regarding claims 6 and 19, although Yang discloses the one or more non-transitory media of claim 5 and the system of claim 18 mentioned above, Yang does not explicitly teach identifying and communicating a primary text block having a highest relevance score relative to a set of text blocks of the plurality of text blocks. Sadeghi teaches:
wherein the operations further comprise identifying a primary text block having a highest relevance score relative to a set of text blocks of the plurality of text blocks, and wherein the operations further comprise communicating the primary text block for display as a recommended selection for automatic population into at least a narrative field, of a healthcare related electronic document, configured to receive free-form narrative text (See Fig. 4-6, column 25, lines 30-61, column 26, lines 10-25 where the statistical model is used to score extracted concepts as blocks.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical error reporting before the effective filing date of the claimed invention to modify the system of Yang to include identifying and communicating a primary text block having a highest relevance score relative to a set of text blocks of the plurality of text blocks as taught by Sadeghi in order to maintain treatment efficacy and best practices mentioned in Sadeghi’s column 1, lines 19-27.
Regarding claims 7, 13 and 20, although Yang discloses the one or more non-transitory media of claim 1, the computer-implemented method of claim 8 and the system of claim 14 mentioned above, Yang does not explicitly teach identifying a primary text block among text blocks in the primary cluster having a highest relevance score. Sadeghi teaches:
wherein the at least one cluster corresponds to a primary cluster having a highest relevance score among a set of clusters of the plurality of clusters, and wherein the operations further comprise identifying a primary text block among text blocks in the primary cluster having a highest relevance score (See confidence score in column 18, lines 28-39 and likelihood score in column 25, lines 30-61, column 26, lines 10-25 where the statistical model is used to score extracted concepts as blocks.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical error reporting before the effective filing date of the claimed invention to modify the system of Yang to include identifying a primary text block among text blocks in the primary cluster having a highest relevance score as taught by Sadeghi in order to maintain treatment efficacy and best practices mentioned in Sadeghi’s column 1, lines 19-27.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.S.W./Examiner, Art Unit 3687 06/23/2026
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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