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 .
Response to Amendment
In the amendment filed on November 13, 2025, the following has occurred: claim(s) 1, 3-4, 7-10, 13, 15-17, 19-20 have been amended. Now, claim(s) 1-20 are pending.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 2A Prong One
extracting a database schema and a data dictionary associated with a medical imaging database;
extracting object attributes associated with medical images stored in the medical imaging database, wherein the object attributes include data that is descriptive of the medical images and corresponding studies; and
generate a search query from a natural language request, wherein the search query is executed against the medical imaging database to identify content relevant to the natural language request
These limitations, as drafted given the broadest reasonable interpretation, but for the
recitation of generic computer components, encompass managing interactions between people, including following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the generic computer components of “one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:”, “a medical imaging database”, “wherein the trained LLM is used to…”, the claim recites a Certain Method of Organizing Human Activity. For example, the claim encompasses a user following instructions to extract a database schema and a data dictionary associated with a medical imaging database, a user following instructions to extract object attributes associated with medical images stored in the medical imaging database, and a user following instructions to generate a search query. These steps could be accomplished by a user following rules or instructions.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining element amounts to no more than general purpose computer components programmed to perform
the abstract idea, generally linking the use of an abstract idea to a particular technological environment or field of use.
Claim 1, directly or indirectly, recite the following generic computer components “one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:”, “a medical imaging database”, “wherein the trained LLM is used to…” are recited at a high degree of generality. As set forth in the MPEP 2106.05(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “training a large language model (LLM) to generate search queries from natural language requests, wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries”, and “wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 1: Step 2B
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Additionally, generally linking the abstract idea to a particular technological environment
does not amount to significantly more than the abstract idea (See MPEP 2106.05(h)).
Claims 2-9 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claim 2 further describes the generic computer component. Similarly, claim 3 further describes the object attributes and the instructions. Similarly, claim 4 further describes the search query. Similarly, claims 5-6 further describe the content relevant to the natural language request. Similarly, claims 7-9 describes the generic computer components and the extraction of information from medical imaging databases. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims 2-9 recite additional subject matter which amount to limitations
consisted with the additional elements in independent claim 1 (such as claims 7-9 recite
additional limitations that amount to generic computer components.) Looking at the limitations
as an ordered combination adds nothing that is not already present when looking at the elements
taken individually. There is no indication that the combination of elements improves the
functioning of a computer or improves any other technology. Their collective functions merely
provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application. The claims are not patent eligible.
Claims 10, 12-17, 19, and 20 recite the same functions as claim 1, 2-4, 6-7, 9, but in computer-implemented method form and one or more non-transitory computer-readable medium.
Claims 11 and 18 incorporate the abstract idea identified above and recite additional
limitations that expand on the abstract idea. For example, claim 11 further describes the object attribute. Finally, claim 18 describes instructions to extract a plurality of unstructured data documents, synthesizing pixel data to synthesize a content document, encoding each corresponding synthesized content document as a vector embedding using the trained second LLM, and generating a vector database. Therefore, these claims recite limitations
that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims 11 and 18 recite additional subject matter which amount to limitations
consisted with the additional elements in independent claim 10 (such as claim 18 recites additional limitations that amount to generic computer components.) Looking at the limitations
as an ordered combination adds nothing that is not already present when looking at the elements
taken individually. There is no indication that the combination of elements improves the
functioning of a computer or improves any other technology. Their collective functions merely
provide conventional computer implementation and do not impose a meaningful limit to
integrate the abstract idea into a practical application. The claims are not patent eligible.
Therefore, these claims also recite an abstract idea that falls into the Certain Methods of
Organizing Human Activity grouping of abstract ideas as explained above. The claims are not
patent eligible.
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.
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 1-5, 10-15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Arkoff et al. (U.S. Patent Publication No. 12,001,464) in view of Leary et al. (U.S. Patent Pre-Grant Publication No. 2024/0095463).
As per independent claim 1, Arkoff discloses a system comprising:
one or more processors (See col. 10, ll. 59-67, col. 11, ll. 1-4: The circuitry 202 may include one or more specialized processing units, which may be implemented as an integrated processor or a cluster of processors that perform the functions of the one or more specialized processing units, collectively); and
one or more memories storing processor-executable instructions that, when executed by the one or more processors (See col. 11, ll. 5-18: The memory 204 may include suitable logic, circuitry, interfaces, and/or code that may be configured to store the program instructions to be executed by the circuitry 202), cause the one or more processors to perform operations comprising:
extracting object attributes associated with medical images stored in the medical imaging database, wherein the object attributes include data that is descriptive of the medical images and corresponding studies (See col. 15, ll. 12-21, col. 18, ll. 8-19: The third data source may be associated with a patient and may correspond to medical data that may be obtained from medical records of the patient, which the Examiner is interpreting the medical data to encompass object attributes associated with medical images, and interpreting CT reports and pathology reports to encompass data that is descriptive of the medical images and corresponding studies); and
training a large language model (LLM) to generate search queries from natural language requests (See col. 18, ll. 20-60: The data generated or stored in the one or more MDG databases that may be utilized by the system to train the one or more LLMs, which the Examiner is interpreting the one or more MDG databases that may be utilized by the system to train the one or more LLMs to encompass training a LLM to generate search queries), wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries (See col. 8, ll. 52-67, col. 9, ll. 1-48: Each of the one or more LLMs may correspond to a sophisticated AI system trained on vast amounts of text data, and each of the one or more LLMs may learn to predict and generate text by analyzing patterns and relationships within the massive corpus of text, which the Examiner is interpreting the training on vast amounts of text data to encompass a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries),
wherein the trained LLM is used to generate a search query from a natural language request (See col. 14, ll. 4-25: The system may be configured to apply a NLP model on the metadata to determine the at least one keyword, which the Examiner is interpreting the metadata to determine the at least one keyword to encompass the trained LLM is used to generate a search query from a natural language request), wherein the search query is executed against the medical imaging database to identify content relevant to the natural language request (See col. 14, ll. 11-25: The circuitry may be configured to select the first MDG database of the one or more MDG databases based on the determined at least one keyword, which the Examiner is interpreting the first MDG database to encompass the medical imaging database, and interpreting each of the one or more MDG databases based on the determined at least one keyword to encompass identify content relevant to the natural language request.)
While Arkoff teaches the system as described above, Arkoff may not explicitly teach extracting a database schema and a data dictionary associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database.
Leary teaches a system for extracting a database schema and a data dictionary associated with a medical imaging database (See [0041]: A retrieval set tag can be used to specify a database, dictionary, library, or other set of data or facts to be used for a specific inferencing task associated with an endpoint, which the Examiner is interpreting a retrieval set tag can be used to specify a database or dictionary to encompass a database schema and a data dictionary associated with a medical imaging database), wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database (See [0029]: A data marshalling selection can be made that can specify how to map from input fields with specific types (e.g., for structured language protocols such as JSON or Protobuf) into, and out of, the text strings to be processed by the LLMs, which the Examiner is interpreting the specific types to encompass a structural representation of a logical and physical layout of the medical imaging database as the endpoints described may also include an indicator for the size of LLM to use for a request sent to this endpoint, as well as one or more associated inference parameters (e.g., number of samples or temperature).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff to include extracting a database schema and a data dictionary associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database as taught by Leary. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff with Leary with the motivation of providing computationally cost effective solutions (See Detailed Description of Leary in Paragraph [0021]).
Claim(s) 10 and 20 mirror claim 1 only within different statutory categories, and are rejected for the same reason as claim 1.
The addition of “wherein the medical imaging database maintains medical images and non-image data associated with a plurality of clinical studies” of claim 10 is encompassed by Arkoff in col. 15, ll. 12-21, col. 18, ll. 8-19: The third data source may be associated with a patient and may correspond to medical data that may be obtained from medical records of the patient, which the Examiner is interpreting the medical data to encompass object attributes associated with medical images, and interpreting CT reports and pathology reports to encompass non-image data associated with a plurality of clinical studies.
The addition of “wherein the search query is executed against the medical imaging database to identify content relevant to the natural language request” of claim 20 is encompassed by Arkoff in col. 6, ll. 5-19, col. 15, ll. 22-32: The system may extract, from the first MDG database, raw structured data associated with the determined metadata, which the Examiner is interpreting raw structured data to encompass the content relevant to the natural language request comprises structured data.)
As per claim 2, Arkoff/Leary discloses the system of claim 1 as described above. Arkoff may not explicitly teach wherein the medical imaging database is a picture archiving and communication system (PACS), and wherein the object attributes comprise digital imaging and communications in medicine (DICOM) tags.
Leary teaches a system wherein the medical imaging database is a picture archiving and communication system (PACS) (See [0110]: Machine learning models may be trained at facility using data (such as imaging data) generated at facility (and stored on one or more picture archiving and communication system (PACS) servers at facility), may be trained using imaging or sequencing data 1408 from another facility(ies), or a combination thereof, which the Examiner is interpreting the PACS server to encompass the medical imaging database is a PACS), and wherein the object attributes comprise digital imaging and communications in medicine (DICOM) tags See [0120]: A data augmentation service may further be included that may provide GPU accelerated data (e.g., DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing, scaling, and/or other augmentation, which the Examiner is interpreting the GPU accelerated data to encompass the object attributes comprise DICOM tags.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff to include the medical imaging database is a picture archiving and communication system (PACS), and wherein the object attributes comprise digital imaging and communications in medicine (DICOM) tags as taught by Leary. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff with Leary with the motivation of providing computationally cost effective solutions (See Detailed Description of Leary in Paragraph [0021]).
Claim(s) 12 mirrors claim 2 only within a different statutory category, and is rejected for the same reason as claim 2.
As per claim 3, Arkoff/Leary discloses the system of claim 1 as described above. Arkoff further teaches wherein the object attributes are stored in an auxiliary database (See col. 18, ll. 29-53: A set of data sources for obtaining medical data to be stored in one or more medical governance databases, which the Examiner is interpreting to encompass the object attributes are stored in an auxiliary database), wherein the processor-executable instructions further cause the system to determine a second database schema and a second data dictionary associated with the auxiliary database, and wherein the LLM is trained further using the second database schema and the second data dictionary (See col. 10, ll. 20-39: The one or more MDG databases may be utilized to train the one or more LLMs, and the training is iterative, which the Examiner is interpreting one or more MDG databases to encompass the second database schema and the second data dictionary (col. 7, ll. 35-51: Each MDG database is arranged systematically.)
Claim(s) 13 mirrors claim 3 only within a different statutory category, and is rejected for the same reason as claim 3.
As per claim 4, Arkoff/Leary discloses the system of claim 1 as described above. Arkoff further teaches wherein to generating the search query includes:
receiving the natural language request via a user input (See col. 10, ll. 10-22: The system may be configured to receive the user input including at least one search query to retrieve first medical data from one or more MDG databases); and providing the natural language request as an input to the trained LLM, wherein the trained LLM outputs the search query (See col. 10, ll. 23-39: The system may apply the one or more LLMs on the received at least one search query, and the system may determine metadata associated with the at least one search query based on the application of the one or more LLMs on the received search query, which the Examiner is interpreting the application of the one or more LLMs to encompass providing the natural language request as an input to the trained LLM.)
Claim(s) 14 mirrors claim 4 only within a different statutory category, and is rejected for the same reason as claim 4.
As per claim 5, Arkoff/Leary discloses the system of claim 1 as described above. Arkoff further teaches wherein the content relevant to the natural language request comprises at least one of medical images, structured data, or unstructured data associated with at least one study in the medical imaging database, wherein the instructions further cause the system to present the content relevant to the natural language request via a graphical user interface (GUI) (See Fig. 2 and col. 6, ll. 5-19, col. 15, ll. 22-32: The system may extract, from the first MDG database, raw structured data associated with the determined metadata, which the Examiner is interpreting raw structured data to encompass the content relevant to the natural language request comprises structured data., and a display device may be used.)
Claim(s) 15 mirrors claim 5 only within a different statutory category, and is rejected for the same reason as claim 5.
As per claim 11, Arkoff/Leary discloses the computer-implemented method of claim 11 as described above. Arkoff further teaches wherein the object attributes comprise at least one of: patient attributes including demographics, a medical state, and a medical history of a patient associated each of the plurality of clinical studies; study attributes indicative of an imaging procedure that was used to capture the medical images associated with each of the plurality of clinical studies (See col. 15, ll. 12-21, col. 18, ll. 8-19: The third data source may be associated with a patient and may correspond to medical data that may be obtained from medical records of the patient, which the Examiner is interpreting CT reports and pathology reports to encompass study attributes indicative of an imaging procedure that was used to capture the medical images associated with each of the plurality of clinical studies); or image attributes that describe each of the medical images and their associated acquisition parameters for each of the plurality of clinical studies
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Arkoff et al. (U.S. Patent Publication No. 12,001,464) in view of Leary et al. (U.S. Patent Pre-Grant Publication No. 2024/0095463) in further view of He et al. (U.S. Patent Pre-Grant Publication No. 2024/0362286).
As per claim 6, Arkoff/Leary discloses the system of claim 1 as described above. Arkoff/Leary may not explicitly teach wherein the content relevant to the natural language request is ranked according to relevance by the trained LLM.
He teaches a system wherein the content relevant to the natural language request is ranked according to relevance by the trained LLM (See [0131]: Ranking algorithms that take into account factors like term frequency and document relevance, which the Examiner is interpreting to encompass the claimed portion when combined with Arkoff/Leary.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff/Leary to include the content relevant to the natural language request is ranked according to relevance by the trained LLM as taught by He. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary with He with the motivation of improving search results for a user (See Detailed Description of He in Paragraph [0036]).
Claim(s) 16 mirrors claim 6 only within a different statutory category, and is rejected for the same reason as claim 6.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Arkoff et al. (U.S. Patent Publication No. 12,001,464) in view of Leary et al. (U.S. Patent Pre-Grant Publication No. 2024/0095463) in further view of Lehmann et al. (U.S. Patent Pre-Grant Publication No. 2024/0370464).
As per claim 7, Arkoff/Leary discloses the system of claim 1 as described above. Arkoff/Leary may not explicitly teach wherein the LLM is a first LLM, and wherein the operations further comprise:
training a second LLM to encode documents containing unstructured data as vector embeddings, wherein the second LLM is trained using a second training dataset comprising a plurality of text documents that are representative of unstructured data stored in the medical imaging database, wherein the trained second LLM is used to generate a second search query for the medical imaging database from the natural language request, wherein the second search query is used in conjunction with the search query to identify the content relevant to the natural language request.
Lehmann teaches a system wherein the LLM is a first LLM (See [0105]: Providing the embeddings to a large language model and receiving an identifier of the first cluster from the large language model), and wherein the operations further comprise:
training a second LLM to encode documents containing unstructured data as vector embeddings, wherein the second LLM is trained using a second training dataset comprising a plurality of text documents that are representative of unstructured data stored in the medical imaging database (See [0037]-[0038], [0051]: The cognition processing system is configured to receive unstructured content and process the unstructured input into different domains of knowledge, the LLM can be trained based on particular knowledge sets, which the Examiner is interpreting unstructured content to encompass unstructured data to encompass encode documents containing unstructured data as vector embeddings ([0052]), and interpreting the LLM can be trained based on particular knowledge sets to encompass a second training dataset comprising a plurality of text documents that are representative of unstructured data stored in the medical imaging database ([0112]), wherein the trained second LLM is used to generate a second search query for the medical imaging database from the natural language request, wherein the second search query is used in conjunction with the search query to identify the content relevant to the natural language request (See [0060]-[0061], [0070]: The classification engine may use a large language model (LLM) to extract key terms from the content using the various attention techniques, and a self-attention technique can be used to identify the relevant terms within the document, and a cross-attention technique can be used to compare the relevant terms to other taxonomies to identify a classification, which the Examiner is interpreting identify the relevant terms within the document to encompass identify the content relevant to the natural language request to encompass identify the content relevant to the natural language request when combined with Arkoff/Leary, and interpreting an attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors to encompass a second search query for the medical imaging database from the natural language request.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff/Leary to include the LLM is a first LLM, and wherein the operations further comprise: training a second LLM to encode documents containing unstructured data as vector embeddings, wherein the second LLM is trained using a second training dataset comprising a plurality of text documents that are representative of unstructured data stored in the medical imaging database, wherein the trained second LLM is used to generate a second search query for the medical imaging database from the natural language request, wherein the second search query is used in conjunction with the search query to identify the content relevant to the natural language request as taught by Lehmann. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary with Lehmann with the motivation of improve the performance of downstream tasks (See Detailed Description of Lehmann in Paragraph [0045]).
Claim(s) 17 mirrors claim 7 only within a different statutory category, and is rejected for the same reason as claim 7.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Arkoff et al. (U.S. Patent Publication No. 12,001,464) in view of Leary et al. (U.S. Patent Pre-Grant Publication No. 2024/0095463) in view of Lehmann et al. (U.S. Patent Pre-Grant Publication No. 2024/0370464) in further view of He et al. (U.S. Patent Pre-Grant Publication No. 2024/0362286).
As per claim 8, Arkoff/Leary discloses the system of claim 1 and Arkoff/Leary/Lehmann discloses the system of claim 7 as described above. Arkoff/Leary may not explicitly teach wherein the operations further comprise:
extracting a plurality of unstructured data documents from the medical imaging database;
encoding each of the plurality of unstructured data documents as a vector embedding using the trained second LLM;
and generating a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database.
Lehmann teaches a system wherein the operations further comprise:
extracting a plurality of unstructured data documents from the medical imaging database (See [0037]: The cognition processing system is configured to receive unstructured content and process the unstructured input into different domains of knowledge, which the Examiner is interpreting receive unstructured content to encompass the claimed portion);
encoding each of the plurality of unstructured data documents as a vector embedding using the trained second LLM (See [0044]: The encoder takes the input text and converts the input text into a sequence of hidden representations and captures the meaning of the text at different levels of abstraction.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff/Leary to include extracting a plurality of unstructured data documents from the medical imaging database, encoding each of the plurality of unstructured data documents as a vector embedding using the trained second LLM as taught by Lehmann. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary with Lehmann with the motivation of improve the performance of downstream tasks (See Detailed Description of Lehmann in Paragraph [0045]).
While Arkoff/Leary/Lehmann discloses the system as described above, Arkoff/Leary/Lehmann may not explicitly teach generating a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database.
He teaches a system to generating a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database (See [0110], [0130]-[0132]: The search manager may store the document vectors in a database, and index the document vectors into a searchable document index, which the Examiner is interpreting store the document vectors in a database to encompass a vector database, and interpreting the search query to encompass the second search query executed against the vector database.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff/Leary/Lehmann to include generating a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database as taught by He. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary/Lehmann with He with the motivation of improving search results for a user (See Detailed Description of He in Paragraph [0036]).
As per claim 9, Arkoff/Leary discloses the system of claim 1, Arkoff/Leary/Lehmann discloses the system of claim 7, and Arkoff/Leary/Lehmann/He discloses the system of claim 8 as described above. Arkoff/Leary/Lehmann may not explicitly teach wherein generating the second search query includes: receiving the natural language request via a user input; and providing the natural language request as an input to the trained second LLM, wherein the trained second LLM outputs a vector embedding of the natural language request as the second search query, and wherein the vector embedding of the natural language request is executed against the vector database using a similarity search.
He teaches a system wherein generating the second search query includes:
receiving the natural language request via a user input (See [0132]-[0133]: The search manager may receive a search query in a natural language representation); and
providing the natural language request as an input to the trained second LLM, wherein the trained second LLM outputs a vector embedding of the natural language request as the second search query, and wherein the vector embedding of the natural language request is executed against the vector database using a similarity search (See [0122], [0196]-[0197]: The search model may receive as input the information blocks of the electronic document and output contextualized embeddings corresponding to each of the information blocks to form a set of document vectors, which the Examiner is interpreting the information blocks to encompass the natural language request, and the semantically similar document content to encompass the natural language request is executed against the vector database using a similarity search.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Arkoff/Leary/Lehmann to include generating the second search query includes: receiving the natural language request via a user input; and providing the natural language request as an input to the trained second LLM, wherein the trained second LLM outputs a vector embedding of the natural language request as the second search query, and wherein the vector embedding of the natural language request is executed against the vector database using a similarity search as taught by He. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary/Lehmann with He with the motivation of improving search results for a user (See Detailed Description of He in Paragraph [0036]).
Claim(s) 19 mirrors claim 9 only within a different statutory category, and is rejected for the same reason as claim 9.
As per claim 18, Arkoff/Leary discloses the computer-implemented method of claim 10 and Arkoff/Leary/Lehmann discloses the computer-implemented method of claim 17 as described above. Arkoff/Leary may not explicitly teach further comprising:
extracting, by the one or more processors, a plurality of unstructured data documents from the medical imaging database;
synthesizing, by the one or more processors, pixel data for each medical image in a selected series to generate a corresponding synthesized content document;
encoding, by the one or more processors and using a large multimodal model (LMM), each corresponding synthesized content document as a vector embedding using the trained second LLM; and
generating, by the one or more processors, a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database.
Lehmann teaches a computer-implemented method further comprising:
extracting, by the one or more processors, a plurality of unstructured data documents from the medical imaging database (See [0037]: The cognition processing system is configured to receive unstructured content and process the unstructured input into different domains of knowledge, which the Examiner is interpreting receive unstructured content to encompass the claimed portion);
synthesizing, by the one or more processors, pixel data for each medical image in a selected series to generate a corresponding synthesized content document (See [0044]-[0045]: An embedding is a representation of a discrete object, such as a word, a document, or an image, as a continuous vector in a multi-dimensional space, an embedding captures the semantic or structural relationships between the objects, such that similar objects are mapped to nearby vectors, and dissimilar objects are mapped to distant vectors, which the Examiner is interpreting embedding captures the semantic or structural relationships between the objects to encompass pixel data for each medical image in a selected series to generate a corresponding synthesized content document);
encoding, by the one or more processors and using a large multimodal model (LMM), each corresponding synthesized content document as a vector embedding using the trained second LLM (See [0044]: The encoder takes the input text and converts the input text into a sequence of hidden representations and captures the meaning of the text at different levels of abstraction.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Arkoff/Leary to include extracting, by the one or more processors, a plurality of unstructured data documents from the medical imaging database; synthesizing, by the one or more processors, pixel data for each medical image in a selected series to generate a corresponding synthesized content document; encoding, by the one or more processors and using a large multimodal model (LMM), each corresponding synthesized content document as a vector embedding using the trained second LLM as taught by Lehmann. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary with Lehmann with the motivation of improve the performance of downstream tasks (See Detailed Description of Lehmann in Paragraph [0045]).
While Arkoff/Leary/Lehmann teaches the computer-implemented method as described above, Arkoff/Leary/Lehmann may not explicitly teach generating, by the one or more processors, a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database.
He teaches a computer-implemented method further comprising: generating, by the one or more processors, a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database (See [0110], [0130]-[0132]: The search manager may store the document vectors in a database, and index the document vectors into a searchable document index, which the Examiner is interpreting store the document vectors in a database to encompass a vector database, and interpreting the search query to encompass the second search query executed against the vector database.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Arkoff/Leary/Lehmann to include generating, by the one or more processors, a vector database that includes, for each of the plurality of unstructured data documents, the vector embedding and an identifier for an associated study in the medical imaging database, wherein the second search query is executed against the vector database as taught by He. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Arkoff/Leary/Lehmann with He with the motivation of improving search results for a user (See Detailed Description of He in Paragraph [0036]).
Response to Arguments
In the Remarks filed on November 13, 2025, the Applicant argues that the newly amended and/or added claims overcome the Claim Objection(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s). The Examiner acknowledges that the newly added and/or amended claims overcome the Claim Objection(s). However, the Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) the claimed invention improves the functioning of a computer at least by improving the computer's ability to generate a search query from a natural language request. To this end, claim 1 recites: "extracting a database schema and a data dictionary associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database" "extracting object attributes associated with medical images stored in the medical imaging database, wherein the object attributes include data that is descriptive of the medical images and corresponding studies," "training a large language model (LLM) to generate search queries from natural language requests, wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries," and "wherein the trained LLM is used to generate a search query from a natural language request, wherein the search query is executed against the medical imaging database to identify content relevant to the natural language request." Claims 10 and 20 recite similar features. Consequently, claims 1, 10, and 20 are patent eligible under 35 U.S.C. § 101 because any judicial exceptions allegedly recited by the claims are integrated into a practical application - namely, improving the functioning of a computer at least by improving the computer's ability to generate a search query from a natural language request;
(2) the claims are not obvious in view of the cited references at least because the cited references, even in combination, fail to disclose all of the features recited by the claims. That is, as discussed during the Interview, the cited references, even in combination, at least fail to disclose: "extracting a database schema ... associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database" or "wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries," as recited by claim 1. Claims 10 and 20 recite similar features. First, the cited references, even in combination, fail to disclose "extracting a database schema ... associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database," as recited by claim 1. The Office Action cites to para. [0041] of Leary in connection with this feature. Para. [0041] of Leary states: "A retrieval set tag can be used to specify a database, dictionary, library, or other set of data or facts to be used for a specific inferencing task associated with an endpoint." However, Leary does not disclose a database schema, let alone "extracting a database schema ... associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database," as recited by claim 1. The other cited references do not remedy the deficiencies of Leary with respect to this feature, nor are they cited for this purpose;
(3) the cited references, even in combination, fail to disclose "wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries," as recited by claim 1. The Office Action cites to Arkoff in connection with this feature. Arkoff generally states that "[e]ach of the one or more LLMs 106 may correspond to a sophisticated artificial intelligence (Al) system trained on vast amounts of text data," and "[e]ach of the one or more LLMs 106 may learn to predict and generate text by analysing patterns and relationships within the massive corpus of text they've been trained on." See Arkoff, column 7, lines 52-65. However, Arkoff fails to disclose that the "vast amounts of text data" or "massive corpus of text" include any of the specific data included in the training dataset recited by claim 1, such as a database schema, a data dictionary, object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries. Consequently, Arkoff fails to disclose "wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries," as recited by claim 1. The other cited references do not remedy the deficiencies of Arkoff with respect to this feature, nor are they cited for this purpose. Consequently, the cited references, even in combination, at least fail to disclose "extracting a database schema ... associated with a medical imaging database, wherein the database schema describes a structural representation of a logical and physical layout of the medical imaging database" or "wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries," as recited by claim 1. Claims 10 and 20 recite similar features. Accordingly, Applicant respectfully requests that the rejection of claims 1, 10, and 20, and their respective dependent claims, under 35 U.S.C. § 103 be withdrawn.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the Applicant’s newly amended claims improve the functioning of a computer as the Applicant’s newly amended claims are similar to “ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)” (See MPEP 2106.05(a)(I)), which the courts have indicated may not be sufficient to show an improvement in computer-functionality. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the newly amended claims are taught by the combination of prior art. The database schema is the structure of a database described in a formal language supported typically by a relational database management system, the Examiner maintains Leary’s disclosure in [0029]: “A data marshalling selection can be made that can specify how to map from input fields with specific types (e.g., for structured language protocols such as JSON or Protobuf) into, and out of, the text strings to be processed by the LLMs” and “An endpoint might also include an indicator for the size of LLM to use for a request sent to this endpoint, as well as one or more associated inference parameters (e.g., number of samples or temperature)”, which the Examiner has interpreted the specific types to encompass a structural representation of a logical and physical layout of the medical imaging database as the endpoints described may also include an indicator for the size of LLM to use for a request sent to this endpoint. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that Arkoff discloses “wherein the LLM is trained on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries”. The “object attributes” are defined in the Applicant’s Specification in [0039]: “Object attributes can include, for example, patient attributes relating to patient demographics, medical state, and history (e.g., tags from the patient medical module), study attributes that describe the imaging procedure that was performed (e.g., tags from the performed procedure step information), and image attributes that describe the images and their acquisition parameters (e.g., tags from the general series module and modality-specific tags, such as those in the CT Image Module).”, which, under broadest reasonable interpretation, is interpreted as metadata, which the LLMs of Arkoff can output (col. 6, ll. 34-35), and the LLMs of Arkoff are trained to predict and generate text by analysing patterns and relationships within the massive corpus of text they’ve been trained on (See col. 7, ll. 62-65), the Examiner maintains that Arkoff’s LLMs’ trained massive corpus of text would include metadata. Similarly, the LLMs of Arkoff are trained on a massive corpus of text, which the Examiner would maintain would include “a database schema, a data dictionary” as the LLMs are trained to search medical data governance databases (col. 6, ll. 20-25). The Examiner maintains that the massive corpus of text would also include “a prompt template comprising partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries” as the LLMs can be iteratively trained based on new user requests (col. 10, ll. 26-28). The 35 U.S.C. 103 rejection(s) stand.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683