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 .
Status of Claims
This action is in reply to the present action filed on 9/15/2025.
Claims 1 has been amended
Claims 2-20 have been added.
Claims 1-20 are currently pending and have been examined.
Claim Objections
Claims 1, 11, and 17 are objected to for stating “similarly search”. Examiner will interpret this as “similarity search”. Appropriate correction is required
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 Analysis:
Independent Claims 1, 11, and 17 are within the four statutory categories. Claim 1, 11, and 17 are directed to a method, a system (i.e., a machine), and a non-transitory computer-readable storage medium (i.e., machine), respectively. Dependent Claims 2-10, 12-16, and 18-20 are further directed to the method, system, and non-transitory computer-readable medium, respectively, and therefore, the dependent claims also fall into one of the statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A method of phenotyping performed at a computing system, comprising:
receiving a request to identify a target population having a phenotype; and in response to the request: obtaining one or more predefined characteristics associated with the phenotype;
generating a phenotype embedding by vectorizing information of the request and the one or more predefined characteristics into a numerical format;
identifying, using a retriever model, a set of subjects as potential members of the target population by performing a similarly search between the phenotype embedding and a plurality of embeddings stored in a first set of one or more databases, the plurality of embeddings corresponding to patient records for a plurality of patients;
obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases;
providing, for each respective subject in the set of subjects, corresponding medical information of the medical information to an artificial intelligence (AI) component;
providing a set of natural language instructions to the Al component, wherein the set of natural language instructions provide a context to the Al component for natural language processing of the corresponding medical information to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics;
and obtaining, from the Al component, identification of a subset of subjects from the set of subjects determined by the Al component to be, or to have a high likelihood of being, a member of the target population through a determination by the Al component that each subject in the subset of subjects has at least one of the one or more predefined characteristics,
wherein, for each respective subject in the subset of subjects, the determination for at least one of the one or more predefined characteristics is made through natural language processing of corresponding medical information using the set of natural language instructions.
The series of limitations as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea of certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of receiving a request to identify a target population, obtaining predefined characteristics, generating a phenotype embedding by vectorizing information of the request and predefined characteristics into a numerical formation, identifying a set of subjects as potential members of the target population by performing a similarity search between the phenotype embedding and a plurality of embeddings stored in a database, obtaining medical information by searching a second set of one or more databases, providing medical information, providing a set of natural language instructions, and obtaining identification of a subset of subjects from the set of subjects determined to be or have a high likelihood of being a member of the target population), e.g., see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 3-10, 12-17, and 19-20 include other limitations directed toward the abstract idea. For example, Claim 3 recites the target population comprises a target cohort having a first medical condition or having experienced an outcome of interest, Claims 4 and 19 recite the plurality of embeddings corresponding to the patient records for the plurality of patients comprises a plurality of numerical vectors, and wherein the first set of one or more databases comprises one or more vector databases, Claims 5 and 20 recite each embedding in the plurality of embeddings corresponds to a respective chunk of data from the patient records, Claim 6 recites each embedding in the plurality of embeddings represents the respective chunk of data from the patient records and associated data, Claim 7 recites identifying the set of subjects includes obtaining respective identifiers for the set of subjects, and wherein the medical information is obtained using the identifiers, Claim 8 recites at least a portion of the medical information for a subject in the set of subjects is treated as unstructured, Claim 9 recites the set of natural language instructions provides a first context for interpreting a first portion of medical information for a subject in unstructured form and a different second context for interpreting a second portion of the medical information in unstructured form, Claim 10 recites the medical information for a subject includes first data in a first format and second data in a second format, Claim 12 recites obtaining a determination as to likelihood that the first subject includes the one or more predefined characteristics, by application of the medical information of the first subject to the plurality of parameters, Claim 13 recites the request provides a list of characteristics associated with the phenotype, Claim 14 recites predefined characteristics are obtained via a look-up table or through a search of a medical reference, Claim 15 recites the one or more predefined characteristics includes a first characteristic that is a predefined treatment regimen incurred and a second characteristic that is a biomarker status, Claim 16 recites a characteristic in the one or more predefined characteristics is treatment with a drug from the group consisting of sunitinib, lestaurtinib, midostaurin, crenolanib, gliteritinib, and sorafenib. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-20 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 3-10, 12-17, and 19-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 11, and 17.
Step 2A Analysis – Prong Two:
Claims 1, 11, and 17 are not integrated into a practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the computing system, retriever model, and AI component of Claim 1, the computing system, processor, memory, retriever model, and AI component of Claim 11, and the non-transitory computer-readable storage medium, computing system, retriever model, and AI component of Claim 17) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer components (see MPEP 2106.05(f)). For example, Applicant’s specification explains that the method 600 is performed at a computing system (e.g., a client device, server system, and/or service platform) having one or more processors (e.g., the CPUs 202 and/or 302) and memory (e.g., the memory 218 and/or 310). In some embodiments, the memory stores one or more programs configured for execution by the one or more processors (see Applicant’s specification, ¶ 0072). [A] prompt template is filled with the patient/medical information (obtained via a retriever model) and instructs the AI component in how to decide on the subject categorization [0103]. The computing system obtains (660), from the AI component, identification of a subset of patients from the set of patients [0081]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claims 1, 11, and 17 are directed to an abstract idea without practical application.
Dependent Claims 2, 6, 8, 12, and 18 also recite additional elements. Claims 2 and 18 recite the previously cited retriever model and a new additional element of a KNN algorithm and specify the retriever model identifies the set of subjects using a k-nearest neighbors (KNN) algorithm. Claim 6 recites each embedding in the plurality of embeddings represents the respective chunk of data from the patient records and associated metadata (new additional element). Claim 8 recites the AI component and specifies at least a portion of the medical information for a subject in the set of subjects is treated as unstructured by the AI component. Claim 12 recites the AI component and specifies the AI component comprises a plurality of parameters, and obtaining the identification of the subjects comprises inputting into the AI component the medical information of a first subject in the set of subjects thereby obtaining, as output form the AI component, a determination as to likelihood that the first subject includes the one or more predefined characteristics, by application of the medical information of the first subject to the plurality of parameters. However, these additional elements are described only at a high level of generality and are being used in their expected fashion, so these additional elements do not integrate the abstract idea into a practice application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, 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 a practical application, the additional elements of the computing system, retriever model, and AI component of Claim 1, the computing system, processor, memory, retriever model, and AI component of Claim 11, and the non-transitory computer-readable storage medium, computing system, retriever model, and AI component of Claim 17 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”).
Dependent Claims 3-5, 7, 9-10, do not recite any additional elements and further narrow the abstract idea. Claim 3 recites the target population comprises a target cohort having a first medical condition or having experienced an outcome of interest, Claims 4 and 19 recite the plurality of embeddings corresponding to the patient records for the plurality of patients comprises a plurality of numerical vectors, and wherein the first set of one or more databases comprises one or more vector databases, Claims 5 and 20 recite each embedding in the plurality of embeddings corresponds to a respective chunk of data from the patient records, Claim 7 recites identifying the set of subjects includes obtaining respective identifiers for the set of subjects, and wherein the medical information is obtained using the identifiers, Claim 9 recites the set of natural language instructions provides a first context for interpreting a first portion of medical information for a subject in unstructured form and a different second context for interpreting a second portion of the medical information in unstructured form, Claim 10 recites the medical information for a subject includes first data in a first format and second data in a second format, Claim 13 recites the request provides a list of characteristics associated with the phenotype, Claim 14 recites predefined characteristics are obtained via a look-up table or through a search of a medical reference, Claim 15 recites the one or more predefined characteristics includes a first characteristic that is a predefined treatment regimen incurred and a second characteristic that is a biomarker status, Claim 16 recites a characteristic in the one or more predefined characteristics is treatment with a drug from the group consisting of sunitinib, lestaurtinib, midostaurin, crenolanib, gliteritinib, and sorafenib.
Claim 12 recites previously cited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 12 recites the AI component and specifies the AI component comprises a plurality of parameters, and inputting into the AI component the medical information of a first subject in the set of subjects thereby obtaining, as output form the AI component, a determination as to likelihood that the first subject includes the one or more predefined characteristics, by application of the medical information of the first subject to the plurality of parameters.
Claims 2, 6, 8, and 18 recite new additional elements. Claims 2 and 18 recite the previously cited retriever model and a new additional element of a KNN algorithm and specify the retriever model identifies the set of subjects using a k-nearest neighbors (KNN) algorithm. Claim 6 recites each embedding in the plurality of embeddings represents the respective chunk of data from the patient records and associated metadata (new additional element). Claim 8 recites the AI component and specifies at least a portion of the medical information for a subject in the set of subjects is treated as unstructured by the AI component. However, these additional elements are described only at a high level of generality and are being used in their expected fashion, so these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception. Hence, Claims 2-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to a non-statutory subject matter.
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, 3-6, 8-12, 14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Peissig et al. (Peissig PL, et al. Importance of multi-modal approaches to effectively identify cataract cases from electronic health records. J Am Med Inform Assoc. 2012., hereinafter Peissig), in view of Crabtree et al. (US 20240386015 A1, hereinafter Crabtree) and Casale et al. (US 20230360758 A1, hereinafter Casale).
Regarding Claim 1, Peissig discloses the following:
A method of phenotyping performed at a computing system, comprising: (Peissig teaches multi-modal methods can be applied to a variety of studies beyond the current one (p. 7, ¶ 0007).)
obtaining one or more predefined characteristics associated with the phenotype; (Peissig teaches a multi-modal phenotyping strategy was applied to the EHR data and documents to identify information pertaining to nuclear sclerotic, posterior sub-capsular, and cortical (N-P-C) cataract subtypes, severity (numeric grading scale), and eye (p. 3, ¶ 0002).)
identifying… a set of subjects as potential members of the target population by performing a similarly search (Peissig teaches over 3.5 million documents for the PMRP cohort were pre processed using a pattern search mechanism for the term ‘cataract.’ (p. 3, ¶ 0002). The output from this process yielded a new string that was matched against a list of acceptable results. If after two iterations the string could not be matched, it was discarded. Otherwise, the result was loaded into a database, similar to the NLP results (p. 4, ¶ 0001).)
obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases; (Peissig teaches Fig. 2, EHR Text Documents. The EHR represents a valuable resource for obtaining a longitudinal record of a patient’s health and treatments through on-going interactions with a healthcare system. The health information obtained from those interactions can be stored in multiple formats within the EHR, which may vary between institutions and may present significant challenges when trying to retrieve and utilize the information for research (p. 7, ¶ 0006). The Examiner interprets an EHR as a database.)
and obtaining,…identification of a subset of subjects from the set of subjects determined …to be, or to have a high likelihood of being, a member of the target population through a determination …that each subject in the subset of subjects has at least one of the one or more predefined characteristics, wherein, for each respective subject in the subset of subjects, the determination for at least one of the one or more predefined characteristics is made through natural language processing of corresponding medical information… (Pessig teaches Figure 2 "Subjects with N-P-C subtypes (619)". This figure depicts the N-P-C subtype yield using two populations: (1) the left-most path of the figure denotes unique subject counts for the entire Personalized Medicine Research Project (PMRP) cohort; and (2) the right-most path denotes unique subject counts for the identified cataract cases. A hierarchical extraction approach was used to identify the N-P-C subtypes. If a subject had a cataract subtype identified by an ICD-9-CM code, natural language processing (NLP) or optical character recognition (OCR) was not utilized. Cataract subtypes identified using NLP had no subsequent OCR processing.)
… at least one of the one or more predefined characteristics… (Pessig teaches CDM was used to identify all subjects having a documented N-P-C subtype (ICD-9-CM codes 366.14e366.16) (p. 3, ¶ 0002). The Examiner interprets an N-P-C subtype as a predefined characteristic.)
Pessig does not disclose the following limitations met by Crabtree:
receiving a request to identify [information]; (Crabtree teaches the process begins at step 1801 when a distributed generative AI reasoning and action platform receives a user query directed to a generative AI system. The query may comprise a request for information, a summary, a request for a document,… [0386].)
and in response to the request: generating a phenotype embedding by vectorizing information of the request and the one or more predefined characteristics into a numerical format; (Crabtree teaches a query portal 710 may be configured to send received queries to an embedding model which can vectorize the received query and store it in a vector database [0267]. The platform computes numerical embedding representations of the input data in a given format. These embeddings capture the semantic properties and relationships of the data in a dense vector format, enabling efficient storage, retrieval, and comparison [0142].)
identifying, using a retriever model, a set of subjects as potential members …by performing a similarity search between the …embedding and a plurality of embeddings stored in a first set of one or more databases,… (Crabtree teaches the computed embeddings may then be persisted in memory or in a database such as a vector database, which allows for fast and scalable similarity search (e.g., cosine, dot product, Euclidean, etc.) and other vector operations or graph operations or hybrid representations depending on the data type, representation, and elements such as facts, spatial or temporal dynamics of the systems and/or entities of interest [0142]. Embeddings are dense vector representations that capture the semantic meaning and relationships of data points. Vector databases 2128 store and index these embeddings for efficient retrieval and similarity search [0144].)
providing, for each respective subject …, corresponding … information of the… information to an artificial intelligence (AI) component; (Crabtree teaches the persisted embeddings serve as input features for downstream ML or AI models, such as neural networks or symbolic reasoning engines, or knowledge bases [0142].)
providing a set of natural language instructions to the Al component, wherein the set of natural language instructions provide a context to the Al component for natural language processing…using the set of natural language instructions (Crabtree teaches the context data 301 is broken into chunks, passed through an embedding model 315, then stored in a specialized database called a vector database 320. Embedding models are a class of models used in many tasks such as natural language processing (NLP) to convert words, phrases, or documents into numerical representations (embeddings) that capture similarity which often correlates semantic meaning [0253]. Midserver 530 can be configured to periodically receive data (e.g., context data) and state information from each of the connected edge devices 510a-n. Midserver 530 may use this information to train/update the models 531, 532 [0261].)
…from the AI component,…by the AI component… (Crabtree teaches a recommendation AI system uses embeddings to represent user preferences and item characteristics. As users interact with the system, their feedback is used to iteratively refine the embeddings, making them more accurate predictors of user interests [0144].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate generating embeddings by vectorizing data and performing a similarity search on the embeddings using natural language processing and an artificial intelligence platform as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Pessig and Crabtree do not teach the following limitations met by Casale:
… to identify a target population having a phenotype; (Casale teaches an exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic data related to the phenotype obtained from a group of clinical subjects;…[0005].)
identifying, using a retriever model, a set of subjects as potential members of the target population by performing a similarly search …the plurality of embeddings corresponding to patient records for a plurality of patients; (Casale teaches the system may obtain biopsy embeddings from (some or all) H&E-stained liver biopsy images through the unsupervised learning procedure. The system may then perform an unsupervised analysis of baseline biopsy embeddings to identify clusters of patients based on biopsy embedding similarities. For each cluster, the system may determine or obtain a binary indicator vector across patients to indicate whether the patient is in the cluster or not. This binary phenotype can be used in downstream analyses [0448]. The model can be fit using biopsy data, including biopsy embeddings and the corresponding medical diagnosis scores [0269].)
providing, for each respective subject in the set of subjects, corresponding medical information of the medical information to an artificial intelligence (AI) component; (Casale teaches method of identifying a patient subgroup of interest comprises: inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space;…[0130]. Each medical image (e.g., histology image) has associated gene sequences from the human subject from whom the image was taken [0199].)
…of the corresponding medical information to determine whether a respective subject in the set of subjects has at least one of the one or more…characteristics;… (Casale teaches the data can indicate whether the subject has each of the plurality of genetic variants [0241]. The discovery platform can identify a relationship (e.g., a causal relationship) between the genetic variant of interest and the disease of interest such as NASH. The identified relationship can be used to determine the likelihood that a disease of interest will develop in a new subject or to more confidently diagnose the presence of the disease in a new subject given certain symptoms or the presence of other disease-associated factors [0191]. )
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate identifying a population with a phenotype and determining whether a subject has predefined characteristics as taught by Casale. This modification would create a method which can identify specific factors that may cause the subject to incur a disease (see Casale, ¶ 0003).
Regarding Claim 11, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies.
Further, Pessig discloses:
A computing system, comprising: one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: (Crabtree teaches a computing system for semantic search employing an advanced reasoning platform, the computing system comprising: one or more hardware processors configured for: automatically creating and updating ontologies by analyzing structured and unstructured data from multiple sources using natural language processing…[0030].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate a computing system as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 17, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies.
A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that, when executed by a computing system, cause the computing system to: (Crabtree teaches non-transitory, computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system employing an advanced reasoning platform for semantic search [0033].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate a non-transitory computer-readable medium as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 3, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig further discloses:
the target population comprises a target cohort having a first medical condition or having experienced an outcome of interest. (Pessig discloses clinically relevant information on conditions such as cataracts is buried within clinical notes or in scanned, hand written documents created during office visits, making this information difficult to extract (p. 1, ¶ 0002). We describe the construction and validation of a novel algorithm that combines multiple techniques and heuristics to identify subjects with age-related cataracts and the associated cataract attributes using only information available in the EHR (p. 1, ¶ 0003). The Examiner interprets cataracts as the condition of interest.)
Regarding Claim 4, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig further discloses:
…the patient records for the plurality of patients… (Pessig discloses the EHR represents a valuable resource for obtaining a longitudinal record of a patient’s health and treatments through on-going interactions with a healthcare system. The health information obtained from those interactions can be stored in multiple formats within the EHR, which may vary between institutions and may present significant challenges when trying to retrieve and utilize the information for research (p. 7, ¶ 0006).)
Pessig does not disclose the following limitations met by Crabtree:
the plurality of embeddings corresponding to the …records…comprises a plurality of numerical vectors, and wherein the first set of one or more databases comprises one or more vector databases. (Crabtree teaches embeddings are dense vector representations that capture the semantic meaning and relationships of data points. Vector databases 2128 store and index these embeddings for efficient retrieval and similarity search [0144].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate storing numerical vectors in a database as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 19, this claim recited limitations that are substantially similar to those recited in Claim 4 above; thus, the same rejection applies.
Regarding Claim 5, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig further discloses:
…data from the patient records. (Pessig discloses the EHR represents a valuable resource for obtaining a longitudinal record of a patient’s health and treatments through on-going interactions with a healthcare system. The health information obtained from those interactions can be stored in multiple formats within the EHR, which may vary between institutions… (p. 7, ¶ 0006).)
Pessig does not disclose the following limitations met by Crabtree:
each embedding in the plurality of embeddings corresponds to a respective chunk of data from the…records. (Crabtree teaches a method of using recursive/iterative clustering and chunk embeddings, combined with LLM chunk summarizations. Documents are broken into chunks, and passed through an LLM to create summarizations that act as a higher level representation of the underlying information chunk, and is added to the hierarchical tree. This tree is clustered to find similar information chunks based on the similarity of their embeddings [0236].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate embeddings corresponding to chunks of data as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 20, this claim recited limitations that are substantially similar to those recited in Claim 5 above; thus, the same rejection applies.
Regarding Claim 6, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 5 above. Pessig further discloses:
…data from the patient records… (Pessig discloses the EHR represents a valuable resource for obtaining a longitudinal record of a patient’s health and treatments through on-going interactions with a healthcare system. The health information obtained from those interactions can be stored in multiple formats within the EHR, which may vary between institutions… (p. 7, ¶ 0006).)
Pessig does not disclose the following limitations met by Crabtree:
each embedding in the plurality of embeddings represents the respective chunk of data from the…records and associated metadata. (Crabtree teaches the platform may generate semantic embeddings wherein each chunk and its summary are encoded into embeddings using the LLM. These embeddings capture the semantic meaning and contextual information of the chunks [0240]. Metadata, which provides additional context or information to data or systems, is increasingly being utilized to enhance the performance of deep learning systems, particularly in applications such as conversational agents [0150].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate embeddings corresponding to chunks of data and metadata as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 8, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig further discloses:
at least a portion of the medical information for a subject in the set of subjects (Pessig discloses the EHR represents a valuable resource for obtaining a longitudinal record of a patient’s health and treatments through on-going interactions with a healthcare system. The health information obtained from those interactions can be stored in multiple formats within the EHR, which may vary between institutions… (p. 7, ¶ 0006).)
Pessig does not disclose the following limitations met by Crabtree:
at least a portion of the…information for a subject in the set of subjects is treated as unstructured by the AI component (Crabtree teaches analyzing structured and unstructured data from multiple sources using natural language processing and machine learning or artificial intelligence techniques;…[0031].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate information being unstructured as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 9, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 8 above. Pessig does not disclose the following limitations met by Crabtree:
the set of natural language instructions provides a first context for interpreting a first portion of medical information for a subject in unstructured form and a different second context for interpreting a second portion of the medical information in unstructured form. (Crabtree teaches accurate and real-time inference based on ongoing rating of preferred data set and data format combinations (e.g., the same data might be purchased in unstructured, structured, schematized, normalized, or semantified formats)… [0234]. A system for semantic search employing an advanced reasoning platform, comprising one or more computers…cause the system to: automatically create and update ontologies by analyzing structured and unstructured data from multiple sources using natural language processing …process user queries and returning relevant results by leveraging the vector semantic indices, knowledge graphs, and contextual information; store and manage knowledge corpora that integrates information from ontologies, semantic indices, and external sources; utilize user context and preferences to guide semantic search and enable context-aware query interpretation and result personalization;…[0032].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate natural language instructions providing context to unstructured data as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 10, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig further discloses:
the medical information… (Pessig discloses the EHR represents a valuable resource for obtaining a longitudinal record of a patient’s health and treatments through on-going interactions with a healthcare system. The health information obtained from those interactions can be stored in multiple formats within the EHR, which may vary between institutions… (p. 7, ¶ 0006).)
Pessig does not disclose the following limitations met by Crabtree:
the…information for a subject in the set of subjects includes first data in a first format and second data in a second format. (Crabtree teaches accurate and real-time inference based on ongoing rating of preferred data set and data format combinations (e.g., the same data might be purchased in unstructured, structured, schematized, normalized, or semantified formats)… [0234]. A computing system…for: automatically creating and updating ontologies by analyzing structured and unstructured data from multiple sources…[0030].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate data being in different formats as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Regarding Claim 12, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 11 above. Pessig further discloses:
…obtaining…a determination as to likelihood that the first subject includes the one or more predefined characteristics, by application of the medical information of the first subject to the plurality of parameters. (Pessig discloses Fig. 2 "Subjects with N-P-C subtypes (619)". This figure depicts the N-P-C subtype yield using two populations: (1) the left-most path of the figure denotes unique subject counts for the entire Personalized Medicine Research Project (PMRP) cohort; and (2) the right-most path denotes unique subject counts for the identified cataract cases. A hierarchical extraction approach was used to identify the N-P-C subtypes. If a subject had a cataract subtype identified by an ICD-9-CM code, natural language processing (NLP) or optical character recognition (OCR) was not utilized. Cataract subtypes identified using NLP had no subsequent OCR processing.)
Pessig does not disclose the following limitations met by Crabtree:
the AI component comprises a plurality of parameters; (Crabtree teaches an LLM is the evolution of the language model concept in AI that dramatically expands the data used for training and inference. In turn, it provides a massive increase in the capabilities of the AI model. While there isn't a universally accepted figure for how large the data set for training needs to be, an LLM typically has at least one billion or more parameters [0184].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate the AI component having a plurality of parameters as taught by Crabtree. This modification would create a method which can bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Pessig and Crabtree do not teach the following limitations met by Casale:
and obtaining the identification of the subset of subjects comprises inputting into the AI component the medical information of a first subject in the set of subjects thereby obtaining, as output form the AI component, (Casale teaches a method of identifying a patient subgroup of interest comprises: inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings… generate one or more clusters of embeddings; identifying the one or more patient subgroups corresponding to the one or more clusters of embeddings; associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest [0130].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate obtaining an identification of subsets of subjects as taught by Casale. This modification would create a method which can identify specific factors that may cause the subject to incur a disease (see Casale, ¶ 0003).
Regarding Claim 14, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 11 above. Pessig further discloses:
the one or more predefined characteristics are obtained via a look-up table or through a search of a medical reference. (Pessig discloses a multi-modal phenotyping strategy was applied to the EHR data and documents to identify information pertaining to nuclear sclerotic, posterior sub-capsular, and cortical (N-P-C) cataract subtypes, severity (numeric grading scale), and eye. Over 3.5 million documents for the PMRP cohort were pre processed using a pattern search mechanism for the term ‘cataract.’ The strategy (figure 2) consisted of three methods to identify additional cataract attributes: CDM using coded data found in the DW, NLP used on electronic text documents, and OCR used on scanned image documents. CDM was used to identify all subjects having a documented N-P-C subtype (ICD-9-CM codes 366.14e366.16) (p. 3, ¶ 0002). The Examiner interprets EHR data as a medical reference.)
Claims 2, 7, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Peissig, Crabtree, and Casale in view of Mohtar et al. (US 20240038375 A1, hereinafter Mohtar).
Regarding Claim 2, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig, Crabtree, and Casale do not teach the following limitations met by Mohtar:
the retriever model identifies the set of subjects using a k-nearest neighbors (KNN) algorithm. (Mohtar teaches the natural language processing model may recognize text of the patient data and understand that such text is relevant for the contextual characteristics of a field. In various embodiments, the predictive model is any one of …K-Nearest Neighbor classification,…[0069].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate the use of a k-nearest model as taught by Mohtar. This modification would create a method which can identify relevant patient data(see Mohtar, ¶ 0003).
Regarding Claim 18, this claim recited limitations that are substantially similar to those recited in Claim 2 above; thus, the same rejection applies.
Regarding Claim 7, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 1 above. Pessig, Crabtree, and Casale do not teach the following limitations met by Mohtar:
identifying the set of subjects includes obtaining respective identifiers for the set of subjects, and wherein the medical information is obtained using the identifiers. (Mohtar teaches a method for improving medical compliance, the method comprising: obtaining an unfilled medical compliance form (MCF); identifying one or more fields for populating health data of a patient, the patient associated with a patient identifier; transmitting the patient identifier to an entity, wherein the entity has access to the patient identifier, an identity of the patient, and corresponding patient data of the patient; receiving, from the entity, the corresponding patient data of the patient…[0004].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate the patient data having identifiers for the subject as taught by Mohtar. This modification would create a method which can identify relevant patient data and assists with non-clinical tasks (see Mohtar, ¶ 0002-3).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Peissig, Crabtree, and Casale in view of Sharma et al. (US 20230170044 A1, hereinafter Sharma).
Regarding Claim 13, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 11 above. Pessig does not disclose the following limitations met by Crabtree:
the request provides… (Crabtree teaches the process begins at step 1801 when a distributed generative AI reasoning and action platform receives a user query directed to a generative AI system. The query may comprise a request for information, a summary, a request for a document,… [0386].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate a request as taught by Crabtree. This modification would bridge the gap between symbolic and non-symbolic representations to enable more advanced, contextual reasoning based on an understanding and domain expertise (see Crabtree, ¶ 0027).
Pessig, Crabtree, and Casale do not teach the following limitations met by Sharma:
…a list of characteristics associated with the phenotype. (Sharma teaches the first input corresponds to a form of information associated to the disease such as the name of disease, received by the processor to clearly indicate to the system about the specific disease for which the phenotypic targets are to be screened [0063]. [T]he genetic variation of the phenotypic targets may be for example, “TP53”, wherein “TP53” comprises the cumulative score of the phenotypic targets, and a list of drugs related to the genetic variation of the phenotypic targets…[0081].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate providing a list of characteristics associated with a phenotype as taught by Sharma. This modification would overcome the drawbacks associated with known techniques for screening of phenotypes and phenotypic targets associated to a disease (see Sharma, ¶ 0004).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Peissig, Crabtree, and Casale in view of Chen et al. (WO 2016004856 A1, hereinafter Chen).
Regarding Claim 15, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 11 above. Pessig and Crabtree do not teach the following limitations met by Casale:
…the one or more…characteristics includes …a second characteristic that is a biomarker status. (Casale teaches the phenotypic data comprises medical imaging data, histopathology data, clinical biomarker data, or genomic biomarker data [0007].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate characteristics being biomarker status as taught by Casale. This modification would create a method which can identify specific factors that may cause the subject to incur a disease (see Casale, ¶ 0003).
Pessig, Crabtree, and Casale do not teach a characteristic being a predefined treatment which is met by Chen:
the one or more predefined characteristics includes a first characteristic that is a predefined treatment regimen incurred… (Chen teaches the mouse is orally administered with a predetermined treatment, such as 10 mg of sorafenib per kilogram per day,…(p. 22, ¶ 0002).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate characteristics being a predefined treatment as taught by Chen. This modification would create a method which can utilize treatments that treat cancers effectively (see Chen, abstract).
Regarding Claim 16, Pessig, Crabtree, and Casale teach the limitations as seen in the rejection of Claim 11 above. Pessig, Crabtree, and Casale do not teach the following limitations met by Chen:
a characteristic in the one or more predefined characteristics is treatment with a drug from the group consisting of sunitinib, lestaurtinib, midostaurin, crenolanib, gliteritinib, and sorafenib. (Chen teaches the mouse is orally administered with a predetermined treatment, such as 10 mg of sorafenib per kilogram per day,…(p. 22, ¶ 0002).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for identifying a phenotype and identifying subjects determined to be have characteristics using natural language processing as disclosed by Pessig to incorporate characteristics being a treatment drug of sorafenib as taught by Chen. This modification would create a method which can utilize treatments that treat cancers effectively (see Chen, abstract).
Relevant Art Not Currently Being Applied
The following references are considered pertinent to Applicant’s disclosure but are not currently being applied:
Lee et al. (US 20230315856 A1) teaches a system for natural language processing using a machine learning model to generate natural language phrases similar to a reference phrase.
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1-20, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues the alleged “organizing human activity” category is inapplicable: there is no contract insurance, transaction, or interpersonal relationship management. The only “subjects” are EHR database entries processes autonomously by software. That aligns with the Office’s own SME Example 47 which was found to recite no abstract idea (see Applicant’s arguments, p. 8).
Regarding (a), Examiner respectfully disagrees. Claim 1 of Example 47 was found to not recite an abstract idea because the claim recites a specific circuit of a neural network and neurons and synaptic circuits which are specific hardware components. Conversely, the instant claims recite the abstract limitations of receiving a request, obtaining characteristics, generating an embedding by vectorizing information, performing a similarity search between embeddings, obtaining medical information from a database, and obtaining identifications of subjects that have a high likelihood of being in a target population using natural language instructions. It is important to note that the text within the parentheses stating social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that the October 2019 Update: Subject Matter Eligibility on p. 5 states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Applicant argues Step 2A Prong One fails because the claim language fails to confine every core operation to machine-implemented, data-intensive processing that cannot practically be performed in the human mind. The steps of “searching a first set of…databases”, “searching a second set of…databases”, and providing unstructured text to an AI model demand electronic indexing, embedding, and similarity search – tasks the human mind is not equipped to perform, squarely within the rationale of SRI v. Cisco and CyberSource distinguished in MPEP 2106.04(a)(2) (p. 8-9).
Regarding (b), Examiner respectfully disagrees. Examiner firstly notes that the claims are not identified as reciting a mental process or steps performed in the human mind. Furthermore, the AI model is not identified as being a part of the abstract idea. The steps of indexing data, embedding, and performing a similarity search are all steps which could be performed by a person following rules or instructions. It is important to note that the October 2019 Update: Subject Matter Eligibility on p. 5 states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Applicant argues the claim improves computer technology. Under Step 2A Prong 2, the additional elements apply any notional abstract concept to a concrete improvement in computer functionality, e.g., scalable, zero-shot phenotyping over heterogeneous medical databases. The claims cover a particular solution to a problem of cohort identification as described in [0003]-[0008] of the Application. Conventional phenotyping suffers from coding errors, data sparsity, and inability to process full-text notes at scale. The amended claims address these problems and provide a particular solution to a particular technological problem. Amended claim 1 is similarly patent eligible as claim 2 of the Office’s SME Example 48 (p. 9).
Regarding (c), Examiner respectfully disagrees. Examiner notes that the inability to process information at scale is not a technical problem. That is because the concept of phenotyping patient cohorts has existed since long before the advent of computer technology, and thus cannot properly be considered a technological improvement and/or an improvement to the computer itself.
Assuming, arguendo, that the problem was technical in nature, there are no details in the claims that indicate how the algorithm works such that it solves a problem in an unconventional manner. The specification does not provide any evidence as to how the algorithm determines if a group belongs to a target population (see Applicant’s specification ¶ 0012, ¶ 0098). MPEP 2106.05(f) states when determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Regarding rejections under 35 USC 103 to Claims 1-20, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues the Office Action relies on Peissig to allegedly teach providing medical information but relies on Wang to allegedly teach providing the set of natural language instructions for processing the medical information. However, the cited portion of Wang (Figure 5) does not teach or suggest "providing a set of natural language instructions to the AI component, wherein the set of natural language instructions provide a context to the AI component for natural language processing of the corresponding medical information to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics," recited by Claim 1. The cited portion of Wang shows a user interface for providing queries. Wang at pg. 6 ("Fig. 5. Sample results of the user interface using the query 'Does patient have any surgeries?' Retrieved results are shown at the population-level (left) and for an individual patient (right).") (p. 10-11).
Regarding (a), Examiner notes the reference Wang has been removed from the rejection of Claim 1 which is now rejected by Pessig in view of Crabtree and Casale.
Applicant argues claim 1 recites "performing a similarly search between the phenotype embedding and a plurality of embeddings stored in a first set of one or more databases" and "obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases." This aspect is not taught by the cited references. The Office Action relies on Peissig to allegedly teach this aspect. However, Peissig discloses a single EHR corpus (3.5 million documents in the PMRP warehouse) with the same corpus being searched to find patients and to mine their note content. Peissig, pg. 2-4 and Fig. 2. There is no second set of databases in Peissig. Wang does not cure this deficiency in Peissig. Wang likewise relies on one source: the notes of the currently viewed patient stored in the EHR. Wang at pg. 3. Therefore, the references, alone or in combination, fail to teach or suggest "performing a similarly search between the phenotype embedding and a plurality of embeddings stored in a first set of one or more databases" and "obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases," as recited by claim 1 (p. 11).
Regarding (b), Examiner respectfully disagrees. Firstly, Examiner notes that the similarity search function of the claims was not in the claims at the time of the Office Action mailed on 07/09/2025, and such limitation is now rejected by Pessig in view of Crabtree and Casale. Furthermore, there is nothing in the claims that specifies that the first and the second databases must be two different databases. Using broadest reasonable interpretation, the first database can be the same as the second database, as shown in Peissig.
Applicant argues claim 1 recites "receiving a request to identify a target population having a phenotype" and "in response to the request", performing actions to obtain identification of a subset of subjects. This aspect is not taught by the cited references. As the Office Action acknowledges, Peissig does not receive or respond to requests. Wang also fails to cure this deficiency in Peissig. Wang discloses a system to allow clinicians to ask natural questions to retrieve data from patient notes. Wang, Abstract. Accordingly, Wang discloses that the query-bot receives natural-language questions about a single patient, e.g., "Does patient have hypertension?" Wang, Table 1. Wang's system is patient- centric and does not involve phenotyping and does not receive or respond to a "request to identify a target population having a phenotype." (p. 11-12).
Regarding (c), Examiner notes the reference Wang has been removed from the rejection of Claim 1 which is now rejected by Pessig in view of Crabtree and Casale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
