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 response to the reply filed 2/10/2026.
Claims 1-3, 5, 9, 11-12, 14, 17-18 and 20 were amended 2/10/2026.
Claims 1-20 are currently pending and have been examined.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are drawn to a method and a system which are statutory categories of invention (Step 1: YES).
Independent claims 1 and 11 recite: receiving, a user identifier and a prompt related to an identified clinical task; determining, based on an access level associated with the user identifier, a first set of task-specific components to which the user identifier has access, wherein each task-specific component in the first set of task-specific components has corresponding permission data, and wherein determining the first set of task-specific components comprises comparing the access level associated with the user identifier with the permission data; selecting, to select from among the first set of task-specific components, a task-specific component from among the first set of task-specific components based on the prompts communicatively coupling the task-specific component based on the prompt, wherein selecting the task-specific component comprises identifying, an intent from the prompt and selecting the task-specific component in accordance with the identified intent; providing the prompt to the task-specific component; receiving a response to the prompt, wherein the response is generated by the task-specific component using information; and providing the response to a user.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between a user and a patient, as reflected in the specification, which states that “the platform may be configured to monitor an electronic health record (EHR) to identify care gaps and/or reminders to physicians to take action with a respective patient. In this way, the platform may serve as a docket manager for physicians that identifies issues/events the physicians didn't manually docket to ensure patients and other subjects get timely care. The platform may also be configured to track and/or catalog relevant therapies (e.g., on label and/or off label use) for a set of disease states. The platform may also track and/or catalog relevant clinical trials (e.g., in multiple countries and/or from multiple authorities) for a set of disease states.” (see: specification paragraph 58). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “it is extremely challenging for an oncologist to be able to manually analyze the medical records and outcomes of thousands or millions of cancer patients each time they want to make a specific treatment recommendation regarding a particular patient they are treating.” (see: specification paragraph 4). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “user interface”, “computing device”, “set of databases”, “machine-learning model”, “database”, “control circuitry”, “memory”, “computing system”, “non-transitory computer-readable storage medium” are recited at a high level of generality (e.g., that the determining and providing is performed using generic computer components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2A, Figure 3 and
Paragraph 63, where “In some embodiments, a client device 102 is associated with one or more users. In some embodiments, each user is separately authenticated (e.g., assigned distinct/unique authentication tokens). In some embodiments, a client device 102 is a personal computer, mobile electronic device, wearable computing device, laptop computer, tablet computer, mobile phone, feature phone, smart phone, a speaker, television (TV), and/or any other electronic device capable of interacting with a user (e.g., an electronic device having an I/O interface). The client device(s) 102 may communicatively couple to other components of the platform 100 wirelessly and/or through a wired connection (e.g., directly through an interface, such as an HDMI interface).”
Paragraph 67, where “The client device 102 includes one or more central processing units (CPUs) 202, a user interface 204, one or more network ( or other communications) interfaces 214, memory 218, and one or more communication buses 217 for interconnecting these components. In some embodiments, the client device 102 includes a processor or other control circuitry (e.g., in addition, or alternatively, to the CPUs 202).”
Paragraph 69, where “The user interface 204 includes output device(s) 206 and input device(s) 212. h1 some embodiments, the input device(s) 212 include a keyboard, mouse, a track pad, and/or a touchscreen. In some embodiments, the user interface 204 includes a display device that includes a touch-sensitive surface, in which case the display device is a touch-sensitive display. In client devices that have a touch-sensitive display, a physical keyboard is optional (e.g., a soft keyboard may be displayed when keyboard entry is needed). h1 some embodiments, the output device(s) 206 include a speaker and/or a connection port for connecting to speakers, earphones, headphones, or other external listening devices. In some embodiments, the input device(s) 212 include a microphone and/or voice recognition device to capture audio (e.g., speech from a user).”
Paragraph 71, where “The memory 218 includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices; and may include nonvolatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory 218 optionally includes one or more storage devices remotely located from the CPU(s) 202. The memory 218, or alternately, the non-volatile memory solid-state storage devices within the memory 218, includes a non-transitory computer-readable storage medium.”
Paragraph 179, where “a user interface element 1818B for interacting with a task-specific orchestration that includes a machine-learning model (e.g., a general-purpose machine-learning model and/or a task-specific machine learning model) that has been trained with specific data (e.g., from a data collection that is continuously updated in real-time)”
Paragraph 54, where “In some embodiments, each task-specific orchestration (or agent) may include one or more machine-learning models, such as a language model trained and/or fine-tuned on a particular domain.”
Paragraph 205, where “The method 2100 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. At least some of the operations shown in Figure 21 correspond to instructions stored in a computer memory or a computer-readable storage medium. In some embodiments, the computing system is the platform 100, the client device(s) 102, and/or the server system 106.”
Paragraph 333, where “In some embodiments of B 1, the one or more databases includes one or more of: a clinical database, a therapies database, and a medical database (e.g., the database(s) 400). In some embodiments, the one or more databases include one or more datasets and/or data collections (e.g., document collections). In some embodiments, the one or more documents store multiple types of documents (e.g., text documents, images, audio files, etc.).”
Paragraph 434, where “the set of databases comprises one or more databases storing data owned by the user”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-10, 12-16 and 18-20 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 2-10, 12-16 and 18-20 recite receiving, selecting, authenticating, and storing task-specific and patient data on the generic machine learning models and on the generically recited computing device as shown in the parent claims above.
Claim 2 further recites “a second set of databases” and “a second database” which is recited at a high level of generality (e.g., that the selecting and receiving are performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 431. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 5 further recites “one or more databases” which is recited at a high level of generality (e.g., that the storing of data is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 292. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 6 further recites “a super agent module” which is a machine learning model recited at a high level of generality (e.g., that the analyzing of data using a generic machine learning model implemented on generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 192. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 7 further recites “an interconnected node architecture” which is a component of a generic machine learning model recited at a high level of generality (e.g., that the analyzing of data using a generic machine learning model implemented on generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 391. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crafts JR (US 2017/0124263 A1) (herein referred to as Crafts) in view of Jarrett (US 2019/0108313 A1).
CLAIM 1-
Crafts teaches:
receiving, at a user interface of a computing device, a user.. and a prompt related to an identified clinical task; (Crafts teaches a user interface of a computing device that receives data including a log in of a user and a patient attribute (i.e., prompt as defined in the specification paragraph 441) queried to identify a clinical task (para [0033, 0048, 0123, 0117, 0124]))
determining, based on an access level associated with the user…, a first set of task-specific components and a first set of databases to which the user …has access; (Crafts teaches that the user is a medical professional that has access databases and tasks to access on a specific interface whether they are the clinician or the patient (the specification paragraphs 256 and 250 recites that the agent modules (task-specific components related to recommendations) are determined based on access level) and the recommendations that the medication professional has access to is based on their tasks) (para [0098, 0036, 0028, 0108, 0110, 0115, 0124]))
wherein each task-specific component in the first set of task-specific components has corresponding permission data, and wherein determining the first set of task-specific components comprises comparing the access level associated with the user… with the permission data (Crafts teaches that the system grants access based on the type of user (clinician has higher levels of access than a patient and researchers have the highest levels of access) and visualizes the workflow tasks based on the specific user (the specification paragraphs 256 and 250 recites that the agent modules (task-specific components related to recommendations are determined based on access level) and the recommendations that the medication professional has access to is based on their tasks (para [0115, 0108,0109]))
selecting, by a machine-learning model trained to select from among the set of task-specific components, a task-specific component from among the set of task-specific components based on the prompt; wherein selecting the task-specific component comprises identifying, by the machine-learning model, an intent from the prompt and selecting the task-specific component in accordance with the identified intent (Crafts teaches that the machine learning model is trained based on past treatment plans that have created predefined workflows with specific tasks that the clinician can select and the query of the system determines the risk models and the tasks based on those risk assessments which is based on the prompt determined by the machine learning model (which is how the queries are recited in paragraphs 257 and 289 of the specification) (para [0032-0036, 0124, 0094, 0063, 0089, 112]))
communicatively coupling the first task-specific component to a database from the set of databases based on the prompt; providing the prompt to the task-specific component; (Crafts teaches that the tasks in the workflow are determined based on the type of query and can be implemented across multiple databases that are relevant to the query and update the task based on the query (para [0063, 0086-87, 0040, 0124]))
receiving a response to the prompt, wherein the response is generated by the task-specific component using information from the database; and providing the response to a user (Crafts teaches that a response is generated to the user to visualize the workflow tasks that are needed for the specific patient that is queried using the databases (para [0095, 0032-0036, 0124, 0094, 0063, 0085]), Figure 16)
Crafts teaches identifying users based on login credentials, but does not explicitly teach, however Jarrett teaches:
user identifier (Jarrett teaches using an authorization of a clinician to access the databases using an authentication token (i.e., user identifier uses authorization token as defined in the specification paragraph 433) (para [0062, 0064]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the clinical workflow system of Crafts to integrate the application of providing authorization tokens of users of Jarrett with the motivation of ensuring security of transmission of patient data (see: Jarrett, paragraph 61).
CLAIM 2-
Crafts in view of teaches the limitations of claim 1. Regarding claim 2, Crafts further teaches:
receiving a second user …and the prompt related to the identified clinical task; (Crafts teaches the patient user can indicate that an assigned task has been completed (para [0098, 0036, 0028, 0108, 0110, 0115, 0124]))
determining a second set of task-specific components and a second set of databases to which the second user …has access; wherein the second set of task-specific components includes one or more task-specific components not in the first set of task-specific components, and wherein the second set of databases includes one or more databases not in the first set of databases (Crafts teaches that multiple users including a user is a medical professional and a user that is a researcher that has access to multiple databases and multiple different tasks to access on a specific interface whether they are the clinician, researcher or the patient based on tasks in their workflow and the databases can include a medical record database that can convert the data (i.e., different database that only transfers data when converted) (para [0098, 0036, 0028, 0108, 0110, 0115, 0124, 0036]))
selecting, by the machine-learning model, a second task-specific component from among the second set of task-specific components based on the prompt; (Crafts teaches that the machine learning model is trained based on past treatment plans that have created predefined workflows with multiple specific tasks that the clinician can select (para [0032-0036, 0124, 0094, 0063]))
communicatively coupling the second task-specific component to a second database from the second set of databases based on the prompt; providing the prompt to the second task-specific component; (Crafts teaches that the tasks in the workflow are determined based on the type of query and can be implemented across multiple databases that are relevant to the query and update the task based on the query (para [0063, 0086-87, 0040, 0124]))
and receiving a second response to the prompt, wherein the second response is generated by the second task-specific component using information from the second database (Crafts teaches that multiple responses are generated to the user to visualize the workflow tasks that are needed for the specific patient that is queried using the multiple databases (para [0095, 0032-0036, 0124, 0094, 0063, 0085]), Figure 16)
Crafts teaches identifying users based on login credentials, but does not explicitly teach, however Jarrett teaches:
user identifier for a second user (Jarrett teaches using an authorization of a clinician to access the databases using an authentication token (i.e., user identifier uses authorization token as defined in the specification paragraph 433) for multiple different users (para [0062, 0064, 0022]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the clinical workflow system of Crafts to integrate the application of providing authorization tokens of users of Jarrett with the motivation of ensuring security of transmission of patient data (see: Jarrett, paragraph 61).
CLAIM 3-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 3, Crafts further teaches:
wherein the first set of task-specific components comprises one or more task-specific agent modules (Crafts teaches that the tasks are embodied in the workflow and visualization management modules (para [0097-98]))
CLAIM 4-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 4, Jarrett further teaches:
wherein the user identifier comprises an authentication token for the user (Jarrett teaches using an authorization of a clinician to access the databases using an authentication token (i.e., user identifier uses authorization token as defined in the specification paragraph 433) (para [0062, 0064]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the clinical workflow system of Crafts to integrate the application of providing authorization tokens of users of Jarrett with the motivation of ensuring security of transmission of patient data (see: Jarrett, paragraph 61).
CLAIM 5-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 5, Crafts further teaches:
wherein the set of databases comprises one or more databases storing data owned by the user (Crafts teaches that the user can update the repository of data based on their own research data from their research papers to modify the models (para [0090, 0115, 0116]))
CLAIM 6-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 6, Crafts further teaches:
wherein the machine-learning model is a component of a super agent module (The specification teaches that the super agent module comprises a machine learning model and indicates guidelines (i.e., rules) in paragraph 192. Crafts teaches this as the machine learning models uses rules to implement the queries (i.e., prompts) (para [0052-0053, 0047, 0087]))
CLAIM 7-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 7, Crafts further teaches:
wherein the task-specific component comprises an interconnected node architecture (Crafts teaches that the system includes a feedback loop architecture of the data (i.e., nodes) to visualize the tasks (para [0085]))
CLAIM 8-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 8, Crafts further teaches:
wherein the task-specific component comprises a patient query agent, and wherein the database stores information from medical documents provided by the user (Crafts teaches that the user that queries the system can update the repository of data based on their own research data from their research papers to modify the models (para [0090, 0115, 0116]))
CLAIM 9-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 9, Crafts further teaches:
wherein each task-specific component in the first set of task-specific components has a corresponding individual or group-level permission data (Crafts teaches that the system grants access based on the type of user (clinician has higher levels of access than a patient and researchers have the highest levels of access) and visualizes the workflow tasks based on the specific user (para [0115, 0108,0109]))
Crafts teaches identifying users based on login credentials, but does not explicitly teach, however Jarrett teaches:
user identifier (Jarrett teaches using an authorization of a clinician to access the databases using an authentication token (i.e., user identifier uses authorization token as defined in the specification paragraph 433) (para [0062, 0064]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the clinical workflow system of Crafts to integrate the application of providing authorization tokens of users of Jarrett with the motivation of ensuring security of transmission of patient data (see: Jarrett, paragraph 61).
CLAIM 10-
Crafts in view of Jarrett teach the limitations of claim 1. Regarding claim 10, Crafts further teaches:
wherein the task-specific component comprises a care gap agent configured to identify gaps in patient care plans, (Crafts teaches that a human expert can review the template to ensure that the workflow template is accurate and removing false positives (i.e., gaps) (para [0040, 0033]))
and wherein the database stores patient care plan data of the user (Crafts teaches that the patient data including the treatment plan is stored (para [0037, 0032]))
CLAIM 11-
Claim 11 is significantly similar to claim 1 and is rejected upon the same prior art as claim 1.
CLAIMS 12-16-
Claims 12-16 are significantly similar to claims 3-7 and are rejected upon the prior art of claims 3-7 respectively.
CLAIM 17-
Claim 17 is significantly similar to claim 1 and is rejected upon the same prior art as claim 1.
CLAIMS 18-20-
Claims 18-20 are significantly similar to claims 3-5 and are rejected upon the prior art of claims 3-5 respectively.
Response to Arguments
The arguments filed 2/10/2026 have been fully considered.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the claimed invention recites a specific technical architecture for secure clinical data processing that governs how information flows through a computing system. Examiner respectfully disagrees. The rejection has followed all current USPTO guidelines. The claimed invention does not recite a specific secure technical architecture that provides a practical application to overcome the abstract idea. The input/output of data based on user level access on generic computing devices does not provide a technological improvement that solves a technical problem. The machine learning model that is recited is generic and does not provide an improvement that would create a practical application to overcome the abstract idea. The filtering of data that provides secure access of data is provided by the agent module (paragraph 101). The agent module (as shown in Figure 17) appears to use a generative LLM when transmitting data between databases. Generative LLMs are inherently not secure and a generative LLM can be secure if there are robust security practices as is known to one of ordinary skill in the art. Filtering user access data does not provide robust security practices that would secure the data transmission of a generative LLM. Using a generative LLM for data transfer teaches away from providing patient protection as the data security of a generative LLM as it is trained is not a given as is known of one of ordinary skill in the art. The agent module’s security protocol of implementing guardrails, therefore, does not provide a practical application as the security guardrails of limiting user access based on log-in credentials on a generic computing device does not provide a technological solution to a technical problem.
Applicant further argues that dynamic database coupling provides a practical application to overcome the 101 rejection. Examiner respectfully disagrees as the specification is silent on dynamic database coupling, however the specification recites that the database is coupled to the interface through data transfer (paragraph 60). Transferring data from multiple generic databases to a generic computing device does not provide significantly more to overcome the abstract idea.
The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
Regarding the arguments pertaining to the 103 rejection, these arguments are not persuasive. Applicant argues that Crafts does not teach the claim amendments as Crafts does not teach using task-specific components when comparing access level associated with the user identifier with the permission data. Examiner respectfully disagrees. The specification recites in paragraph 256 that the user identifier is checked against the access control lists to determine what data the user is authorized to access. Crafts teaches this under broadest reasonable interpretation. Crafts teaches that the tasks in the workflow are only able to be controlled by the user that has a level that identifies the user as able to access the tasks in the workflow (para [0098, 0036, 0028, 0108, 0110, 0115, 0124]). The user data is compared through a list and the system determines the access level based on the list, which Crafts teaches.
Applicant further argues that Crafts’ machine learning models do not select task-specific components based on a prompt. Examiner respectfully disagrees. Crafts teaches that the machine learning model is trained based on past treatment plans that have created predefined workflows with specific tasks that the clinician can select and the query of the system determines the risk models and the tasks based on those risk assessments which is based on the prompt determined by the machine learning model (which is how the queries are recited in paragraphs 257 and 289 of the specification) (para [0032-0036, 0124, 0094, 0063, 0089, 112]))
The dependent claims are rejected for the same reasons as the independent claims as shown above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Padmos (WO 2020/243400 A1) teaches clinical tasking based on machine learning models.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY A SASS whose telephone number is (571)272-4774. The examiner can normally be reached 7AM-5PM (EST).
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/K.A.S./ Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights