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 .
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.
The claims at a high level recite managing the end-to-end workflow of cell therapy manufacturing.
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-20 recite a method and a system and therefore, are directed to the statutory class of machine and a product.
The USPTO Guidance recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). MPEP §§ 2106.04(a), (d).
Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look in Step 2B to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field; or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. MPEP § 2106.05(d).
Step 2A, Prong One: Is a Judicial Exception Recited?
First, determine whether the claims recite any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes). MPEP § 2106.04(a).
Claim 1 recites –
▪ obtaining patient data for a patient; obtaining an initial protocol for the cell manufacturing process for the patient (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can logically obtain and evaluate patient data and establish a protocol);
▪ facilitating tracking and updating of the planned sequence of events by iteratively performing steps including: obtaining tracking data for tracking progress of the cell manufacturing process (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can logically and repeatedly track data over time);
▪ deriving one or more actions associated with the planned sequence of events; facilitating performance of the one or more actions. (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can derive appropriate actions);
These limitations, based on their broadest reasonable interpretation, recite a mental process, i.e. a judicial exception. For these reasons, the independent claim 1, as well as independents claims 10, 19, which include limitations commensurate in scope with claim 1, recite a judicial exception.
A method, like the claimed method, “a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” See Digitech Image Techs, LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) where collecting information, analyzing it, and displaying results from certain results of the collection and analysis was held to be an abstract idea. See In re Meyer, 688 F.2d 789, 795—96 (CCPA 1982), which held that “a mental process that a neurologist should follow” when testing a patient for nervous system malfunctions was not patentable.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
Next determine whether the claims recite additional elements that integrate the judicial exception into a practical application (see MPEP §§ 2106.05(a)-(c), (e)-(h)). To integrate the exception into a practical application, the additional claim elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)).
Additional elements:
▪ using machine learning models to optimize event management; applying a machine learning model to the patient data and the initial protocol to infer an initial planned sequence of events for the cell manufacturing process, wherein the machine learning model is trained based on historical cell manufacturing processes, and the machine learning model is trained to optimize an operational efficiency metric associated with the historical cell manufacturing processes; re-applying the machine learning model to the patient data and the tracking data to update the planned sequence of events for the cell manufacturing process (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using a processor to apply the exception);
▪ communicating, over a network, action data for facilitating performance of the one or more actions (A generic computer functions of receiving and processing that are well-understood, routine, and conventional activities previously known to the industry. Extracting caption data and natural text processing are merely extra-solution activities and does not meaningfully limit the independent claims. Generic computer implementation does not provide significantly more than the abstract idea and is merely adding insignificant extra-solution activity to the judicial exception - see MPEP § 2106.05(g)).
The term “additional elements” for claim features, limitations, or steps that the claim recites beyond the identified judicial exception. Claim 10 recites the additional elements of “storage medium” and claim 19 recites “one or more processors; and a non-transitory computer-readable storage medium.” However, claims do not recite any improvements to these additional elements, nor does the claims recite any particularly programmed or configured computer system, device, or machine learning. Rather, the additional elements in claims 1, 10 and 20 serve merely to automate the abstract idea. See Int’l Bus. Machs. Corp. v. Zillow Group, Inc., 50 F. 4" 1371, 1382 (Fed. Cir. 2022) (“[A] patent that ‘automate[s] “pen and paper methodologies” to conserve human resources and minimize errors’ is a ‘quintessential “do it on a computer” patent’ directed to an abstract idea.”) (quoting Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019)). Therefore, none of these recited additional elements, whether considered individually or in combination, integrates the judicial exception into a practical application.
The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating and processing known data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, 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. Therefore, these claims are directed to an abstract idea.
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception.
For these reasons, independent claim 1, as well as independent claims 10 and 19, which include similar additional elements as claim 1, are directed to an abstract idea.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, determine whether the claims recite an “inventive concept” that “must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer.” BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016); see MPEP § 2106.05(d). There must be more than “computer functions [that] are “well-understood, routine, conventional activit[ies]’ previously known to the industry.” Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 225 (2014) (second alteration in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 73 (2012)); see MPEP § 2106.05(d).
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception (see MPEP 2106.05(d)(Il). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer and associated computer network to obtain data, use data to identify other data, and comparing data, are some of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. The method claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea of displaying, processing and storing data using some unspecified, generic computer).
Note, that in similar case, such as Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), the Courts have identified that the additional elements of displaying and analyzing data, as shown in the independent claims 1, 10 and 19 do not amount to significantly more than the judicial exception. Consequently, that is not enough to transform an abstract idea into a patent-eligible invention.
No “inventive concept” sufficient to transform the abstract method of organizing human activity into a patent-eligible application. See MPEP § 2106.05. Rather, the additional elements identified above are merely well-understood, conventional computer components, as confirmed by the Specification. See MPEP § 2106.05(d)(1). For example, the Specification refers to the additional elements in generic terms.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate and process known data.
Additionally, the computer components are used for performing insignificant extra-solution activity and well understood, routine, and conventional functions. For example, the claimed processor and machine learning merely communicates and processes known data. Activities such as these are insignificant extra-solution activity and, therefore, well understood, routine, and conventional. See MPEP 2106.05(d); see also, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (Obtaining information about transactions using the Internet to verify credit card transactions); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d at 715, 112 USPQ2d at 1754 (Consulting and updating an activity log); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display); Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016) (Recording a customer’s order); Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017) (Identifying undeliverable mail items, decoding data on those mail items, and creating output data); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price). Furthermore, limitations such as integrating account details are well-understood, routine, and conventional activity. See Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
Independent system claim 1, 10 and 19 contain the identified abstract ideas, with the additional elements of a processor, hardware and the media, which is a generic computer component, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-9, 11-18, 20 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible. As such, the claims are not patent eligible.
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 Stephens (US 2025/0322924) in view of Brooks et al. (US 20220059202) and in further view of John et al. (US 12623068).
Regarding claim 1, Stephens teaches a method for managing a cell manufacturing process using machine learning models to optimize event management ([0037], [0129]), the method comprising:
obtaining patient data for a patient ([0043]);
obtaining an initial protocol for the cell manufacturing process for the patient ([0126], [0129], [0131] “receiving a set of process parameters of a cell engineering process”);
applying a machine learning model to the patient data and the initial protocol to infer an initial planned sequence of events for the cell manufacturing process ([0086], [0129], [0131] “applying a machine learning model to the received set of process parameters”),
wherein the machine learning model is trained based on historical cell manufacturing processes ([0056], [0068]-[0071]), and the machine learning model is trained to optimize an operational efficiency metric ([0148]) associated with the historical cell manufacturing processes ([0083], [0085], [0096], [0129]);
re-applying the machine learning model to the patient data
deriving one or more actions associated with the planned sequence of events ([0142]); and communicating, over a network ([0170]), action data for facilitating performance of the one or more actions ([0146]).
Stephens does not explicitly teach, however Brooks discloses obtaining an initial protocol for the cell manufacturing process for the patient ([1255]-[1257], [0266]);
facilitating tracking and updating of the planned sequence of events by iteratively performing ([1109]) steps including: obtaining tracking data for tracking progress of the cell manufacturing process ([0268]-[0269], [1119], [1166]); storing the tracking data to an event tracking log associated with the cell manufacturing process ([1121]-[1123]);
re-applying estimation ([1234]) to the patient data and the tracking data to update the planned sequence of events for the cell manufacturing process ([1107], [0110], [1194], [1197], [1214]-[1215]);
deriving one or more actions associated with the planned sequence of events ([0268] “patient treatment events are rescheduled based on the estimated time of completion of the expansion of the cell therapy product and a timing of patient treatment events”, [0310], [1141]-[1142], [1151]); and communicating, over a network, action data for facilitating performance of the one or more actions ([1155]-[1156], [1160], [1209]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stephens to include tracking and updating of the planned sequence of events as disclosed by Brooks. Doing so providing a continuous and automatic chain of custody and chain of identity for a patient-specific biological sample during an immunotherapy procedure, to create a computerized information portal that interested parties—such as the patient, physician, manufacturer, and other medical personnel—may use to quickly understand and track the current phase of the immunotherapy procedure and the status of the patient's biological sample during the procedure (Brooks [1108]).
Stephens as modified by Brooks does not explicitly teach, however, John discloses
re-applying the machine learning model to the patient data and the tracking data (C75L14-31) to update the planned sequence of events for the cell manufacturing process (C62L62-67L13-20, C74L24-40C75L49-55). NOTE John further explicitly teaches event log in C90L12-27.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stephens to include re-applying the machine learning model to the patient data and the tracking data as disclosed by John. Doing so would support the user towards successful treatment (John) C79L45-48.
Regarding claim 10, Stephens teaches a non-transitory computer-readable storage medium stores instructions for managing a cell manufacturing process using one or more machine learning models to optimize event management, the instructions for causing one or more processors to perform steps including: obtaining patient data for a patient; obtaining an initial protocol for the cell manufacturing process for the patient; applying a machine learning model to the patient data and the initial protocol to infer an initial planned sequence of events for the cell manufacturing process, wherein the machine learning model is trained based on historical cell manufacturing processes, and the machine learning model is trained to optimize an operational efficiency metric associated with the historical cell manufacturing processes; facilitating tracking and updating of the planned sequence of events by iteratively performing steps including: obtaining tracking data for tracking progress of the cell manufacturing process; storing the tracking data to an event tracking log associated with the cell manufacturing process; re-applying the machine learning model to the patient data and the tracking data to update the planned sequence of events for the cell manufacturing process; deriving one or more actions associated with the planned sequence of events; and communicating, over a network, action data for facilitating performance of the one or more actions.
Claim 10 recites substantially the same limitations as claim 1 and is rejected for substantially the same reasons.
Regarding claim 19, Stephens teaches a computer system comprising: one or more processors; and a non-transitory computer-readable storage medium stores instructions for managing a cell manufacturing process using one or more machine learning models to optimize event management, the instructions for causing the one or more processors to perform steps including: obtaining patient data for a patient; obtaining an initial protocol for the cell manufacturing process for the patient; applying a machine learning model to the patient data and the initial protocol to infer an initial planned sequence of events for the cell manufacturing process, wherein the machine learning model is trained based on historical cell manufacturing processes, and the machine learning model is trained to optimize an operational efficiency metric associated with the historical cell manufacturing processes; facilitating tracking and updating of the planned sequence of events by iteratively performing steps including: obtaining tracking data for tracking progress of the cell manufacturing process; storing the tracking data to an event tracking log associated with the cell manufacturing process; re-applying the machine learning model to the patient data and the tracking data to update the planned sequence of events for the cell manufacturing process; deriving one or more actions associated with the planned sequence of events; and communicating, over a network, action data for facilitating performance of the one or more actions.
Claim 19 recites substantially the same limitations as claim 1 and is rejected for substantially the same reasons.
Regarding claims 2, 11 and 20, Stephens as modified teaches the method, the medium and the system, wherein communicating the action data comprises: generating a user interface associated with the cell manufacturing process for the patient that includes a representation of the planned sequence of events (Stephens [0138], [0144], Brooks [1160], [1202] “transmit a target schedule of patient treatment events based on the received cell order request”); receiving, over a network, an access request from a client device to access the user interface including the representation of the planned sequence of events (Stephens [0171], Brooks [1163], [1256]); and responsive to the access request, outputting the user interface to the client device (Brooks [1083]-[1084], [1100], [1202], John C15L9-35, C82L26-27).
Regarding claims 3 and 12, Stephens as modified teaches the method and the medium, wherein communicating the action data comprises: generating a hard recommendation to halt the cell manufacturing process; and automatically disabling actions in a user interface associated with continuing the cell manufacturing process (Brooks [1092], [1211] “the case is reviewed for potential termination”, [1216], [1233], [1246] “if it is determined that the treatment is to be terminated, the scheduling module communicates with the hospital-side interface that the treatment is to be terminated”, [1251], [1263]).
Regarding claims 4 and 13, Stephens as modified teaches the method and the medium, wherein communicating the action data comprises: generating a soft recommendation to halt the cell manufacturing process; and communicating the soft recommendation to one or more client devices (Brooks [1212] “treatment events may be rescheduled”, [1235], [01238] “cells did not pass the QA test at the second time point, the scheduling module may reschedule (i.e., delay) … subsequent patient treatment events,” [1240]).
Regarding claims 5 and 14, Stephens as modified teaches the method and the medium, wherein communicating the action data comprises: generating a notification relating to an upcoming event in the planned sequence of events; and communicating the notification to one or more client devices (Brooks [1092], [1244], [1241]-[1242], [1244]).
Regarding claims 6 and 15, Stephens as modified teaches the method and the medium, wherein communicating the action data comprises: obtaining and storing an acknowledgement message (Brooks [1103]) from the one or more client devices responsive to the notification (Brooks [1220]- [1221]).
Regarding claims 7 and 16, Stephens as modified teaches the method and the medium, wherein communicating the action data comprises: facilitating acquisition of a digital affirmation relating to the cell manufacturing process; and storing the digital affirmation (Brooks [1124] [1160], [1180], [1202], Stephens [0125]).
Regarding claims 8 and 17, Stephens as modified teaches the method and the medium, wherein communicating the action data comprises: assigning an action associated with an event to one or more parties; and communicating the assignment to a client device associated with the one or more parties (Brooks [1102], [1103] “matching the medical and/or manufacturing event with the interested parties”; “based upon rules associated with each interested party… forward information and/or issue an alert or notification to an interested party to make the interested party aware of the medical event”, [1185]).
Regarding claims 9 and 18, Stephens as modified teaches the method and the medium, wherein the machine learning model is trained according to a training process comprising:
obtaining, over a network, training data for training the machine learning model, the training data including patient data relating to patients that have participated in historical cell manufacturing processes and event data relating to historical events of the historical cell manufacturing processes (Stephens [0009], [0041], [0056], [0068], [0071]);
applying a machine learning algorithm to the training data to train the machine learning model based on the operational efficiency metric (Stephens [0085], [0091], [0096], [0130], [0143]); and storing the machine learning model (Stephens [0095], John C14L6-13).
Claim(s) 1, 10 and 19 is/are alternatively or additionally rejected under 35 U.S.C. 103 as being unpatentable over Brooks et al. (US 20220059202) in view of Santiago (US 20250046407).
Regarding claims 1, Brooks teaches a method for managing a cell manufacturing process using machine learning models to optimize event management, the method comprising:
obtaining patient data for a patient ([0264], [1112]);
obtaining an initial protocol for the cell manufacturing process for the patient ([1255]-[1257], [0266]);
applyingestimation to the patient data and the initial protocol to infer an initial planned sequence of events for the cell manufacturing process ([1234], [1280]),
facilitating tracking and updating of the planned sequence of events by iteratively performing ([1109]) steps including:
obtaining tracking data for tracking progress of the cell manufacturing process ([0268]-[0269], [1119], [1166]); storing the tracking data to an event tracking log associated with the cell manufacturing process ([1121]-[1123]);
re-applying estimation ([1234]) to the patient data and the tracking data to update the planned sequence of events for the cell manufacturing process ([1107], [0110], [1194], [1197], [1214]-[1215]);
deriving one or more actions associated with the planned sequence of events ([0268] “patient treatment events are rescheduled based on the estimated time of completion of the expansion of the cell therapy product and a timing of patient treatment events”, [0310], [1141]-[1142]); and communicating, over a network, action data for facilitating performance of the one or more actions ([1155]-[1156], [1160], [1209]).
Brooks does not explicitly teach, however Santiago discloses applying a machine learning model to the patient data and the initial protocol to infer an initial planned sequence of events for the cell manufacturing process, wherein the machine learning model is trained based on historical cell manufacturing processes ([0066]-[0067], [0069], [0121], [0137]), and the machine learning model is trained to optimize an operational efficiency metric associated with the historical cell manufacturing processes ([0212], [0226], [0232]-[0233], [0244]);
re-applying the machine learning model to the patient data and the tracking data to update the planned sequence of events ([0058], [0070]-[0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Brooks to include machine learning model as disclosed by Santiago. Doing so helps ensure the system's effectiveness and reliability, reducing the risk of issues after full-scale deployment (Santiago [0155]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/Primary Examiner, Art Unit 2165 August 19, 2026
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