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 received 3/31/2026.
Claims 2, 13, and 19 were amended 3/13/2026.
Claims 2-11 and 13-22 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 2-11 and 13-22 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 2-11 and 13-22 are drawn to a method, system and a non-transitory computer readable medium which are statutory categories of invention (Step 1: YES).
Independent claim 2 recites: obtaining, a first dataset including indicators of a first plurality of symptoms experienced by a patient, each symptom in the first plurality of symptoms being associated with a condition and characterized by at least one severity value for a particular symptom; discovering, based on the first dataset, coexisting simultaneous conditions that are independent of each other but contribute to at least some symptoms form the first plurality of symptoms, wherein discovering the coexisting simultaneous conditions comprises: applying to a plurality of other datasets associated with other patients to generate a set of co-occurring symptoms, wherein the plurality of other datasets includes indicators of a second plurality of symptoms, and wherein the set of co-occurring symptoms includes a subset of the second plurality of symptoms; based on the generated set of co-occurring symptoms, generating an N-dimensional space, wherein each dimension in the N-dimensional space is associated with a symptom in a set of co-occurring symptoms and the N-dimensional space comprises an item associated with a dataset from a first subset of the plurality of other datasets, wherein the first subset comprises datasets form the plurality of other datasets that include at least one indicator of a symptom from the set of co-occurring symptoms, applying a clustering algorithm to a set of items in the N-dimensional space to generate a set of clusters, wherein a cluster in the set of clusters is associated with a particular coexistence of simultaneous conditions and includes a subset of the set of items; and classifying the first dataset as having the coexisting simultaneous conditions based on a similarity between data in the first dataset and data in a second dataset from the subset of the plurality of other datasets, wherein a particular item associated with the second dataset is included in a cluster associated with the coexisting simultaneous conditions; generating a second subset of the plurality of other datasets, wherein the second subset includes each dataset form the plurality of other datasets that is associated with items in the cluster associated with the coexisting simultaneous conditions; applying a data mining algorithm to each dataset form the second subset of the plurality of other datasets to generate a vector of scores, wherein each score in the vector of scores represents a fitness of a given treatment to the coexisting simultaneous conditions; selecting, a treatment for the simultaneous conditions based on a highest score from the vector of scores; in response to the selecting, generating, a measurable goral for achievement by the patient, wherein the measurable goal is generated based on the treatment and at least one of (i) patient behavior data input by a user of a community of the patient (ii) patient behavior data input by the patient, or (iii) patient behavior data passively collected; in response to generating the measurable goal, a set of electronic messages that relate to the measurable goal to be sent associated with one or more users of the community of the patient, and in response to receiving an update to a progress of the patient towards the measurable goal, updating, the first dataset based on the progress of the patient towards the a measurable goal, and prescribing an updated treatment based on the updated first dataset, wherein the updated treatment is increased physical exercise.
Independent claims 13 and 19 recite: obtain a first dataset including indicators of a first plurality of symptoms experienced by a patient, each symptom in the first plurality of symptoms being associated with a condition and characterized by at least one severity value for a particular symptom; discover, based on the first dataset, coexisting simultaneous conditions that are independent of each other but contribute to at least some symptoms form the first plurality of symptoms, wherein discovering the coexisting simultaneous conditions comprises: applying to a plurality of other datasets associated with other patients to generate a set of co-occurring symptoms, wherein the plurality of other datasets includes indicators of a second plurality of symptoms, and wherein the set of co-occurring symptoms includes a subset of the second plurality of symptoms: based on the generated set of co-occurring symptoms, generating an N-dimensional space, wherein each dimension in the N-dimensional space comprises is associated with a symptom in the set of co-occurring symptoms and the N-dimensional space comprises an item associated with a dataset from a first subset of the plurality of other datasets, wherein the first subset comprises datasets from the plurality of other datasets that include at least one indicator of a symptom from the set of co-occurring symptoms; applying to items in the N-dimensional space to generate a plurality of sets of items, wherein a set of items in the plurality of sets of items is associated with a particular coexistence of simultaneous conditions and includes a subset of the items in the N-dimensional space; and classifying the first dataset as having the coexisting simultaneous conditions based on a similarity between data in the first dataset and data in a second dataset from the subset of the plurality of other datasets, wherein a particular item associated with the second dataset is included in a set of items associated with the coexisting simultaneous conditions; generate a second subset of the plurality of other datasets, wherein the second subset includes each dataset from the plurality of other datasets that is associated with items in the set of items associated with the coexisting simultaneous conditions; apply a data mining algorithm to each dataset from the second subset of the plurality of other datasets to generate a vector of scores, wherein each score in the vector of scores represents a fitness of a given treatment to the coexisting simultaneous conditions; select treatment for the simultaneous conditions based on a highest score from the vector of scores; in connection with the treatment, generate a measurable goal for achievement by the patient, wherein the measurable goal is based on at least one of (i) data input by a user in a community of the patient, (ii) data input by the patient, or (iii) data passively collected and causing a set of electronic messages that relate to the measurable goal to be sent associated with one or more users of the community of the patient; in response to receiving an update to a progress of the patient towards the measurable goal, update the first dataset based on the progress of the patient towards the measurable goal and prescribe an updated treatment based on the updated first dataset, wherein the updated treatment is increased physical exercise.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that “The disclosed teachings relate generally to techniques for assessing and treating individuals. More particularly, the disclosed teachings relate to a comorbidity-based platform that can assess patients based on observations by groups of resources and provide personalized feedback to treat the patients.” (see: specification paragraph 2). 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 “Hence, the system 10 can enable healthcare professionals to identify treatments for patient profiles classified with comorbidities, rather than prescribing a piecemeal combination of treatments for separate and distinct known diseases or conditions.” (see: specification paragraph 114). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Further, the recited limitations, as drafted, under the broadest reasonable interpretation, cover mathematical relationships by calculating vectors of scores using algorithms. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (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 “computer system”, “system”, “at least one processor”, “non-transitory computer readable storage medium”, “one or more processors”, “associative rule learning algorithm”, “machine learning algorithm”, “autonomous devices”, “client devices” are recited at a high level of generality (e.g., that the determining and selecting is performed using generic computer components and generic machine learning 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 2, Figure 9 and
Paragraph 107, where “Each component of the system 10 may include combinations of hardware and/or software to process data, perform functions, communicate over the network 18, and the like. For example, a component of the system 10 may include a processor, memory or storage, a network transceiver, a display, an operating system and application software ( e.g., for providing a user interface), and the like.”
Paragraph 183, where “The computing device 24 may be a generic computer or specifically designed to carry out features of system 10. For example, the computing device 24 may be a system-on-chip (SOC), a single-board computer (SBC) system, a desktop or laptop computer, a kiosk, a mainframe, a mesh of computer systems, a handheld mobile device, or combinations thereof.”
Paragraph 186, where “The control 28 includes one or more processors 36 ( e.g., central processing units (CPUs)), application-specific integrated circuits (ASICs), and/or field-programmable gate arrays (FPGAs), and memory 38 (which may include software 40). For example, the memory 38 may include volatile memory, such as random-access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM). The memory 38 can be local, remote, or distributed”
Paragraph 187, “A software program (e.g., software 40), when referred to as "implemented in a computer-readable storage medium," includes computerreadable instructions stored in the memory ( e.g., memory 38). A processor ( e.g., processor 36) is "configured to execute a software program" when at least one value associated with the software program is stored in a register that is readable by the processor. In some embodiments, routines executed to implement the disclosed embodiments may be implemented as part of operating system (OS) software (e.g., Microsoft Windows® and Linux®) or a specific software application, component, program, object, module, or sequence of instructions referred to as "computer programs."”
Paragraph 145, “Moreover, the disclosed technology can implement a machine learning algorithm using the feedback loops described above to learn of more effective treatments for the comorbidity profiles.”
Paragraph 156, “For example, nodes can represent autonomous computing devices that passively collect data of the patient's activities and transmit the collected data to the platform. For example, a fitness tracker worn by the patient can passively collect activity data and provide that data to the platform that also collects data actively input by users of the patient's community.”
Paragraph 109, “For example, the client devices 14 (also referred to individually as client device 14) are used by users to interact with the system 10. Examples of the client devices 14 include smart phones (e.g., APPLE IPHONE, SAMSUNG GALAXY, NOKIA LUMINA), tablet computers (e.g., APPLE IPAD, SAMSUNG NOTE, AMAZON FIRE, MICROSOFT SURFACE), computers (e.g., APPLE MACBOOK, LENOVO 440), and any other device that is capable of accessing information provided by the comorbidity server 12 over the network 18. The client device 14 may run a mobile application ("app") used to interact with the comorbidity server 12 over the network 18. The mobile app may be downloadable from an app store or equivalent app library.”
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 3-11, 14-18 and 20-22 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. Claims 3-11, 14-18 and 20-21 recite updating, generating, and calculating datasets and values on the generically recited computing elements and learning algorithm as shown in the parent claims above.
Claim 22 further recites a “Bayesian network” and in paragraph 141 of the specification and does not provide a practical application. The use of the Bayesian network is merely using an algorithm to input/output data and does not provide a technological solution to a technical problem. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. 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.
Response to Arguments
The arguments filed 3/31/2026 have been fully considered.
Regarding the arguments pertaining to the 112 rejections are persuasive. The claims have been sufficiently amended and the 112 rejections have been withdrawn.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the claimed invention improves existing computer technology by improving treatment selection and administration through the application of machine learning and clustering techniques to patient profiles. Examiner respectfully disagrees, as the improvement of treatment selection and administration is directly related to “Certain Methods of Organizing Human Activity” by improving the interactions between users to provide treatment plans and is not related to an improvement of a technology. Applicant recites claim 2 of Example 48 is similar to the claimed invention. Examiner respectfully disagrees. The claimed invention uses generic machine learning algorithms to filter data using generic computing components and does not provide separation of signal waves and create a mixed signal wave based on the separation recognition techniques. Example 48 claim 2 improves the technology by recognizing masked clusters in the time domain to create a mixed signal as shown Example 48 claim 2. It improves on the technology of the technique of separating speech signals, however it does not provide an improved speech signal as argued. The current claimed invention is merely transmitting, filtering, and outputting data without significantly more as shown in the rejection above and the improvement of treating users is a function of the abstract idea.
Applicant further argues that the claimed invention is similar to Example 49 claim 2 in that the claimed invention prescribes an updated treatment and Example 49 claim 2 administers an appropriate treatment. Examiner respectfully disagrees. Administering a treatment is similar to that of Classen, however, prescribing a treatment does not positively recite that the treatment is administered to the user. Therefore, the act of prescribing a treatment does not provide a practical application to overcome the abstract idea and is dissimilar to Classen and Example 49 claim 2.
The dependent claims rely on the arguments of the independent claims and are rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Abbruzzese (US 20160358501 A1) teaches identifying treatments based on logical constructs of algorithm functions.
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
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