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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/04/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a transformer” in claims 1 and 11; “a storage layer” in claims 1, 9, and 11; “a delivery network” in claim 1; “an electronic device comprising a processor” in claim 1; and “a search engine” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 9-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The following claim elements lack a sufficient disclosure for the corresponding structure in the specification: “a transformer” in claims 1 and 11; “a storage layer” in claims 1, 9, and 11; “a delivery network” in claim 1; “an electronic device comprising a processor” in claim 1; and “a search engine” in claim 9. Accordingly, these claims fail to comply with the written description requirement.
Claims 10-11 are rejected based on their dependency on claim 9.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 9-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “a transformer” in claims 1 and 11; “a storage layer” in claims 1, 9, and 11; “a delivery network” in claim 1; “an electronic device comprising a processor” in claim 1; and “a search engine” in claim 9 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification lacks sufficient structure for what these components are. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 10-11 are rejected based on their dependency on claim 9.
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. Claim 1 is drawn to a system, claims 2-11 are drawn to a device, and claims 12-20 are drawn to a method, each of which is within the four statutory categories. Claims 1-20 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a system for improving adherence to patient treatment plans, the system comprising:
a) a transformer configured to vectorize data regarding a patient;
b) a storage layer configured to store the data;
c) a plurality of machine learning (ML) models configured to 13) generate a personalized communication for the patient based on the data, wherein the plurality of ML models comprises (i) a plan-complexity ML model trained using historical adherence data associated with treatment plans similar to a treatment plan prescribed for the patient, (ii) a patient-persona ML model trained using behavioral pattern data associated with patients similar to the patient, (iii) an adherence-probability ML model trained using the historical adherence data and persona data associated with patients similar to the patient, and (iv) a personalized- communication ML model trained using interaction data comprising language from interactions involving other patients similar to the patient;
d) a delivery network configured to deliver the personalized communication to the patient; and
e) an electronic device comprising a processor configured to:
1) receive the data regarding the patient, wherein the data comprises (i) treatment plan data regarding the treatment plan prescribed for the patient, and (ii) other data regarding a healthcare provider associated with the patient, a clinician associated with the patient, a medical record associated with the patient, a social media account associated with the patient, a laboratory associated with the patient, or a pharmacy associated with the patient;
2) vectorize the data using the transformer, wherein vectorizing the data comprises translating the treatment plan data and the other data into numerical representations that capture semantic meaning embedded within the treatment plan data and the other data;
3) store the data in the storage layer after vectorizing the data;
4) provide the treatment plan data from the storage layer to the plan-complexity ML model, wherein the treatment plan data comprises (i) a number of medications prescribed in the treatment plan, and (ii) a dosage frequency for the prescribed medications;
5) receive a plan-complexity score from the plan-complexity ML model responsive to providing the treatment plan data to the plan-complexity ML model, wherein the plan-complexity score indicates a complexity level of the treatment plan;
6) provide the other data from the storage layer to the patient-persona ML model, wherein the other data regards at least (i) the medical record associated with the patient and (ii) the social media account associated with the patient;
7) receive persona indicators from the patient-persona ML model responsive to providing the other data to the patient-persona ML model, wherein the persona indicators indicate (i) an ability of the patient to follow a prescribed regime and (ii) a communication style of the patient;
8) provide the treatment plan data from the storage layer, the plan-complexity score, and the persona indicators to the adherence-probability ML model;
9) receive an adherence-probability score from the adherence-probability ML model responsive to providing the treatment plan data, the plan-complexity score, and the persona indicators to the adherence-probability ML model, wherein the adherence-probability score indicates a probability of the patient adhering to the treatment plan;
10) provide the treatment plan data from the storage layer, the adherence- probability score, and the persona indicators to the personalized-communication ML model;
11) receive a communication plan and the personalized communication from the personalized-communication ML model, wherein (i) the communication plan indicates whether the personalized communication should include text, images, or video and whether the personalized communication should be delivered via email, text message, notification, phone call, or letter and (ii) the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient; and
12) deliver the personalized communication to the patient via the delivery network and according to the communication plan.
Claim 1 recites, in part, performing the steps of 2) vectorize the data using the transformer, wherein vectorizing the data comprises translating the treatment plan data and the other data into numerical representations that capture semantic meaning embedded within the treatment plan data and the other data and 3) store the data in the storage layer after vectorizing the data. These steps correspond to Mathematical Concepts.
Claim 1 recites, in part, performing the steps of 13) generate a personalized communication for the patient based on the data, wherein the plurality of models comprises (i) a plan-complexity model trained using historical adherence data associated with treatment plans similar to a treatment plan prescribed for the patient, (ii) a patient-persona model trained using behavioral pattern data associated with patients similar to the patient, (iii) an adherence-probability model trained using the historical adherence data and persona data associated with patients similar to the patient, and (iv) a personalized- communication model trained using interaction data comprising language from interactions involving other patients similar to the patient, 1) receive the data regarding the patient, wherein the data comprises (i) treatment plan data regarding the treatment plan prescribed for the patient, and (ii) other data regarding a healthcare provider associated with the patient, a clinician associated with the patient, a medical record associated with the patient, a social media account associated with the patient, a laboratory associated with the patient, or a pharmacy associated with the patient, 3) store the data in the storage layer, 4) provide the treatment plan data from the storage layer to the plan-complexity ML model, wherein the treatment plan data comprises (i) a number of medications prescribed in the treatment plan, and (ii) a dosage frequency for the prescribed medications, 5) receive a plan-complexity score from the plan-complexity ML model responsive to providing the treatment plan data to the plan-complexity ML model, wherein the plan-complexity score indicates a complexity level of the treatment plan, 6) provide the other data from the storage layer to the patient-persona ML model, wherein the other data regards at least (i) the medical record associated with the patient and (ii) the social media account associated with the patient, 7) receive persona indicators from the patient-persona ML model responsive to providing the other data to the patient-persona ML model, wherein the persona indicators indicate (i) an ability of the patient to follow a prescribed regime and (ii) a communication style of the patient, 8) provide the treatment plan data from the storage layer, the plan-complexity score, and the persona indicators to the adherence-probability ML model, 9) receive an adherence-probability score from the adherence-probability ML model responsive to providing the treatment plan data, the plan-complexity score, and the persona indicators to the adherence-probability ML model, wherein the adherence-probability score indicates a probability of the patient adhering to the treatment plan, 10) provide the treatment plan data from the storage layer, the adherence-probability score, and the persona indicators to the personalized-communication ML model, 11) receive a communication plan and the personalized communication from the personalized-communication ML model, wherein (i) the communication plan indicates whether the personalized communication should include text, images, or video and whether the personalized communication should be delivered via email, text message, notification, phone call, or letter and (ii) the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient; and 12) deliver the personalized communication to the patient via the delivery network (delivery executed by a network of humans) and according to the communication plan. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how people can assess patient data in order to make a determination about a treatment, and then communicate that determination.
Independent claims 2 and 12 recite similar limitations and are also directed to an abstract idea under the same analysis. Going forward, the above-abstract concepts will be considered as a singular abstract idea for further analysis.
Depending claims 3-11 and 13-20 include all of the limitations of claims 2 and 12, and therefore likewise incorporate the above described abstract idea. Depending claims 3-8, 10, and 13-20 add additional functional steps; and claims 9 and 11 add components which have functional elements. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 3-11 and 13-20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 2 and 12 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a transformer (if hardware) configured to vectorize data regarding a patient, b) a storage layer (if hardware) configured to store the data, c) a plurality of machine learning (ML) models, d) a delivery network (if hardware) configured to deliver the personalized communication to the patient, e) an electronic device comprising a processor, f) a search engine (in claim 9), g) an object data store (in claim 11), and h) a vector database (in claim 11) to perform the claimed steps.
The a) a transformer configured to vectorize data regarding a patient, b) a storage layer configured to store the data, d) a delivery network configured to deliver the personalized communication to the patient, e) an electronic device comprising a processor, f) a search engine, g) an object data store, and h) a vector database in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification for a lack of description of anything but what may be considered as generic computing components for the elements above, see MPEP 2106.05(f)).
Additionally, the c) plurality of machine learning (ML) models in these steps generally links the abstract idea to a particular technological environment or field of use (such as machine learning, see MPEP 2106.05(h)).
The remaining dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these 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. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
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, the additional elements of using a) a transformer configured to vectorize data regarding a patient, b) a storage layer configured to store the data, c) a plurality of machine learning (ML) models, d) a delivery network configured to deliver the personalized communication to the patient, e) an electronic device comprising a processor, f) a search engine, g) an object data store, and h) a vector database to perform the claimed steps amounts to no more than a general linking to a particular technological field and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity or certain mathematical steps. Specifically, MPEP 2106.05(f) and MPEP 2106.05(h) recite that the following limitations are not significantly more:
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The current invention determines and delivers a personalized communication utilizing a) a transformer configured to vectorize data regarding a patient, b) a storage layer configured to store the data, d) a delivery network configured to deliver the personalized communication to the patient, e) an electronic device comprising a processor, f) a search engine, g) an object data store, and h) a vector database, thus the computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Additionally, the c) plurality of machine learning (ML) models generally links the abstract idea to a particular technological environment or field of use. The following represent an example that courts have identified as generally linking the abstract idea to a particular technological environment (e.g. see MPEP 2106.05(h)): Limiting the abstract idea data to machine learning models, because limiting application of the abstract idea to machine learning is simply an attempt to limit the use of the abstract idea to a particular technological environment, e.g. see Electric Power Group, LLC v. Alstom S.A.
Mere instructions to apply an exception using generic computer components or a general linking to a particular technological field cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-20 are therefore rejected under 35 U.S.C. 101 as being directed to 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2010/0205008 to Hua et al. in view of U.S. 2022/0028511 to Neumann.
As per claim 2, Hua et al. teaches an electronic device configured to generate personalized communications regarding patient treatment plans, the electronic device comprising a processor configured to:
--receive data regarding a patient, (see: 240 of FIG. 2 and paragraph [0024] where there is reception of data) wherein the data comprises (ii) other data regarding a healthcare provider associated with the patient, a clinician associated with the patient, a medical record associated with the patient, a social media account associated with the patient, a laboratory associated with the patient, or a pharmacy associated with the patient; (see: 240 of FIG. 2 and paragraph [0024] where there is reception of data which can be considered as a medical record associated with the patient)
--provide the data to a plurality of ML models configured to generate a personalized communication for the patient based on the data; (see: paragraphs [0024] and [0025] where there is data being provided to a model to predict a likelihood of compliance with a treatment regimen. The likelihood is then used to determine a personalized communication/message via an action component (another model). Also see: paragraph [0026] where there may be a plurality of models used)
--receive the personalized communication from the plurality of ML models responsive to providing the data to the plurality of ML models, (see: paragraphs [0024] and [0025] where there is input of the likelihood of adherence and an output of a message. The message here is being received upon creation) wherein the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient; (see: paragraph [0025] where the message here is addressed to the patient as it is sent to them and about them, and it is also regarding the treatment plan prescribed to the patient and the likelihood of compliance with it) and
--deliver the personalized communication to the patient (see: paragraph [0025] where there is a message delivered to the patient).
Hua et al. may not further, specifically teach:
--wherein the data comprises (i) treatment plan data regarding a treatment plan prescribed for the patient.
Neumann teaches:
--wherein the data comprises (i) treatment plan data regarding a treatment plan prescribed for the patient (see: paragraph [0054] where a user ameliorative plan is being received as a function of the user identifier).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the data comprises (i) treatment plan data regarding a treatment plan prescribed for the patient as taught by Neumann in the device as taught by Hua et al. with the motivation(s) of improving the physical condition of the user (see: paragraph [0046] of Neumann).
As per claim 12, Hua et al. teaches a computer-implemented method for improving adherence to patient treatment plans, the method comprising:
--receiving data regarding a patient, (see: 240 of FIG. 2 and paragraph [0024] where there is reception of data) wherein the data comprises (ii) other data regarding a healthcare provider associated with the patient, a clinician associated with the patient, a medical record associated with the patient, a social media account associated with the patient, a laboratory associated with the patient, or a pharmacy associated with the patient; (see: 240 of FIG. 2 and paragraph [0024] where there is reception of data which can be considered as a medical record associated with the patient)
--providing the data to a plurality of ML models configured to generate a personalized communication for the patient based on the data; (see: paragraphs [0024] and [0025] where there is data being provided to a model to predict a likelihood of compliance with a treatment regimen. The likelihood is then used to determine a personalized communication/message via an action component (another model). Also see: paragraph [0026] where there may be a plurality of models used)
--generating the personalized communication using the plurality of ML models after providing the data to the plurality of ML models, (see: paragraphs [0024] and [0025] where there is input of the likelihood of adherence and an output of a message. The message here is being received upon generation) wherein the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient; (see: paragraph [0025] where the message here is addressed to the patient as it is sent to them and about them, and it is also regarding the treatment plan prescribed to the patient and the likelihood of compliance with it)
--receiving the personalized communication from the plurality of ML models after generating the personalized communication; (see: paragraphs [0024] and [0025] where there is input of the likelihood of adherence and an output of a message. The message here is being received upon generation) and
--delivering the personalized communication to the patient (see: paragraph [0025] where there is a message delivered to the patient).
Hua et al. may not further, specifically teach:
--wherein the data comprises (i) treatment plan data regarding a treatment plan prescribed for the patient.
Neumann teaches:
--wherein the data comprises (i) treatment plan data regarding a treatment plan prescribed for the patient (see: paragraph [0054] where a user ameliorative plan (treatment plan) is being received as a function of the user identifier).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the data comprises (i) treatment plan data regarding a treatment plan prescribed for the patient as taught by Neumann in the method as taught by Hua et al. with the motivation(s) of improving the physical condition of the user (see: paragraph [0046] of Neumann).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2010/0205008 to Hua et al. in view of U.S. 2022/0028511 to Neumann as applied to claims 2 and 12, and further in view of U.S. 2025/0174333 to Hubenov et al.
As per claim 3, Hua et al. and Neumann in combination teaches the device of claim 2, see discussion of claim 2. Neumann further teaches wherein the plurality of ML models comprises a plan-complexity ML model configured to:
--receive the treatment plan data, (see: paragraph [0090] where there is reception of a treatment/ameliorative plan data by a model (executed by processing module)) wherein the treatment plan data comprises (vi) a dietary restriction prescribed in the treatment plan (see: paragraph [0089] where there are dietary restrictions for the plan).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 2, and incorporated herein.
Hua et al. and Neumann in combination may not further, specifically teach:
1) --wherein the treatment plan data comprises three or more of (i) a number of medications prescribed in the treatment plan, (ii) an availability of the prescribed medications, (iii) a dosage frequency for the prescribed medications, (iv) a number of chronic conditions addressed by the treatment plan, (v) an indication that one or more of the prescribed medications is a high-risk medication, and (vii) an indication that the prescribed treatment plan requires special medical equipment;
2) --generate a plan-complexity score based on the treatment plan data, wherein the plan-complexity score indicates a complexity level of the treatment plan; and
3) --wherein (i) providing the data to the plurality of ML models comprises providing the treatment plan data to the plan-complexity ML model and (ii) the plurality of ML models generating the personalized communication is further based on the treatment-plan-complexity score.
Kiaie et al. teaches:
1) --wherein the treatment plan data comprises three or more of (i) a number of medications prescribed in the treatment plan, (ii) an availability of the prescribed medications, (iii) a dosage frequency for the prescribed medications, (iv) a number of chronic conditions addressed by the treatment plan, (v) an indication that one or more of the prescribed medications is a high-risk medication, and (vii) an indication that the prescribed treatment plan requires special medical equipment (see: paragraph [0182] where there is reception of a dosage frequency. Also see: paragraph [0051] where there is information about the availability of the medication).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) wherein the treatment plan data comprises three or more of (i) a number of medications prescribed in the treatment plan, (ii) an availability of the prescribed medications, (iii) a dosage frequency for the prescribed medications, (iv) a number of chronic conditions addressed by the treatment plan, (v) an indication that one or more of the prescribed medications is a high-risk medication, and (vii) an indication that the prescribed treatment plan requires special medical equipment as taught by Kiaie et al. in the device as taught by Hua et al. and Neumann in combination with the motivation(s) of improving the user’s therapy management (see: paragraph [0200] of Kiaie et al.).
Hubenov et al. teaches:
2) --generate a plan-complexity score based on the treatment plan data, (see: paragraph [0163] where there is a generation of a complexity score for a treatment) wherein the plan-complexity score indicates a complexity level of the treatment plan; (see: paragraph [0163] where the complexity score is based on how complex the treatment is)
3) --wherein (i) providing the data to the plurality of ML models comprises providing the treatment plan data to the plan-complexity ML model (see: paragraph [0163] where the ML model is given data and provides this complexity score) and (ii) the plurality of ML models generating the personalized communication is further based on the treatment-plan-complexity score (see: paragraph [0145] where there is a generation of an alert using processing logic and based on the complexity of the treatment. Also see: paragraph [0059] where there are multiple models).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 2) generate a plan-complexity score based on the treatment plan data, wherein the plan-complexity score indicates a complexity level of the treatment plan and have 3) wherein (i) providing the data to the plurality of ML models comprises providing the treatment plan data to the plan-complexity ML model and (ii) the plurality of ML models generating the personalized communication is further based on the treatment-plan-complexity score as taught by Hubenov et al. in the device as taught by Hua et al., Neumann, and Kiaie et al. in combination with the motivation(s) of making estimates for a treatment (see: paragraph [0041] of Hubenov et al.).
As per claim 13, Hua et al. and Neumann in combination teaches the method of claim 12, see discussion of claim 12. Neumann further teaches wherein the plurality of ML models comprises a plan-complexity ML model configured to:
--receive the treatment plan data, (see: paragraph [0090] where there is reception of a treatment/ameliorative plan data by a model (executed by processing module)) wherein the treatment plan data comprises (vi) a dietary restriction prescribed in the treatment plan (see: paragraph [0089] where there are dietary restrictions for the plan).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 12, and incorporated herein.
Hua et al. and Neumann in combination may not further, specifically teach:
1) --wherein the treatment plan data comprises three or more of (i) a number of medications prescribed in the treatment plan, (ii) an availability of the prescribed medications, (iii) a dosage frequency for the prescribed medications, (iv) a number of chronic conditions addressed by the treatment plan, (v) an indication that one or more of the prescribed medications is a high-risk medication, and (vii) an indication that the prescribed treatment plan requires special medical equipment;
2) --generate a plan-complexity score based on the treatment plan data, wherein the plan-complexity score indicates a complexity level of the treatment plan; and
3) --wherein (i) providing the data to the plurality of ML models comprises providing the treatment plan data to the plan-complexity ML model and (ii) the plurality of ML models generating the personalized communication is further based on the treatment-plan-complexity score.
Kiaie et al. teaches:
1) --wherein the treatment plan data comprises three or more of (i) a number of medications prescribed in the treatment plan, (ii) an availability of the prescribed medications, (iii) a dosage frequency for the prescribed medications, (iv) a number of chronic conditions addressed by the treatment plan, (v) an indication that one or more of the prescribed medications is a high-risk medication, and (vii) an indication that the prescribed treatment plan requires special medical equipment (see: paragraph [0182] where there is reception of a dosage frequency. Also see: paragraph [0051] where there is information about the availability of the medication).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) wherein the treatment plan data comprises three or more of (i) a number of medications prescribed in the treatment plan, (ii) an availability of the prescribed medications, (iii) a dosage frequency for the prescribed medications, (iv) a number of chronic conditions addressed by the treatment plan, (v) an indication that one or more of the prescribed medications is a high-risk medication, and (vii) an indication that the prescribed treatment plan requires special medical equipment as taught by Kiaie et al. in the method as taught by Hua et al. and Neumann in combination with the motivation(s) of improving the user’s therapy management (see: paragraph [0200] of Kiaie et al.).
Hubenov et al. teaches:
2) --generate a plan-complexity score based on the treatment plan data, (see: paragraph [0163] where there is a generation of a complexity score for a treatment) wherein the plan-complexity score indicates a complexity level of the treatment plan; (see: paragraph [0163] where the complexity score is based on how complex the treatment is)
3) --wherein (i) providing the data to the plurality of ML models comprises providing the treatment plan data to the plan-complexity ML model (see: paragraph [0163] where the ML model is given data and provides this complexity score) and (ii) the plurality of ML models generating the personalized communication is further based on the treatment-plan-complexity score (see: paragraph [0145] where there is a generation of an alert using processing logic and based on the complexity of the treatment. Also see: paragraph [0059] where there are multiple models).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 2) generate a plan-complexity score based on the treatment plan data, wherein the plan-complexity score indicates a complexity level of the treatment plan and have 3) wherein (i) providing the data to the plurality of ML models comprises providing the treatment plan data to the plan-complexity ML model and (ii) the plurality of ML models generating the personalized communication is further based on the treatment-plan-complexity score as taught by Hubenov et al. in the method as taught by Hua et al., Neumann, and Kiaie et al. in combination with the motivation(s) of making estimates for a treatment (see: paragraph [0041] of Hubenov et al.).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2010/0205008 to Hua et al. in view of U.S. 2022/0028511 to Neumann as applied to claims 2 and 12, and further in view of U.S. 2025/0174333 to Hubenov et al.
As per claim 9, Hua et al. and Neumann in combination teaches the device of claim 2, see discussion of claim 2. Hua et al. further teaches further comprising:
--a storage layer configured to (i) store the data regarding the patient (see: 910 of FIG. 9 where this data is being received and stored at least temporarily in memory as a computer is receiving this data) and (ii) store additional data regarding additional patients; (see: paragraph [0023] where there is historical data of other patients being stored) and
--wherein the plurality of ML models generating the personalized communication is further based on the relevant portion of the additional data (see: paragraph [0027] where the historical/additional data is used to generate the personalized communication/message. There are a plurality of models being used as explained in the independent claim. Also, the historical data includes a relevant portion of data as relevant data is being used to make this determination).
Hua et al. and Neumann in combination may not further, specifically teach:
1) --a search engine configured to (i) receive a portion of the data as a query and (ii) locate a relevant portion of the additional data that is similar to the query; and
2) --wherein the electronic device is further configured to (i) store the data in the storage layer, (ii) receive the additional data, (iii) store the additional data in the storage layer, (iv) determine that the data should be supplemented with the additional data, and (v) responsive to determining that the data should be supplemented, retrieve the relevant portion of the additional data using the search engine and provide the relevant portion of the additional data to the plurality of ML models prior to receiving the personalized communication.
Murrish et al. teaches:
1) --a search engine configured to (i) receive a portion of the data as a query and (ii) locate a relevant portion of the additional data that is similar to the query; (see: column 24, lines 10-15 where there is a search engine configured to receive a query and search for relevant data) and
2) --wherein the electronic device is further configured to (i) store the data in the storage layer, (see: column 31, lines 31-35 where there is data reception, indexing, and storage) (ii) receive the additional data, (see: column 31, lines 31-35 where there is data reception, indexing, and storage) (iii) store the additional data in the storage layer, (see: column 31, lines 31-35 where there is data reception, indexing, and storage) (iv) determine that the data should be supplemented with the additional data, (see: column 25, lines 1-10 where there is a determination made with all relevant data) and (v) responsive to determining that the data should be supplemented, retrieve the relevant portion of the additional data using the search engine and provide the relevant portion of the additional data to the plurality of ML models prior to receiving the personalized communication (see: column 25, lines 1-10 where there is a determination made with all relevant data).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) a search engine configured to (i) receive a portion of the data as a query and (ii) locate a relevant portion of the additional data that is similar to the query and have 2) wherein the electronic device is further configured to (i) store the data in the storage layer, (ii) receive the additional data, (iii) store the additional data in the storage layer, (iv) determine that the data should be supplemented with the additional data, and (v) responsive to determining that the data should be supplemented, retrieve the relevant portion of the additional data using the search engine and provide the relevant portion of the additional data to the plurality of ML models prior to receiving the personalized communication as taught by Murrish et al. in the device as taught by Hua et al. and Neumann in combination with the motivation(s) of improving the quality of healthcare (see: column 1, lines 45-49 of Murrish et al.).
As per claim 19, Hua et al. and Neumann in combination teaches the method of claim 12, see discussion of claim 12. Hua et al. further teaches:
--storing the data regarding the patient in a storage layer; (see: 910 of FIG. 9 where this data is being received and stored at least temporarily in memory as a computer is receiving this data)
--receiving additional data regarding additional patients; (see: paragraph [0023] where there is historical data of other patients being stored, thus this additional information is being received)
--storing the additional data regarding the additional patients in the storage layer; (see: paragraph [0023] where there is historical data of other patients being stored)
--the plurality of ML models generating the personalized communication is further based on the relevant portion of the additional data (see: paragraph [0027] where the historical/additional data is used to generate the personalized communication/message. There are a plurality of models being used as explained in the independent claim. Also, the historical data includes a relevant portion of data as relevant data is being used to make this determination).
Hua et al. and Neumann in combination may not further, specifically teach:
1) --determining that the data should be supplemented with the additional data;
2) --responsive to determining that the data should be supplemented, (i) retrieving a relevant portion of the additional data using a search engine and (ii) providing the relevant portion of the additional data to the plurality of ML models prior to receiving the personalized communication; and
3) --wherein the search engine is configured to receive a portion of the data as a query and locate the relevant portion of the additional data that is similar to the query.
Murrish et al. teaches:
1) --determining that the data should be supplemented with the additional data; (see: column 25, lines 1-10 where there is a determination made with all relevant data)
2) --responsive to determining that the data should be supplemented, (i) retrieving a relevant portion of the additional data using a search engine and (ii) providing the relevant portion of the additional data to the plurality of ML models prior to receiving the personalized communication; (see: column 25, lines 1-10 where there is a determination made with all relevant data, thus all relevant data is being retrieved and used) and
3) --wherein the search engine is configured to receive a portion of the data as a query and locate the relevant portion of the additional data that is similar to the query (see: column 24, lines 10-15 where there is a search engine configured to receive a query and search for relevant data).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) determine that the data should be supplemented with the additional data, 2) responsive to determining that the data should be supplemented, (i) retrieve a relevant portion of the additional data using a search engine and (ii) provide the relevant portion of the additional data to the plurality of ML models prior to receiving the personalized communication, and have 3) wherein the search engine is configured to receive a portion of the data as a query and locate the relevant portion of the additional data that is similar to the query as taught by Murrish et al. in the method as taught by Hua et al. and Neumann in combination with the motivation(s) of improving the quality of healthcare (see: column 1, lines 45-49 of Murrish et al.).
No Art Rejections
Claims 1, 4-8, 10-11, 14-18, and 20 do not have art rejections in view of the potential combination of prior art references which could be used to reject these claims not being a reasonable combination.
Conclusion
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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