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 .
Response to Amendment
Most of the previous 112(f) claim interpretations and 112(a) and 112(b) rejections given in view of these interpretations have been withdrawn, while one remains.
In light of the amendments, claims 1-11 are rejected under 35 U.S.C. 112(b).
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, claims 1-20 do not have art rejections.
Notice to Applicant
In the amendment dated 08/28/2026, the following has occurred: claims 1-20 have been amended; and no new claims have been added.
Claims 1-20 are pending.
Effective Filing Date: 01/30/2024
Response to Arguments
Claim Interpretation:
All of the previous claim interpretations have been withdrawn in view of the amendments to the claims, except for the storage layer in claim 9.
35 U.S.C. 112(a) and 112(b) Rejections:
All of the previous 112(a) and 112(b) rejections given in view of the claim interpretations have been withdrawn, except for the ones given to claim 9 for the term storage layer.
35 U.S.C. 101 Rejections:
Step 2A, Prong One:
Applicant states that the claims are eligible under Prong One. Examiner however respectfully disagrees as the claims include an abstract idea categorized under both mathematical concepts and certain methods of organizing human activity (from paragraph [0038] of the specification). There is an issue here with two things. First, it is something that “may” occur therefore it is not necessarily something that is an improvement. Second, the usage of this data and database is done so in an “apply it” manner where the improvement is merely an effect of application of this particular database.
Step 2A, Prong Two:
Applicant states that the claims now more clearly reflect the technical problem and solution as outlined in the specification. Examiner however respectfully disagrees. The description of using vector embeddings in combination with a time-series vector database may significantly improve storage and retrieval efficiency.
Additionally, Applicant argues that the vectorization and the database are not merely named as tools or generally linked to a particular environment, and nor do they provide insignificant activity. Examiner agrees with this expect for the determination that the additional elements are not being used as tools. They are being used as tools in an “apply it” manner based on the description above.
Step 2B:
Applicant argues that the previous office action analyzes the components in isolation. Examiner however would like to direct Applicant towards the present office action where the additional elements are “applying” the abstract idea using generic components.
35 U.S.C. 103 Rejections:
Applicant’s arguments are deemed moot in view of the withdrawn art rejections. The art rejections were withdrawn in view of the amendments to the claims.
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 storage layer” 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 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 storage layer” 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 1-11 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.
Claims 1 and 2 recite “one or more processors and memory” in the amendments, however it is unclear if there are multiple processor and multiple memories. Based on this lack of clarity, these claims have been deemed indefinite. Examiner is interpreting that there is exactly 1 memory, and separately, one or more processors.
Claim limitations “a storage layer” in claim 9 invokes 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 3-11 and 10-11 are rejected based on their dependency on claims 1 and 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;
b) a storage layer comprising b1) a time-series vector database;
c) a plurality of machine learning (ML) models, 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; and
e) an electronic device comprising one or more processors and memory, wherein memory stores instructions that, when executed by the processor, cause the processor to perform operations including:
1) receiving 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) vectorizing the data using the transformer, wherein vectorizing the data comprises translating the treatment plan data and the other data into vector embeddings comprising numerical representations that capture (i) semantic meaning embedded within the treatment plan data and the other data, (ii) relationships or similarities among portions of the treatment plan data and the other data, and (iii) connections between medical concepts represented in the treatment plan data and the other data, the vector embeddings being generated for use as input data to at least one of the plurality of ML models as part of generating a communication plan and a personalized communication;
3) storing the treatment plan data and the other data in the storage layer and the vector embeddings in the time-series vector database after vectorizing the data;
4) providing 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) receiving 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) providing one or more vector embeddings from the time-series vector database to the patient-persona ML model, wherein the one or more vector embeddings represent other data regarding at least (i) the medical record associated with the patient and (ii) the social media account associated with the patient;
7) receiving persona indicators from the patient-persona ML model responsive to providing the one or more vector embeddings to the patient-persona ML model, wherein the persona indicators are generated based at least in part on the one or more vector embeddings and indicate (i) an ability of the patient to follow a prescribed regime and (ii) a communication style of the patient;
8) providing the treatment plan data from the storage layer, the plan-complexity score, and the persona indicators to the adherence-probability ML model;
9) receiving 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) providing the treatment plan data from the storage layer, the adherence-probability score, and the persona indicators to the personalized-communication ML model;
11) applying the personalized-communication ML model to generate a communication plan and a personalized communication based on the treatment plan data, the adherence-probability score, and the persona indicators generated based at least in part on the one or more vector embeddings;
12) receiving 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
13) delivering 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) vectorizing the data, wherein vectorizing the data comprises translating the treatment plan data and the other data into vector embeddings comprising numerical representations that capture (i) semantic meaning embedded within the treatment plan data and the other data, (ii) relationships or similarities among portions of the treatment plan data and the other data, and (iii) connections between medical concepts represented in the treatment plan data and the other data, the vector embeddings being generated for use as input data to at least one of the plurality of models as part of generating a communication plan and a personalized communication. These steps correspond to Mathematical Concepts.
Claim 1 also recites, in part, performing the steps of 1) receiving 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) vectorizing the data, 3) storing the treatment plan data and the other data and the vector embeddings after vectorizing the data, 4) providing the treatment plan data from the storage layer to the plan-complexity 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) receiving a plan-complexity score from the plan-complexity model responsive to providing the treatment plan data to the plan-complexity model, wherein the plan-complexity score indicates a complexity level of the treatment plan, 6) providing one or more vector embeddings to the patient-persona model, wherein the one or more vector embeddings represent other data regarding at least (i) the medical record associated with the patient and (ii) the social media account associated with the patient, 7) receiving persona indicators from the patient-persona model responsive to providing the one or more vector embeddings to the patient-persona model, wherein the persona indicators are generated based at least in part on the one or more vector embeddings and indicate (i) an ability of the patient to follow a prescribed regime and (ii) a communication style of the patient, 8) providing the treatment plan data, the plan-complexity score, and the persona indicators to the adherence-probability model, 9) receiving an adherence-probability score from the adherence-probability model responsive to providing the treatment plan data, the plan-complexity score, and the persona indicators to the adherence-probability model, wherein the adherence-probability score indicates a probability of the patient adhering to the treatment plan, 10) providing the treatment plan data, the adherence-probability score, and the persona indicators to the personalized-communication model, 11) applying the personalized-communication model to generate a communication plan and a personalized communication based on the treatment plan data, the adherence-probability score, and the persona indicators generated based at least in part on the one or more vector embeddings, 12) receiving a communication plan and the personalized communication from the personalized-communication 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 13) delivering 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.
Claim 2 recites an electronic device comprising:
b) a storage layer comprising b1) a time-series vector database and a) a transformer;
e) one or more processors and memory, wherein the memory stores instructions that, when executed by the processor, cause the processor to perform operations including:
14) receiving receive data regarding a patient, wherein the data comprises (i) treatment plan data regarding a 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;
15) vectorizing the data using the transformer to generate vector embeddings, wherein vectorizing the data comprises translating the treatment plan data and the other data into vector embeddings comprising numerical representations that capture (i) semantic meaning embedded within the treatment plan data and the other data, (ii) relationships or similarities among portions of the treatment plan data and the other data, and (iii) connections between medical concepts represented in the treatment plan data and the other data, the vector embeddings being generated for use as input data to at least one of a plurality of ML models as part of generating a communication plan and a personalized communication;
16) storing the treatment plan data and the other data in the storage layer and the vector embeddings in the time-series vector database;
17) providing the data and at least one or more of the vector embeddings from the time-series vector database to the plurality of ML models configured to generate the 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;
18) receiving the personalized communication from the plurality of ML models responsive to providing the data to the plurality of ML models, wherein the personalized communication is generated based at least in part on the one or more vector embeddings, and wherein the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient; and
19) delivering the personalized communication to the patient.
Claim 2 recites, in part, performing the steps of 15) vectorizing the data to generate vector embeddings, wherein vectorizing the data comprises translating the treatment plan data and the other data into vector embeddings comprising numerical representations that capture (i) semantic meaning embedded within the treatment plan data and the other data, (ii) relationships or similarities among portions of the treatment plan data and the other data, and (iii) connections between medical concepts represented in the treatment plan data and the other data, the vector embeddings being generated for use as input data to at least one of a plurality of models as part of generating a communication plan and a personalized communication. These steps correspond to Mathematical Concepts.
Claim 2 also recites, in part, performing the steps of 14) receiving receive data regarding a patient, wherein the data comprises (i) treatment plan data regarding a 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, 15) vectorizing the data, 16) storing the treatment plan data and the other data and the vector embeddings, 17) providing the data and at least one or more of the vector embeddings from the time-series vector database to the plurality of models configured to generate the 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, 18) receiving the personalized communication from the plurality of models responsive to providing the data to the plurality of models, wherein the personalized communication is generated based at least in part on the one or more vector embeddings, and wherein the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient, and 19) delivering the personalized communication to the patient. Claim 12 recites similar limitations and is 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. Claims 9 and 11 add further additional elements and these elements are addressed below in Prong Two. 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, b) a storage layer comprising b1) a time-series vector database, c) a plurality of machine learning (ML) models, 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 (if hardware), e) an electronic device comprising one or more processors and memory, wherein memory stores instructions that, when executed by the processor, cause the processor to perform operations, f) the electronic device further comprises a search engine (in claim 9), and g) an object data store (in claim 11) to perform the claimed steps.
The a) transformer, b) storage layer comprising b1) a time-series vector database, d) delivery network, e) electronic device comprising one or more processors and memory, wherein memory stores instructions that, when executed by the processor, cause the processor to perform operations, f) the electronic device further comprises a search engine, and g) object data store 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, 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 in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (see: MPEP 2106.05(f))
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, b) a storage layer comprising b1) a time-series vector database, c) a plurality of machine learning (ML) models, 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, e) an electronic device comprising one or more processors and memory, wherein memory stores instructions that, when executed by the processor, cause the processor to perform operations, f) the electronic device further comprises a search engine, and g) an object data store to perform the claimed steps amounts to no more than 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) recites 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)).
The current invention determines and delivers a personalized communication utilizing a) a transformer, b) a storage layer comprising b1) a time-series vector database, c) a plurality of machine learning (ML) models, d) a delivery network, e) an electronic device comprising one or more processors and memory, wherein memory stores instructions that, when executed by the processor, cause the processor to perform operations, f) the electronic device further comprises a search engine, and g) an object data store, thus the computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer using machine learning.
Mere instructions to apply an exception using generic computer components 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.
No Art Rejections
Claims 1-20 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
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 Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
