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 .
Drawings
The drawings are objected to because the arrows leaving box 306 are missing “yes” and “no” labels. The arrow going right from box 306 should be labeled “no”, and the arrow going straight down from box 306 should be labeled “yes”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Par. [0025], line 9: “analysis would done” should be changed to “analysis would be done”.
Par. [0047], line 6: please remove the first instance of the phrase “The training data can further include”.
Par. [0051], line 2: “intervene take action” should be changed to “intervene and take action”.
Appropriate correction is required.
Claim Objections
Claim 16 objected to because of the following informalities:
Line 11: please indent limitation beginning with “triggering an intervention …” in order to line up with other limitations.
Appropriate correction is required.
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:
“[the computer/processor] predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model” in claim 1 (lines 6-7), claim 11 (lines 9-10), and claim 16 (lines 7-8).
“the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients” in claim 1 (lines 8-9), claim 11 (lines 11-12), and claim 16 (lines 9-10).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Examiner notes that for a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function (MPEP 2181(II)(B)). Evidence of such an algorithm for covering the corresponding structure, material, or acts is not found. Please see 35 U.S.C. 112(a) and 112(b) rejections below.
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
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 1-20 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. Claims 1, 11, and 16 recite the limitations “[the computer/processor] predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model” and “the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients”. The Applicant’s specification appears to essentially recite these claim limitations (Par. [0022]; Par. [0049]; Figs. 3-4), wherein the functional intent of predicting a probability of attrition from a medical treatment for the patient using a ML model is recited, but without disclosing how this functional intent is achieved. It is understood that a plurality of prediction data (i.e., patient medical data (Fig. 8), patient order data (Fig. 9), and patient intervention data (Fig. 10)) is used as an input (Fig. 4, # 180) for the Attrition ML Model (Fig. 4, # 164), in order to determine an attrition risk score (Fig. 4, # 410) which is used in determining a probability of attrition. However, it is not explained how the ML model actually uses the inputs (i.e., prediction data) in order to determine an attrition risk score. The ML model is represented as a black box, instead of providing algorithms/steps for how the ML model is set up or trained to interpret input data in order to produce a corresponding prediction of a probability of attrition. Therefore, the Applicant’s specification is not found to have written description support for the indicated claim limitations.
MPEP 2161.01(I) explains that it is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015). MPEP 2161.01(I) also explains that the description requirement of the patent statute requires a description of an invention, not an indication of a result that one might achieve if one made that invention. Original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed (MPEP 2161.01(I), MPEP 2163.02, and MPEP 2181(IV)).
*All other claims are rejected due to their dependency on a rejected claim.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claims 1, 11, and 16 recite the limitations “[the computer/processor] predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model” and “the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients”. These claim limitations are not sufficiently described in the specification in such a way to enable one skilled in the art to make and/or use the invention. The Wands factors detailed in MPEP 2164.01(a) have been considered. For example, (A) the breadth of the claims was considered. The broadest reasonable interpretation of these limitations includes a computer/processor being able to provide prediction data (i.e., patient medical data and patient order data) to a machine learning (ML) model in order to predict a probability of attrition from a medical treatment for the patient. This interpretation is considered to be very broad, as there are a plurality of possibilities for patient medical data (e.g., age, race, height, weight, eye color, insurance, family history, etc.) and patient order history (e.g., types of items ordered, number of items ordered, price of items ordered, size of items ordered, price amount covered by insurance, etc.). There are also a plethora of medical treatments that the ML model could be predicting a probability of attrition for, such as chemotherapy, phototherapy, electrostimulation, preventative treatment, surgical treatment, non-surgical treatment, skin treatment, heart treatment, liver treatment, etc. This broad scope of the claims are not commensurate with the scope of enablement provided to one of skill in the art by the Applicant’s specification. Only “CPAP therapy” was disclosed in the Applicant’s specification (Par. [0075]). (B) The nature of the invention was also considered. The nature of the present invention involves applying artificial intelligence (i.e., machine learning) to help in predicting a patient’s likelihood of reducing the strength/effectiveness of medical treatment (i.e., attrition) in order to attempt to intervene so that medical treatment is able to continue and have a better chance at success. (D) The level of one of ordinary skill in the art was also considered. One of ordinary skill in the art would recognize that a machine learning model could be made to help solve the Applicant’s identified problem of preventing attrition of medical treatment of a patient. One of ordinary skill in the art would recognize that a machine learning algorithm would learn patterns/correlations among inputs and outputs. However, the level of ordinary skill in the art would not include knowing exactly which machine learning algorithm to use and how to set up the machine learning model. One of ordinary skill in the art would also not know how much training data to use in order to make a machine learning model that is sufficiently accurate in predicting attrition rate for all possible medical treatments. (F) The amount of direction provided by the inventor, (G) The existence of working examples, and (H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure were also considered. There is not much direction provided by the Inventor outside of a general explanation of different types of inputs that can be fed into the ML model in order to determine an attrition risk. The Inventor also generally explains to use a suitable ML model (e.g., a random forest ML model) (Par. [0022]), or a logistic progression ML model, a gradient boosting ML model, a fully randomized trees ML model, supervised ML model, trained DNN, or a regression model (Par. [0046]), without any specific guidance on using any of these particular ML models. There is also a lack of working examples. There is simply a brief suggestion of determining a risk for attrition for a patient with sleep related diagnoses for CPAP therapy (Par. [0075]). There are no examples of how a supervised ML model would be set up. For instance, what inputs to the supervised ML model would correspond to an increased risk of attrition? A very large quantity of experimentation would be needed in order to make or use the invention. In order to make a ML model that predicts the probability of attrition from any medical treatment for any patient would require a multitude of training data and rules in order to be sufficiently accurate. Therefore, claims 1-20 do not satisfy the enablement requirement under 35 U.S.C. 112(a).
*All other claims are rejected due to their dependency on a rejected claim.
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 1-20 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 “[the computer/processor] predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model” and “the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients” in claims 1, 11, and 16 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. It is unclear how the ML model is actually trained or how it generally functions in order to predict a probability of attrition. Instead, the specification generally explains different inputs and explains that these inputs are fed into a ML model in order to determine at attrition risk score, which can be compared to a threshold to determine the probability of attrition. However, there are no examples showing how the attrition risk/prediction is actually determined. For example, what combination of input values would lead to a patient being at risk for attrition? Instead, Applicant’s disclosure just appears to reiterate these claim limitations and represents the ML model as a black box. 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.
Claim 1 recites the limitation “attrition” in line 10, whereas attrition was already introduced in claim 1 (line 6). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 1 recites the limitation “attrition” in line 11, whereas attrition was already introduced in claim 1 (line 6). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 6 recites the limitation “medical items” in line 2, whereas medical items was already introduced in a claim that claim 6 depends from (claim 1, line 5). It is unclear whether the Applicant intended to claim the same or a different medical items. Consider changing to “the medical items”.
The limitation “items” renders claim 6 (line 3) indefinite. It is unclear if these “items” are the same as the “medical items” referenced earlier in claim 6 (line 2) and introduced in claim 1 (line 5). Or, are these items new/separate from the “medical items”?
Claim 6 recites the limitation “medical items” in line 4, whereas medical items was already introduced in a claim that claim 6 depends from (claim 1, line 5). It is unclear whether the Applicant intended to claim the same or a different medical items. Consider changing to “the medical items”.
Claim 9 recites the limitation "the automated communication" in line 1. There is insufficient antecedent basis for this limitation in the claim. Perhaps claim 9 was intended to be dependent on either claim 7 or claim 8.
Claim 10 recites the limitation “an intervention” in line 1, whereas an intervention was already introduced in a claim that claim 10 depends from (claim 1, line 10). It is unclear whether the Applicant intended to claim the same or a different intervention. Consider changing to “the intervention”.
Claim 10 recites the limitation “attrition” in line 2, whereas attrition was already introduced in a claim that claim 10 depends from (claim 1, line 6). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 10 recites the limitation “attrition” in line 3, whereas attrition was already introduced in a claim that claim 10 depends from (claim 1, line 6). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 10 recites the limitation “attrition” in line 4, whereas attrition was already introduced in a claim that claim 10 depends from (claim 1, line 6). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 11 recites the limitation “attrition” in line 13, whereas attrition was already introduced in claim 11 (line 9). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 11 recites the limitation “attrition” in line 14, whereas attrition was already introduced in claim 11 (line 9). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 13 recites the limitation “medical items” in line 6, whereas medical items was already introduced in a claim that claim 13 depends from (claim 11, line 8). It is unclear whether the Applicant intended to claim the same or a different medical items. Consider changing to “the medical items”.
The limitation “items” renders claim 13 (line 6) indefinite. It is unclear if these “items” are the same as the “medical items” referenced earlier in claim 13 (line 6) and introduced in claim 11 (line 8). Or, are these items new/separate from the “medical items”?
Claim 13 recites the limitation “medical items” in line 8, whereas medical items was already introduced in a claim that claim 13 depends from (claim 11, line 8). It is unclear whether the Applicant intended to claim the same or a different medical items. Consider changing to “the medical items”.
Claim 15 recites the limitation “an intervention” in line 1, whereas an intervention was already introduced in a claim that claim 15 depends from (claim 11, line 13). It is unclear whether the Applicant intended to claim the same or a different intervention. Consider changing to “the intervention”.
Claim 15 recites the limitation “attrition” in line 2, whereas attrition was already introduced in a claim that claim 15 depends from (claim 11, line 9). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 15 recites the limitation “attrition” in line 3, whereas attrition was already introduced in a claim that claim 15 depends from (claim 11, line 9). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 15 recites the limitation “attrition” in line 4, whereas attrition was already introduced in a claim that claim 15 depends from (claim 11, line 9). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 16 recites the limitation “attrition” in line 11, whereas attrition was already introduced in claim 16 (line 7). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 16 recites the limitation “attrition” in line 12, whereas attrition was already introduced in claim 16 (line 7). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 18 recites the limitation “medical items” in line 6, whereas medical items was already introduced in a claim that claim 18 depends from (claim 16, line 6). It is unclear whether the Applicant intended to claim the same or a different medical items. Consider changing to “the medical items”.
The limitation “items” renders claim 18 (line 6) indefinite. It is unclear if these “items” are the same as the “medical items” referenced earlier in claim 18 (line 6) and introduced in claim 16 (line 6). Or, are these items new/separate from the “medical items”?
Claim 18 recites the limitation “medical items” in line 8, whereas medical items was already introduced in a claim that claim 18 depends from (claim 16, line 6). It is unclear whether the Applicant intended to claim the same or a different medical items. Consider changing to “the medical items”.
Claim 20 recites the limitation “an intervention” in lines 1-2, whereas an intervention was already introduced in a claim that claim 20 depends from (claim 18, line 11). It is unclear whether the Applicant intended to claim the same or a different intervention. Consider changing to “the intervention”.
Claim 20 recites the limitation “attrition” in line 2, whereas attrition was already introduced in a claim that claim 20 depends from (claim 16, line 7). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 20 recites the limitation “attrition” in line 3, whereas attrition was already introduced in a claim that claim 20 depends from (claim 16, line 7). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
Claim 20 recites the limitation “attrition” in line 4, whereas attrition was already introduced in a claim that claim 20 depends from (claim 16, line 7). It is unclear whether the Applicant intended to claim the same or a different attrition. Consider changing to “the attrition”.
*All other claims are rejected due to their dependency on a rejected claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of predicting a probability of attrition from a medical treatment for a patient based on prediction data, and intervening to discourage attrition based on the predicted probability) without significantly more.
Step 1
Independent claims 1, 11, and 16 are directed to a method, an apparatus, and a non-transitory computer-readable medium, and thus meet the requirements for step 1.
Step 2A, Prong 1
Regarding claims 1, 11, and 16, the following steps recite an abstract idea:
“predicting a probability of attrition from a medical treatment for the patient” is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(III), the mental process grouping includes observations, evaluation, judgements, and opinions. In this case, a human could mentally predict a probability of attrition from a medical treatment for the patient by observing, evaluating, and making judgements of a patient and their medical treatment.
Step 2A, Prong 2
Regarding claims 1, 11, and 16, the claims do not include any additional elements that integrate the abstract idea into a practical application. The following elements do not add any meaningful limitation to the abstract idea:
identifying a plurality of prediction data, the prediction data comprising both: (i) patient medical data comprising a plurality of characteristics relating to a medical history for the patient, and (ii) patient order data comprising a plurality of characteristics relating to an order history for medical items relating to the patient – insignificant pre-solution activity, i.e. mere data gathering [MPEP 2106.05(g)]
… based on providing the prediction data to a machine learning (ML) model, wherein the ML model is trained to predict the probability – The ML model is described at a high level of generality in the Applicant’s specification, such as a suitable ML model (e.g., a random forest ML model) (Par. [0022]) or a logistic progression ML model, a gradient boosting ML model (e.g., light GBM classifier), a fully randomized trees ML model, any suitable supervised ML model (e.g., a trained DNN, regression model, or any other suitable supervised ML model) (Par. [0046]). The involvement of the “ML model” is insignificant extra-solution activity in that it amounts to generic computer implementation of the abstract idea [MPEP 2106.04(a)(2)(III)(C)].
… using prior patient medical data and prior patient order data, relating to a plurality of prior patients – insignificant pre-solution activity, i.e. mere data gathering [MPEP 2106.05(g)].
triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition – amounts to merely outputting data, which is insignificant extra-solution activity [MPEP 2106.05(g)].
a memory; and a hardware processor communicatively coupled to the memory -– The memory is described at a high level of generality in the Applicant’s specification, as it is explained that the memory may include one or more memory devices having blocks of memory associated with physical addresses, such as random access memory (RAM), read only memory (ROM), flash memory, or other types of volatile and/or non-volatile memory (Par. [0041]). The processor is also described at a high level of generality in the Applicant’s specification, as it is explained that the processor is representative of a single central processing unit (CPU), multiple CPUs, a single CPU having multiple processing cores, graphics processing units (GPUs) having multiple execution paths, and the like (Par. [0040]). The involvement of the “memory” and “processor” is insignificant extra-solution activity in that it amounts to generic computer implementation of the abstract idea [MPEP 2106.04(a)(2)(III)(C)].
Therefore, the claims are directed to an abstract idea without a practical application.
Step 2B
The additional elements of claims 1, 11, and 16, when considered either individually or in an ordered combination, are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the “ML Model”, the “memory”, and the “processor”, along with their associated functions and components, are recited with a high level of generality and simply amount to implementing the abstract idea on a computer. The additional elements that were considered insignificant extra-solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine, and conventional. Also, simply appending well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept [MPEP 2106.05(d)].
identifying a plurality of prediction data, the prediction data comprising both: (i) patient medical data comprising a plurality of characteristics relating to a medical history for the patient, and (ii) patient order data comprising a plurality of characteristics relating to an order history for medical items relating to the patient – MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”)
… based on providing the prediction data to a machine learning (ML) model, wherein the ML model is trained to predict the probability – Varghese, et al. (U.S. PGPub No. 2016/0314418) explains that conventional attrition prediction models use machine learning methods with numerous attrition triggers as input databases (Par. [0005]).
… using prior patient medical data and prior patient order data, relating to a plurality of prior patients – MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”) and MPEP 2106.05(d)(II)(“iv. Storing and retrieving information in memory”).
triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition - Varghese, et al. (U.S. PGPub No. 2016/0314418) explains that the HR officer is instantly notified of the employee’s cumulative risk flag and can take immediate remedial action (i.e., intervention) (Par. [0064-0065]).
a memory; and a hardware processor communicatively coupled to the memory -– MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”) and MPEP 2106.05(d)(II)(“iv. Storing and retrieving information in memory”).
Therefore, the claims are directed to an abstract idea without a practical application and without significantly more.
Dependent claims
Regarding dependent claims 2, 4-6, 12-14, 17-19, the limitations only further define insignificant extra-solution activity of gathering data.
Regarding dependent claims 8-9, the limitations only further define insignificant extra-solution activity of generic computer implementation of the abstract idea.
Regarding dependent claims 3, 10, 15, and 20, the limitations only further define the abstract idea.
Regarding dependent claim 7, the limitations only further define insignificant extra-solution activity of outputting data.
Therefore, claims 1-20 are unpatentable under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6-13, 15-18, and 20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Williams, et al. (U.S. PGPub No. 2022/0273873).
Regarding claim 1, Williams teaches (Fig. 1A, # 106, 116, 156) a computer-implemented method (Abstract; Par. [0023] – the attrition prevention application is a computer application; Par. [0024-0026]), comprising:
(Fig. 1A, # 112, 114; Fig. 2) identifying a plurality of prediction data (Par. [0025]), the prediction data comprising both: (i) (Fig. 2, # 204) patient medical data comprising a plurality of characteristics relating to a medical history for the patient (Par. [0056]; Par. [0060] – patient’s age, weight, gender), and (ii) (Fig. 7) patient order data comprising a plurality of characteristics relating to an order history for medical items relating to the patient (Par. [0109] – Historical usage, ordering, complaint, and other data may also be collected as input, yielding an estimated attrition date for patients);
(Fig. 1A, # 106, 116 – ML system, 156 – ML Algorithm) predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model (Par. [0004]; Par. [0032] – usage, treatment, and service data contain the crucial variables used to drive an attrition prediction algorithm executed by the attrition prevention engine 106 as well as attrition prevention application 150; Par. [0035] – the ML algorithm may use the data as predictor variables in order to produce a probability value that represents a probability of patient attrition (i.e., a user’s likelihood to abandon use of the drug delivery device)),
wherein (Fig. 1A, # 106, 112 and 114 – prior patient medical data, 116 – ML system, 156 – ML Algorithm; Fig. 2; Fig. 7) the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients (Par. [0035] – the ML algorithm may use the data as predictor variables in order to produce a probability value that represents a probability of patient attrition. An example of the set of data collected is shown in Fig. 2 and may be used for the attrition prediction algorithm; Par. [0109] – order data); and
(Fig. 1, # 156) triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition (Par. [0037] – Once the attrition probability and recommendation are stored within the data lake, the ML algorithm 156 is operable to trigger alarms to implement various interventions. The attrition probability may cause ML algorithm 156 to generate different responses).
Therefore, claim 1 is unpatentable over Williams, et al.
Regarding claim 2, Williams teaches the computer-implemented method of claim 1, wherein (Fig. 1, # 156) the prediction data further comprises patient intervention data comprising a plurality of characteristics relating to past interventions with the patient (Par. [0039]).
Therefore, claim 2 is unpatentable over Williams, et al.
Regarding claim 3, Williams teaches the computer-implemented method of claim 2, wherein (Fig. 1, # 156) the ML model is trained to predict the probability further using prior patient intervention data (Par. [0039]).
Therefore, claim 3 is unpatentable over Williams, et al.
Regarding claim 4, Williams teaches the computer-implemented method of claim 2, wherein the patient medical data comprises (Fig. 1A; Fig. 2, # 202, 204) two or more of: (i) demographic data for the patient, (ii) medical equipment data for the patient, (iii) care provider data for the patient, and (iv) prior diagnosis information for the patient (Par. [0056], [0058], and [0060]).
Therefore, claim 4 is unpatentable over Williams, et al.
Regarding claim 6, Williams teaches the computer-implemented method of claim 2, wherein the patient order data comprises (Fig. 7) two or more of: (i) statistical information for the order history for medical items relating to the patient, (ii) information describing items previously ordered by the patient as part of the order history, and (iii) payment history information relating to the order history for medical items relating to the patient (Par. [0109] – historical ordering; It would be well known that historical ordering would include two or more of the items on this list).
Therefore, claim 6 is unpatentable over Williams, et al.
Regarding claim 7, Williams teaches the computer-implemented method of claim 1, wherein (Fig. 1A) triggering the intervention comprises triggering an automated communication to at least one of: (i) the patient, (ii) a care provider associated with the patient, or (iii) a care facility associated with the patient (Par. [0021]; Par. [0037] – a lower attrition probability may cause the generation of an automated response. A user may receive a push notification in their cloud-connected diabetes management device).
Therefore, claim 7 is unpatentable over Williams, et al.
Regarding claim 8, Williams teaches the computer-implemented method of claim 7, wherein (Fig. 3, # 308) the automated communication comprises at least one of: (i) an automated telephone call, (ii) a short message service (SMS) message, (iii) a multimedia messaging service message (MMS), or (iv) an e-mail message (Par. [0037] – calling, emailing, texting, sending a notification; Par. [0065]).
Therefore, claim 8 is unpatentable over Williams, et al.
Regarding claim 9, Williams teaches the computer-implemented method of claim 1, wherein (Fig. 1A; Fig. 3, # 308) the automated communication comprises an electronic communication to the patient (Par. [0037]; Par. [0065]).
Therefore, claim 9 is unpatentable over Williams, et al.
Regarding claim 10, Williams teaches the computer-implemented method of claim 1, wherein (Fig. 1A, # 156) triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition comprises:
determining that the predicted probability of attrition exceeds a threshold value, and in response triggering the intervention (Par. [0035]; Par. [0037] – For example, a lower attrition probability may cause generation of an automated response, while a higher attrition probability may cause the ML algorithm 156 to initiate human interaction; This is representative of threshold values for the predicted probability of attrition).
Therefore, claim 10 is unpatentable over Williams, et al.
Regarding claim 11, Williams teaches (Fig. 1A, # 100) an apparatus (Par. [0024]; Par. [0151] – the various elements of the devices, apparatuses, or systems as previously described with reference to Figs. 1A-9…) comprising:
(Figs. 1A-B, # 146 – memory) a memory (Par. [0043]); and
(Fig. 1A-B, # 134) a hardware processor communicatively coupled to the memory (Par. [0043]), the hardware processor configured to perform operations comprising:
(Fig. 1A, # 112, 114; Fig. 2) identifying a plurality of prediction data (Par. [0025]), the prediction data comprising both: (i) (Fig. 2, # 204) patient medical data comprising a plurality of characteristics relating to a medical history for the patient (Par. [0056]; Par. [0060] – patient’s age, weight, gender), and (ii) (Fig. 7) patient order data comprising a plurality of characteristics relating to an order history for medical items relating to the patient (Par. [0109] – Historical usage, ordering, complaint, and other data may also be collected as input, yielding an estimated attrition date for patients);
(Fig. 1A, # 106, 116 – ML system, 156 – ML Algorithm) predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model (Par. [0004]; Par. [0032] – usage, treatment, and service data contain the crucial variables used to drive an attrition prediction algorithm executed by the attrition prevention engine 106 as well as attrition prevention application 150; Par. [0035] – the ML algorithm may use the data as predictor variables in order to produce a probability value that represents a probability of patient attrition (i.e., a user’s likelihood to abandon use of the drug delivery device)),
wherein (Fig. 1A, # 106, 112 and 114 – prior patient medical data, 116 – ML system, 156 – ML Algorithm; Fig. 2; Fig. 7) the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients (Par. [0035] – the ML algorithm may use the data as predictor variables in order to produce a probability value that represents a probability of patient attrition. An example of the set of data collected is shown in Fig. 2 and may be used for the attrition prediction algorithm; Par. [0109] – order data); and (Fig. 1, # 156) triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition (Par. [0037] – Once the attrition probability and recommendation are stored within the data lake, the ML algorithm 156 is operable to trigger alarms to implement various interventions. The attrition probability may cause ML algorithm 156 to generate different responses).
Therefore, claim 11 is unpatentable over Williams, et al.
Regarding claim 12, Williams teaches the apparatus of claim 11, wherein (Fig. 1, # 156) the prediction data further comprises patient intervention data comprising a plurality of characteristics relating to past interventions with the patient (Par. [0039]).
Therefore, claim 12 is unpatentable over Williams, et al.
Regarding claim 13, Williams teaches the apparatus of claim 12,
wherein the patient medical data comprises (Fig. 1A; Fig. 2, # 202, 204) two or more of: (i) demographic data for the patient, (ii) medical equipment data for the patient, (iii) care provider data for the patient, and (iv) prior diagnosis information for the patient (Par. [0056], [0058], and [0060]), and
wherein (Fig. 7) the patient order data comprises two or more of: (i) statistical information for the order history for medical items relating to the patient, (ii) information describing items previously ordered by the patient as part of the order history, and (iii) payment history information relating to the order history for medical items relating to the patient (Par. [0109] – historical ordering; It would be well known that historical ordering would include two or more of the items on this list).
Therefore, claim 13 is unpatentable over Williams, et al.
Regarding claim 15, Williams teaches the apparatus of claim 11, wherein (Fig. 1A, # 156) triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition comprises:
determining that the predicted probability of attrition exceeds a threshold value, and in response triggering the intervention (Par. [0035]; Par. [0037] – For example, a lower attrition probability may cause generation of an automated response, while a higher attrition probability may cause the ML algorithm 156 to initiate human interaction; This is representative of threshold values for the predicted probability of attrition).
Therefore, claim 15 is unpatentable over Williams, et al.
Regarding claim 16, Williams teaches (Fig. 1, # 106) a non-transitory computer-readable medium (Par. [0026] – the attrition prevention engine 106 may be programming instructions embodied in a non-transitory computer readable medium) comprising instructions that, when executed by (Fig. 1A-B, # 134) a processor (Par. [0043]), cause the processor to perform operations comprising:
(Fig. 1A, # 112, 114; Fig. 2) identifying a plurality of prediction data (Par. [0025]), the prediction data comprising both: (i) (Fig. 2, # 204) patient medical data comprising a plurality of characteristics relating to a medical history for the patient (Par. [0056]; Par. [0060] – patient’s age, weight, gender), and (ii) (Fig. 7) patient order data comprising a plurality of characteristics relating to an order history for medical items relating to the patient (Par. [0109] – Historical usage, ordering, complaint, and other data may also be collected as input, yielding an estimated attrition date for patients);
(Fig. 1A, # 106, 116 – ML system, 156 – ML Algorithm) predicting a probability of attrition from a medical treatment for the patient based on providing the prediction data to a machine learning (ML) model (Par. [0004]; Par. [0032] – usage, treatment, and service data contain the crucial variables used to drive an attrition prediction algorithm executed by the attrition prevention engine 106 as well as attrition prevention application 150; Par. [0035] – the ML algorithm may use the data as predictor variables in order to produce a probability value that represents a probability of patient attrition (i.e., a user’s likelihood to abandon use of the drug delivery device)),
wherein (Fig. 1A, # 106, 112 and 114 – prior patient medical data, 116 – ML system, 156 – ML Algorithm; Fig. 2; Fig. 7) the ML model is trained to predict the probability using prior patient medical data and prior patient order data, relating to a plurality of prior patients (Par. [0035] – the ML algorithm may use the data as predictor variables in order to produce a probability value that represents a probability of patient attrition. An example of the set of data collected is shown in Fig. 2 and may be used for the attrition prediction algorithm; Par. [0109] – order data); and
(Fig. 1, # 156) triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition (Par. [0037] – Once the attrition probability and recommendation are stored within the data lake, the ML algorithm 156 is operable to trigger alarms to implement various interventions. The attrition probability may cause ML algorithm 156 to generate different responses).
Therefore, claim 16 is unpatentable over Williams, et al.
Regarding claim 17, Williams teaches the non-transitory computer-readable medium of claim 16, wherein (Fig. 1, # 156) the prediction data further comprises patient intervention data comprising a plurality of characteristics relating to past interventions with the patient (Par. [0039]).
Therefore, claim 17 is unpatentable over Williams, et al.
Regarding claim 18, Williams teaches the non-transitory computer-readable medium of claim 17,
wherein the patient medical data comprises (Fig. 1A; Fig. 2, # 202, 204) two or more of: (i) demographic data for the patient, (ii) medical equipment data for the patient, (iii) care provider data for the patient, and (iv) prior diagnosis information for the patient (Par. [0056], [0058], and [0060]), and
wherein (Fig. 7) the patient order data comprises two or more of: (i) statistical information for the order history for medical items relating to the patient, (ii) information describing items previously ordered by the patient as part of the order history, and (iii) payment history information relating to the order history for medical items relating to the patient (Par. [0109] – historical ordering; It would be well known that historical ordering would include two or more of the items on this list).
Therefore, claim 18 is unpatentable over Williams, et al.
Regarding claim 20, Williams teaches the non-transitory computer-readable medium of claim 16, wherein (Fig. 1A, # 156) triggering an intervention to prophylactically discourage attrition from the medical treatment for the patient, based on the predicted probability of attrition comprises:
determining that the predicted probability of attrition exceeds a threshold value, and in response triggering the intervention (Par. [0035]; Par. [0037] – For example, a lower attrition probability may cause generation of an automated response, while a higher attrition probability may cause the ML algorithm 156 to initiate human interaction; This is representative of threshold values for the predicted probability of attrition).
Therefore, claim 20 is unpatentable over Williams, et al.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 5, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Williams, et al. (U.S. PGPub No. 2022/0273873) in view of Dibari, et al. (U.S. PGPub No. 2021/0090733).
Regarding claims 5, 14, and 19, Williams teaches the computer-implemented method of claim 4, the apparatus of claim 13, and the non-transitory computer-readable medium of claim 18. Williams does not teach the limitation of instant claims 5, 14, and 19, that is wherein the patient medical data further comprises sentiment analysis generated using natural language processing (NLP) for one or more medical notes relating to the patient.
However, Dibari is directed to method and systems for detecting a mental health condition, where structured and unstructured information is analyzed using natural language processing to extract information including clinical data values and medical concepts pertaining to a user (Abstract, Claim 1). Dibari teaches the limitations of instant claims 5, 14, and 19, that is wherein (Fig. 1, # 125 – NLP sentiment analyzer engine; Fig. 4, # 425) the patient medical data further comprises sentiment analysis generated using natural language processing (NLP) for one or more medical notes relating to the patient (Par. [0017]; Par. [0029-0031] – For example, a patient may be determined to be at-risk if the patient has specific biomarkers associated with depression and the sentiment analyzer determines sentiment as being negative; Par. [0050]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have implemented patient medical data comprising sentiment analysis generated using NLP, as shown in Dibari, into Williams’ invention, because doing so is an example of using a known technique to improve similar devices/methods in the same way. One of ordinary skill in the art would have desired implementing medical data comprising sentiment analysis to allow words with strong positive sentiment to be distinguished from words with low positive sentiment in order to produce a sentiment score (Par. [0050] of Dibari). One of ordinary skill in the art would recognize that such a technique could be implemented in Williams’ invention to improve the attrition probability values.
Therefore, claims 5, 14, and 19 are unpatentable over Williams, et al. and Dibari, et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gandy, et al. (U.S. PGPub No. 2018/0113985)
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/MICHAEL T. HOLTZCLAW/Primary Examiner, Art Unit 3796