DETAILED ACTION
Claims 1-5, 7, 9-12, 16, 18-20, 23-26, 29, and 31 are hereby under examination.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/17/2023 and 01/08/2026 are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 7 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding claim 7, the claim currently depends from canceled claim 6. For the purposes of examination, claim 7 is being interpreted as depending from claim 5.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-5, 7, 9-12, 16, 18-20, 23-26, 29, and 31 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.
Analysis of independent claims 1, 29, and 31:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to a device, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 29 is directed to a method, which describes one of the four statutory categories of patentable subject matter, i.e., a process. Claim 31 is directed to a method, which describes one of the four statutory categories of patentable subject matter, i.e., a process. Therefore, further consideration is necessary.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1, 29, and 31 recite an abstract idea. In particular, the claims recite the following:
Operate at least one prediction model on the perception data to determine at least one risk variable, which is indicative of a risk that the test subject will develop peripheral neuropathy as a result of chemotherapy; and
Generate prediction data based on the at least one risk variable.
These elements recited in claims 1, 29, and 31 are drawn to an abstract idea since (1) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Operating at least one prediction model to determine a risk variable is drawn to a mental process that can be practically performed in the human mind, with the aid of pen and paper. For example, a person with ordinary skill in the art can reasonably use received perception data to determine a subject has a certain risk of developing peripheral neuropathy as a result of chemotherapy if the perception data is within a certain range. There is nothing to suggest an undue level of complexity in operating at least one prediction model, as currently claimed.
Generate prediction data based on the at least one risk variable is drawn to a mental process that can be practically performed in the human mind, with the aid of pen and paper. For example, a person with ordinary skill in the art can reasonably generate prediction data based on the determined at least one risk variable. There is nothing to suggest an undue level of complexity in generating prediction data, as currently claimed.
Prong Two: Claims 1, 29, and 31 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are “directed to” the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “circuitry configured to predict a risk of chemotherapy …” (claim 1), “computer-implemented …” (claim 29)), and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g. “receive input data comprising perception data …” - with no specific structure recited).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Further, “circuitry” and “computer-implemented” does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 29, and 31 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to pre-solution steps of necessary data gathering, which merely facilitate the abstract idea.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Analysis of the dependent claims:
Claims 2-5, 7, 9-12, 16, 18-20, and 23-26 depend from the independent claim. The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“determine the at least one risk variable by operating the at least one prediction model on the perception data and on the set of parameter values” (claim 10), “generate a plurality of variables based on the input data, wherein the at least one prediction model is configured to determine the at least one risk variable by combining the plurality of variables by use of a plurality of predetermined weight factors” (claim 16), “wherein the at least one risk variable comprises a risk variable which is indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy that persists at least six months after completion of the chemotherapy” (claim 18), “wherein the at least one risk variable comprises a risk variable which is indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy during the chemotherapy” (claim 19), “wherein the prediction data is indicative of one of at least three predefined risk classes comprising: a first risk class associated with a low risk, a second risk class associated with a high risk, and a third risk class intermediate the first and second risk classes” (claim 20), “wherein the at least one risk variable comprises a first risk variable and a second risk variable, and wherein the circuitry is further configured to: determine a first category based on the first risk variable, determine a second category based on the second risk variable, and generate the prediction data as a logic combination of the first category and the second category” (claim 23), “wherein the circuitry is further configured to: operate a first prediction model on the input data to determine the first risk variable, and operate a second prediction model on the input data to determine the second risk variable” (claim 24), “wherein the first risk variable is indicative of a low risk of developing CIPN, and the second risk variable is indicative of a high risk of developing CIPN” (claim 25), “wherein said circuitry is further configured to: evaluate the input data in relation to a set of content requirements to determine an adequacy score, and selectively, based on the adequacy score, output a request for further input data” (claim 26)),
Further describe the pre-solution activity (or the structure used for such activity) (“wherein the one or more predefined frequencies comprises at least two different frequencies below 64 Hz” (claim 2), “wherein said at least one of the one or more predefined frequencies is at or below 60 Hz, 50 Hz, 40 Hz, 35 Hz, 30 Hz, 25 Hz, 20 Hz, 15 Hz, or 10 Hz” (claim 3), “wherein the perception data comprises a plurality of perception values that differ by at least one of: predefined frequency, predetermined location, or limb of the test subject” (claim 4), “wherein the chemotherapy comprises a time sequence of sessions with administration of at least one chemotherapeutic agent, and wherein the perception data represents the measured perception of the vibrations by the test subject prior to at least one session in the time sequence of sessions” (claim 5), “wherein the perception data represents the measured perception of the vibrations by the test subject prior to at least an initial session in the time sequence of sessions” (claim 7), “wherein the chemotherapy comprises administration of at least one chemotherapeutic agent in the group consisting of: platinum-containing chemotherapeutic agents, taxanes, immunomodulatory agents, vinca alkaloids, epothilones, and protease inhibitors” (claim 9), “wherein the input data further comprises a set of parameter values representing the test subject and/or the chemotherapy” (claim 10), “wherein the set of parameter values is indicative of one or more of: an age of the test subject, a gender of the test subject, one or more physical characteristics of the test subject, a current temperature of the test subject, a health status of the test subject, a medication status of the test subject, and a medical history of the test subject” (claim 11), “wherein the set of parameter values is indicative of one or more of: a chemotherapy treatment history of the test subject, a chemotherapeutic agent administered in the chemotherapy, an accumulated dose of the chemotherapeutic agent administered during the chemotherapy, a method of administrating the chemotherapeutic agent, and a schedule of the chemotherapy” (claim 12)),
Further describe the computer implementation (“circuitry” (multiple claims)).
Further, “circuitry” does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subjection matter. Claims 1-5, 7, 9-12, 16, 18-20, 23-26, 29, and 31 are rejected.
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 1-5, 7, 9-12, 16, 18-20, 23-26, 29, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over David Poisner (US 20090082694 A1), hereinafter referred to as Poisner, in view of Rubenstein et al. (US 20190371468 A1), hereinafter referred to as Rubenstein.
The claims are generally directed towards a prediction device, comprising circuitry configured to predict a risk of chemotherapy-induced peripheral neuropathy (CIPN), in a test subject, said circuitry being configured to: receive input data comprising perception data that designates measured perception of vibrations at one or more predetermined locations on one or more limbs of the test subject, wherein the perception data represents, for vibrations at each of one or more predefined frequencies, a vibration energy that causes the test subject to switch between perception and non-perception of the vibrations, wherein at least one of the one or more predefined frequencies is below 64 Hz, operate at least one prediction model on the perception data to determine at least one risk variable, which is indicative of a risk that the test subject will develop peripheral neuropathy as a result of chemotherapy, and generate prediction data based on the at least one risk variable.
Regarding claim 1, Poisner discloses a prediction device, comprising circuitry configured to predict a risk of chemotherapy-induced peripheral neuropathy (CIPN), in a test subject (Abstract, “peripheral neuropathy monitor … monitoring a trend towards peripheral neuropathy …”, Fig. 1, para. [0001]), said circuitry being configured to:
receive input data comprising perception data that designates measured perception of vibrations at one or more predetermined locations on one or more limbs of the test subject, wherein the perception data represents, for vibrations at each of one or more predefined frequencies, a vibration energy that causes the test subject to switch between perception and non-perception of the vibrations, wherein at least one of the one or more predefined frequencies is below 64 Hz (para. [0010], “human extremity of the user …”, para. [0011], “no longer feel one or more generated vibrations …”, para. [0017], “vibration sensitivity tests to measure changes in sensation by the user to vibrations … frequency of vibrations may run from as low as about 60 Hertz … frequency range may range from the tens of Hertz … frequency may be gradually increased …”),
operate at least one prediction model on the perception data to determine at least one risk variable, which is indicative of a risk that the test subject will develop peripheral neuropathy (para. [0014-0015], “running a diagnosis program, for example using artificial intelligence or the like, to infer and/or diagnose a condition of the user based at least in park on one or more test results …”, para. [0017], “detect progression of the user towards peripheral neuropathy … indicated a trends towards peripheral neuropathy …”), and
generate prediction data based on the at least one risk variable (para. [0018], “may be trending towards or suffering from peripheral neuropathy … result may be flagged … display a visual flag …”).
However, Poisner does not explicitly disclose the development of peripheral neuropathy is the result of chemotherapy.
Rubenstein teaches an analogous prediction device (Abstract, Fig. 2, para. [0030], para. [0041]). Rubenstein teaches receiving input data comprising perception data (Fig. 2, element 202, element 218, para. [0041], “neuropathy related data”). Rubenstein further teaches operating a prediction model to determine at least one risk variable, where the risk is based on a treatment-regiment-related outcome as a result of chemotherapy (para. [0041], para. [0055], para. [0072]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction model to additionally include chemotherapy data to determine a risk of developing peripheral neuropathy as a result of chemotherapy, as taught by Rubenstein. This is because Rubenstein teaches chemotherapy can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 2, modified Poisner discloses the prediction device of claim 1, wherein the one or more predefined frequencies comprises at least two different frequencies below 64 Hz (para. [0011], “no longer feel one or more generated vibrations …”, para. [0017], “vibration sensitivity tests to measure changes in sensation by the user to vibrations … frequency of vibrations may run from as low as about 60 Hertz … frequency range may range from the tens of Hertz … frequency may be gradually increased …”).
Regarding claim 3, modified Poisner discloses the prediction device of claim 1, wherein said at least one of the one or more predefined frequencies is at or below 60 Hz, 50 Hz, 40 Hz, 35 Hz, 30 Hz, 25 Hz, 20 Hz, 15 Hz, or 10 Hz (para. [0011], “no longer feel one or more generated vibrations …”, para. [0017], “vibration sensitivity tests to measure changes in sensation by the user to vibrations … frequency of vibrations may run from as low as about 60 Hertz … frequency range may range from the tens of Hertz … frequency may be gradually increased …”).
Regarding claim 4, modified Poisner discloses the prediction device of claim 1, wherein the perception data comprises a plurality of perception values that differ by at least one of: predefined frequency, predetermined location, or limb of the test subject (para. [0011], “no longer feel one or more generated vibrations …”, para. [0017], “vibration sensitivity tests to measure changes in sensation by the user to vibrations … frequency of vibrations may run from as low as about 60 Hertz … frequency range may range from the tens of Hertz … frequency may be gradually increased …”).
Regarding claim 5, modified Poisner discloses the prediction of claim 1, wherein the perception data represents the measured perception of the vibrations by the test subject prior to at least one session in a time sequence of sessions (para. [0015], “previously run tests …”).
However, modified Poisner does not explicitly disclose wherein the chemotherapy comprises a time sequence of sessions administration of at least one chemotherapeutic agent.
Rubenstein further teaches the chemotherapy comprises a time sequence of sessions administration of at least one chemotherapeutic agent (para. [0024], para. [0041]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction device taught by modified Posner to explicitly have the chemotherapy comprises a time sequence of sessions administration of at least one chemotherapeutic agent, as taught by Rubenstein. This is because Ribenstein teaches chemotherapy, including a chemotherapy regimen, can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 7, modified Poisner discloses the prediction device of claim 5, wherein the perception data represents the measured perception of the vibrations by the test subject prior to at least an initial session in the time sequence of sessions (para. [0015], “previously run tests …”).
Regarding claim 9, modified Poisner discloses the prediction device of claim 1.
However, modified Poisner does not explicitly disclose wherein the chemotherapy comprises administration of at least one chemotherapeutic agent in the group consisting of: platinum-containing chemotherapeutic agents, taxanes, immunomodulatory agents, vinca alkaloids, epothilones, and protease inhibitors.
Rubenstein further teaches the chemotherapy comprises administration of at least one chemotherapeutic agent in the group consisting of: platinum-containing chemotherapeutic agents, taxanes, immunomodulatory agents, vinca alkaloids, epothilones, and protease inhibitors (para. [0029], para. [0041]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction device to additionally determine the risk as a result of chemotherapy comprising the administration of at least one chemotherapeutic agent in the group consisting of: platinum-containing chemotherapeutic agents, taxanes, immunomodulatory agents, vinca alkaloids, epothilones, and protease inhibitors, as taught by Rubenstein. This is because Rubenstein teaches chemotherapeutic agents can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 10, modified Poisner discloses the prediction device of claim 1, wherein the input data further comprises a set of parameter values representing the test subject and/or the chemotherapy, and wherein said circuitry is configured to determine the at least one risk variable by operating the at least one prediction model on the perception data and on the set of parameter values (para. [0015], “advance processing and adaptive learning based at least in part on one or more test results from previously run tests …”, para. [0018], “temperature test … one or more vibration tests … tests results may be analyzed …”).
Regarding claim 11, modified Poisner discloses the prediction device of claim 10, wherein the set of parameter values is indicative of one or more of: an age of the test subject, a gender of the test subject, one or more physical characteristics of the test subject, a current temperature of the test subject, a health status of the test subject, a medication status of the test subject, and a medical history of the test subject (para. [0015], “advance processing and adaptive learning based at least in part on one or more test results from previously run tests …”, para. [0018], “temperature test … one or more vibration tests … tests results may be analyzed …”).
Regarding claim 12, modified Poisner discloses the prediction device of claim 11.
However, modified Poisner does not explicitly disclose wherein the set of parameter values is indicative of one or more of: a chemotherapy treatment history of the test subject, a chemotherapeutic agent administered in the chemotherapy, an accumulated dose of the chemotherapeutic agent administered during the chemotherapy, a method of administrating the chemotherapeutic agent, and a schedule of the chemotherapy.
Rubenstein further teaches the set of parameter values is indicative of one or more of: a chemotherapy treatment history of the test subject, a chemotherapeutic agent administered in the chemotherapy, an accumulated dose of the chemotherapeutic agent administered during the chemotherapy, a method of administrating the chemotherapeutic agent, and a schedule of the chemotherapy (para. [0041]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction device taught by modified Poisner to additionally include a set of parameter values is indicative of one or more of: a chemotherapy treatment history of the test subject, a chemotherapeutic agent administered in the chemotherapy, an accumulated dose of the chemotherapeutic agent administered during the chemotherapy, a method of administrating the chemotherapeutic agent, and a schedule of the chemotherapy, as taught by Rubenstein. This is because Rubenstein teaches different cancer regimen data, including chemotherapeutic agents, can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 16, modified Poisner discloses the prediction device of claim 1, wherein the circuitry is further configured to: generate a plurality of variables based on the input data, wherein the at least one prediction model is configured to determine the at least one risk variable by combining the plurality of variables by use of a plurality of predetermined weight factors (para. [0018], “temperature test … one or more vibration tests … test results may be analyzed … determine any trend or pattern …”).
Regarding claim 18, modified Poisner discloses the prediction device of claim 1.
However, modified Poisner does not explicitly disclose wherein the at least one risk variable comprises a risk variable which is indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy that persists at least six months after completion of the chemotherapy.
Rubenstein further teaches the at least one risk variable comprises a risk variable which is indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy that persists at least six months after completion of the chemotherapy (para. [0030-0031]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction device taught by modified Poisner to explicitly have the risk variable be indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy that persists at least six months after completion of the chemotherapy, as taught by Rubenstein. This is because Rubenstein teaches different cancer regimen data, including chemotherapeutic agents, can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 19, modified Poisner discloses the prediction device of claim 1.
However, modified Poisner does not explicitly disclose wherein the at least one risk variable comprises a risk variable which is indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy during the chemotherapy.
Rubenstein further teaches the at least one risk variable comprises a risk variable which is indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy during the chemotherapy (para. [0030-0031]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction device taught by modified Poisner to explicitly have the risk variable be indicative of the risk that the test subject will develop chemotherapy-induced peripheral neuropathy during the chemotherapy, as taught by Rubenstein. This is because Rubenstein teaches different cancer regimen data, including chemotherapeutic agents, can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 20, modified Poisner discloses the prediction device of claim 1.
However, modified Poisner does not explicitly disclose wherein the prediction data is indicative of one of at least three predefined risk classes comprising: a first risk class associated with a low risk, a second risk class associated with a high risk, and a third risk class intermediate the first and second risk classes.
Rubenstein further teaches the prediction data is indicative of one of at least three predefined risk classes comprising: a first risk class associated with a low risk, a second risk class associated with a high risk, and a third risk class intermediate the first and second risk classes (para. [0054-0055]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify prediction data disclosed by modified Poisner to additionally include at least three predefined risk classes comprising: a first risk class associated with a low risk, a second risk class associated with a high risk, and a third risk class intermediate the first and second risk classes, as taught by Rubenstein. This is because Rubenstein teaches providing multiple levels of risk levels allows for the patient to be better informed about the risks of the treatment regimens and the risk of developing peripheral neuropathy (para. [0055-0056]).
Regarding claim 23, modified Poisner discloses the prediction device of claim 1.
However, modified Poisner does not explicitly disclose wherein the at least one risk variable comprises a first risk variable and a second risk variable, and wherein the circuitry is further configured to: determine a first category based on the first risk variable, determine a second category based on the second risk variable, and generate the prediction data as a logic combination of the first category and the second category.
Rubenstein further teaches the at least one risk variable comprises a first risk variable and a second risk variable, and wherein the circuitry is further configured to: determine a first category based on the first risk variable, determine a second category based on the second risk variable, and generate the prediction data as a logic combination of the first category and the second category (para. [0071-0086]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the at least one risk variables taught by modified Poisner to additionally include a first risk variable and a second risk variable, and wherein the circuitry is further configured to: determine a first category based on the first risk variable, determine a second category based on the second risk variable, and generate the prediction data as a logic combination of the first category and the second category, as taught by Rubenstein. This is because Rubenstein teaches combining a plurality of risk variables to determine prediction data allows for the model to use multiple data variables to determine the most favorable treatment result (para. [0070-0071]).
Regarding claim 24, modified Poisner discloses the prediction device of claim 23.
However, modified Poisner does not explicitly disclose wherein the circuitry is further configured to: operate a first prediction model on the input data to determine the first risk variable, and operate a second prediction model on the input data to determine the second risk variable.
Rubenstein further teaches the circuitry is further configured to: operate a first prediction model on the input data to determine the first risk variable, and operate a second prediction model on the input data to determine the second risk variable (para. [0071-0086]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the circuitry taught by modified Poisner to additionally be configured to operate a first prediction model on the input data to determine the first risk variable, and operate a second prediction model on the input data to determine the second risk variable, as taught by Rubenstein. This is because Rubenstein teaches operating multiple prediction models allows for the models to use multiple data variables to determine the most favorable treatment result (para. [0070-0071]).
Regarding claim 25, modified Poisner discloses the prediction device of claim 23.
However, modified Poisner does not explicitly disclose wherein the first risk variable is indicative of a low risk of developing CIPN, and the second risk variable is indicative of a high risk of developing CIPN.
Rubenstein further teaches the first risk variable is indicative of a low risk of developing CIPN, and the second risk variable is indicative of a high risk of developing CIPN (para. [0054-0056], para. [0071-0086]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the risk variables taught by modified Poisner to explicitly have the first risk variable is indicative of a low risk of developing CIPN, and the second risk variable is indicative of a high risk of developing CIPN, as taught by Rubenstein. This is because Rubenstein teaches providing multiple levels of risk levels allows for the patient to be better informed about the risks of the treatment regimens and the risk of developing peripheral neuropathy (para. [0055-0056]).
Regarding claim 26, modified Poisner discloses the prediction device of claim 1, wherein said circuitry is further configured to: evaluate the input data in relation to a set of content requirements to determine an adequacy score, and selectively, based on the adequacy score, output a request for further input data (para. [0014]).
Regarding claim 29, Poisner discloses a computer-implemented prediction method (Abstract, “peripheral neuropathy monitor … monitoring a trend towards peripheral neuropathy …”, Fig. 1, para. [0001], para. [0010]), comprising:
receiving input data comprising perception data that designates measured perception of vibrations at one or more predetermined locations on one or more limbs of a test subject, wherein the perception data represents, for vibrations at each of one or more predefined frequencies, a vibration energy that causes the test subject to switch between perception and non-perception of the vibrations, wherein at least one of the one or more predefined frequencies is below 64 Hz (para. [0010], “human extremity of the user …”, para. [0011], “no longer feel one or more generated vibrations …”, para. [0017], “vibration sensitivity tests to measure changes in sensation by the user to vibrations … frequency of vibrations may run from as low as about 60 Hertz … frequency range may range from the tens of Hertz … frequency may be gradually increased …”);
operating a prediction model on the perception data to determine at least one risk variable, which is indicative of a risk that the test subject will develop peripheral neuropathy (para. [0014-0015], “running a diagnosis program, for example using artificial intelligence or the like, to infer and/or diagnose a condition of the user based at least in park on one or more test results …”, para. [0017], “detect progression of the user towards peripheral neuropathy … indicated a trends towards peripheral neuropathy …”); and
generating prediction data based on the at least one risk variable (para. [0018], “may be trending towards or suffering from peripheral neuropathy … result may be flagged … display a visual flag …”).
However, Poisner does not explicitly disclose the development of peripheral neuropathy is the result of chemotherapy.
Rubenstein teaches an analogous prediction method (Abstract, Fig. 2, para. [0030], para. [0041]). Rubenstein teaches receiving input data comprising perception data (Fig. 2, element 202, element 218, para. [0041], “neuropathy related data”). Rubenstein further teaches operating a prediction model to determine at least one risk variable, where the risk is based on a treatment-regiment-related outcome as a result of chemotherapy (para. [0041], para. [0055], para. [0072]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction model to additionally include chemotherapy data to determine a risk of developing peripheral neuropathy as a result of chemotherapy, as taught by Rubenstein. This is because Rubenstein teaches chemotherapy can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Regarding claim 31, Poisner discloses a prediction method (Abstract, “peripheral neuropathy monitor … monitoring a trend towards peripheral neuropathy …”, Fig. 1, Fig. 3, para. [0001]), comprising:
determining perception data that designates measured perception of vibrations at one or more predetermined locations on one or more limbs of a test subject, wherein the perception data represents, for vibrations at each of one or more predefined frequencies, a vibration energy that causes the test subject to switch between perception and non-perception of the vibrations, wherein at least one of the one or more predefined frequencies is below 64 Hz (para. [0010], “human extremity of the user …”, para. [0011], “no longer feel one or more generated vibrations …”, para. [0017], “vibration sensitivity tests to measure changes in sensation by the user to vibrations … frequency of vibrations may run from as low as about 60 Hertz … frequency range may range from the tens of Hertz … frequency may be gradually increased …”); and
operating the prediction device of claim 1 on the perception data to generate prediction data indicative of the risk that the test subject will develop peripheral neuropathy (para. [0014-0015], “running a diagnosis program, for example using artificial intelligence or the like, to infer and/or diagnose a condition of the user based at least in park on one or more test results …”, para. [0017], “detect progression of the user towards peripheral neuropathy … indicated a trends towards peripheral neuropathy …”; Further, see the rejection of claim 1 above).
However, Poisner does not explicitly disclose the development of peripheral neuropathy is the result of chemotherapy.
Rubenstein teaches an analogous prediction method (Abstract, Fig. 2, para. [0030], para. [0041]). Rubenstein teaches receiving input data comprising perception data (Fig. 2, element 202, element 218, para. [0041], “neuropathy related data”). Rubenstein further teaches operating a prediction model to determine at least one risk variable, where the risk is based on a treatment-regiment-related outcome as a result of chemotherapy (para. [0041], para. [0055], para. [0072]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction model to additionally include chemotherapy data to determine a risk of developing peripheral neuropathy as a result of chemotherapy, as taught by Rubenstein. This is because Rubenstein teaches chemotherapy can cause side effects, such as chemotherapy-induced peripheral neuropathy, and determining the risk allows for the patient to select the best outcome (para. [0030-0031]).
Conclusion
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/K.W.K./Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791