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 .
Claims 1-15 are pending and under examination.
This application is a National Stage application under 35 USC 371 of PCT/JP2021/016879, filed 4/28/2021. The filing date of the PCT is the effective filing date for the claims under examination. The Examiner has reviewed all PCT related documentation.
This application has published as US PG Pub: US 2024/0371488 A1.
The preliminary amendment filed 10/27/2023 has been entered, including the amendments to the title, the specification and the claims.
The Drawings as filed are suitable for examination.
Two IDS have been entered and considered.
Claim Objections
Claim 11 is objected to because of the following informalities: “futher” should be corrected to “further”. Appropriate correction is required.
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 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
NOTE: with respect to claims 11-15, the computer readable media comprising instructions encompasses transitory computer media including signals and therefore are not directed to a statutory category of invention. For the purposes of compact prosecution, these claims will be analyzed below for patent-eligibility, but the claims must be amended to a statutory category of invention. The examiner suggests: “a non-transitory computer readable storage medium…”
MPEP 2106: “For example, in Mentor Graphics v. EVE-USA, Inc., 851 F.3d 1275, 112 USPQ2d 1120 (Fed. Cir. 2017), claim interpretation was crucial to the court’s determination that claims to a "machine-readable medium" were not to a statutory category. In Mentor Graphics, the court interpreted the claims in light of the specification, which expressly defined the medium as encompassing "any data storage device" including random-access memory and carrier waves. Although random-access memory and magnetic tape are statutory media, carrier waves are not because they are signals similar to the transitory, propagating signals held to be non-statutory in Nuijten. 851 F.3d at 1294, 112 USPQ2d at 1133 (citing In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)). Accordingly, because the BRI of the claims covered both subject matter that falls within a statutory category (the random-access memory), as well as subject matter that does not (the carrier waves), the claims as a whole were not to a statutory category and thus failed the first criterion for eligibility.”
With respect to step (1): YES, the claims are drawn to statutory categories: Claims 1-5 are drawn to a computer system, claims 6-10 are directed to “control methods” and claims 11-15 are being examined as if they recited a non-transitory computer readable storage media comprising instructions to carry out the method.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE). (MPEP 2106.04(a)(2); (b))
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
[Claim 1] (currently amended) A medicine recommendation apparatus comprising:
at least one memory that is configured to store instructions; and
at least one processor that is configured to execute the instructions to:
(Preamble and EIA of a general purpose computer. MPEP 2106.05(h).)
acquire disease information that is regarding a disease that a subject suffers from,
gene mutation information that is regarding a gene mutation of the subject, and
current medication information that is regarding a medicine that the subject is using; and
(EIA- steps of data gathering, and a description of the data gathered. MPEP 2106.05(g))
generate candidate medicine information by using the disease information, the gene mutation information, and the current medication information, the candidate medicine information being information regarding a candidate for a medicine to be recommended to the subject to use or to be prescribed to the subject.
(Mental Process of observing the gathered information, analyzing the information and making a judgement as to what drug to recommend. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 2] (currently amended) The medicine recommendation apparatus according to claim 1,
wherein the generation of the candidate medicine information further includes:
determining one or more candidate medicines use of which can be recommended to the subject or which can be prescribed to the subject by using first knowledge information and the disease information,
the first knowledge information indicating a disease and a medicine that can be used for the disease in association with each other;
determining resistance or susceptibility of the subject to each of the candidate medicines by using second knowledge information and the gene mutation information,
the second knowledge information indicating information regarding, for each combination of a gene mutation and a medicine, resistance or susceptibility of a person having the gene mutation to the medicine in association with each other;
determining, for each of the candidate medicines, a side effect that can occur in the subject when the medicine is used by using third knowledge information and the current medication information,
the third knowledge information being information regarding a side effect that can occur when a plurality of medicines are used together; and
generating the candidate medicine information indicating the one or more candidate medicines based on resistance or susceptibility of the subject to each of the candidate medicines and side effects each of which can occur in the subject when a respective one of the candidate medicines is used.
(Mental steps of observing first/ second/ third knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 3] (currently amended) The medicine recommendation apparatus according to claim 1, wherein the generation of the candidate information further includes:
computing, for each of the candidate medicines, a recommendation score based on resistance or susceptibility of the subject to the candidate medicine and a side effect that can occur in the subject when the candidate medicine is used,
the recommendation score representing a degree of recommendation of the candidate medicine; and
generating the candidate medicine information indicating the candidate medicine for which the recommendation score is equal to or greater than a threshold or the candidate medicine information indicating each of the candidate medicines together with the recommendation score thereof.
(Mathematic concept of calculating a score, and a description of the score, and a mathematic concept of comparing the score to a threshold value. MPEP 2106.04(a)(2))
[Claim 4] (currently amended) The medicine recommendation apparatus according to claim 2, wherein the at least one processor is configured to execute the instructions further to acquire[[s]] attribute information indicating an attribute of the subject,
wherein the second knowledge information indicates, for each combination of a gene mutation, a medicine, and an attribute, information regarding the gene mutation and resistance or susceptibility of a person having the attribute to the medicine in association with each other, and
wherein the generation of the candidate medicine information further includes determining, for each of the candidate medicines, resistance or susceptibility of the subject to the candidate medicine by using the gene mutation information and the attribute information.
(Mental steps of observing attribute data, and second knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 5] (currently amended) The medicine recommendation apparatus according to claim 2, wherein the at least one processor is configured to execute the instructions further to acquire attribute information indicating an attribute of the subject,
wherein the third knowledge information indicates, for each combination of a plurality of medicines and an attribute, a side effect that can occur when a person having the attribute uses the plurality of medicines together, and
wherein the generation of the candidate medicine information further includes determining, for each of the candidate medicines, a side effect that can occur in the subject when the candidate medicine is used by using the gene mutation information and the attribute information.
(Mental steps of observing attribute information, third knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 6] (currently amended) A control method performed by a computer, comprising:
(Preamble stating a method and EIA of the use of a general purpose computer. MPEP 2106.05(h).)
acquiring disease information that is regarding a disease that a subject suffers from,
gene mutation information that is regarding a gene mutation of the subject, and
current medication information that is regarding a medicine that the subject is using; and
(EIA- steps of data gathering, and a description of the data gathered. MPEP 2106.05(g))
generating candidate medicine information by using the disease information, the gene mutation information, and the current medication information, the candidate medicine information being information regarding a candidate for a medicine to be recommended to the subject to use or to be prescribed to the subject.
(Mental Process of observing the gathered information, analyzing the information and making a judgement as to what drug to recommend. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 7] (currently amended) The control method according to claim 6 wherein the generation of the candidate information further includes:
determining one or more candidate medicines use of which can be recommended to the subject or which can be prescribed to the subject by using first knowledge information and the disease information,
the first knowledge information indicating a disease and a medicine that can be used for the disease in association with each other;
determining resistance or susceptibility of the subject to each of the candidate medicines by using second knowledge information and the gene mutation information,
the second knowledge information indicating information regarding, for each combination of a gene mutation and a medicine, resistance or susceptibility of a person having the gene mutation to the medicine in association with each other;
determining, for each of the candidate medicines, a side effect that can occur in the subject when the medicine is used by using third knowledge information and the current medication information,
the third knowledge information being information regarding a side effect that can occur when a plurality of medicines are used together; and
generating the candidate medicine information indicating the one or more candidate medicines based on resistance or susceptibility of the subject to each of the candidate medicines and side effects each of which can occur in the subject when a respective one of the candidate medicines is used.
(Mental steps of observing first/ second/ third knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 8] (currently amended) The control method according to claim 7 wherein the generation of the candidate information further includes:
computing, for each of the candidate medicines, a recommendation score based on resistance or susceptibility of the subject to the candidate medicine and a side effect that can occur in the subject when the candidate medicine is used,
the recommendation score representing a degree of recommendation of the candidate medicine; and
generating the candidate medicine information indicating the candidate medicine for which the recommendation score is equal to or greater than a threshold or the candidate medicine information indicating each of the candidate medicines together with the recommendation score thereof.
(Mathematic concept of calculating a score, and a description of the score, and a mathematic concept of comparing the score to a threshold value. MPEP 2106.04(a)(2))
[Claim 9] (currently amended) The control method according to claim 7, further comprising:
acquiring attribute information indicating an attribute of the subject,
wherein the second knowledge information indicates, for each combination of a gene mutation, a medicine, and an attribute, information regarding the gene mutation and resistance or susceptibility of a person having the attribute to the medicine in association with each other, and
wherein the generation of the candidate medicine information further includes determining, for each of the candidate medicines, resistance or susceptibility of the subject to the candidate medicine by using the gene mutation information and the attribute information.
(Mental steps of observing attribute data, and second knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 10] (currently amended) The control method according to claim 7, comprising:
acquiring attribute information indicating an attribute of the subject,
wherein the third knowledge information indicates, for each combination of a plurality of medicines and an attribute, a side effect that can occur when a person having the attribute uses the plurality of medicines together, and
wherein the generation of the candidate medicine information includes determining, for each of the candidate medicines, a side effect that can occur in the subject when the candidate medicine is used by using the gene mutation information and the attribute information.
(Mental steps of observing attribute information, third knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 11] (currently amended) A computer readable medium storing a program that causes a computer to execute:
(Preamble and EIA of general purpose computer readable media. MPEP 2106.05(h).)
acquiring disease information that is regarding a disease that a subject suffers from,
gene mutation information that is regarding a gene mutation of the subject, and
current medication information that is regarding a medicine that the subject is using; and
(EIA- steps of data gathering, and a description of the data gathered. MPEP 2106.05(g))
generating candidate medicine information by using the disease information, the gene mutation information, and the current medication information, the candidate medicine information being information regarding a candidate for a medicine to be recommended to the subject to use or to be prescribed to the subject.
(Mental Process of observing the gathered information, analyzing the information and making a judgement as to what drug to recommend. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 12] (currently amended) The computer readable medium according to claim 11 wherein the generation of the candidate medicine information futher includes:
determining one or more candidate medicines use of which can be recommended to the subject or which can be prescribed to the subject by using first knowledge information and the disease information,
the first knowledge information indicating a disease and a medicine that can be used for the disease in association with each other;
determining resistance or susceptibility of the subject to each of the candidate medicines by using second knowledge information and the gene mutation information,
the second knowledge information indicating information regarding, for each combination of a gene mutation and a medicine, resistance or susceptibility of a person having the gene mutation to the medicine in association with each other;
determining, for each of the candidate medicines, a side effect that can occur in the subject when the medicine is used by using third knowledge information and the current medication information,
the third knowledge information being information regarding a side effect that can occur when a plurality of medicines are used together; and
generating the candidate medicine information indicating the one or more candidate medicines based on resistance or susceptibility of the subject to each of the candidate medicines and side effects each of which can occur in the subject when a respective one of the candidate medicines is used.
(Mental steps of observing first/ second/ third knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 13] (currently amended) The computer readable medium according to claim 12 wherein the generation of the candidate medicine information includes:
computing, for each of the candidate medicines, a recommendation score based on resistance or susceptibility of the subject to the candidate medicine and a side effect that can occur in the subject when the candidate medicine is used,
the recommendation score representing a degree of recommendation of the candidate medicine; and
generating the candidate medicine information indicating the candidate medicine for which the recommendation score is equal to or greater than a threshold or the candidate medicine information indicating each of the candidate medicines together with the recommendation score thereof.
(Mathematic concept of calculating a score, and a description of the score, and a mathematic concept of comparing the score to a threshold value. MPEP 2106.04(a)(2))
[Claim 14] (currently amended) The computer readable medium according to claim 12, further comprising:
acquiring attribute information indicating an attribute of the subject,
wherein the second knowledge information indicates, for each combination of a gene mutation, a medicine, and an attribute, information regarding the gene mutation and resistance or susceptibility of a person having the attribute to the medicine in association with each other, and
wherein the generation of the candidate medicine information includes determining, for each of the candidate medicines, resistance or susceptibility of the subject to the candidate medicine by using the gene mutation information and the attribute information.
(Mental steps of observing attribute data, and second knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
[Claim 15] (currently amended) The computer readable medium according to claim 12, further comprising:
acquiring attribute information indicating an attribute of the subject,
wherein the third knowledge information indicates, for each combination of a plurality of medicines and an attribute, a side effect that can occur when a person having the attribute uses the plurality of medicines together, and
wherein the generation of the candidate medicine information includes determining, for each of the candidate medicines, a side effect that can occur in the subject when the candidate medicine is used by using the gene mutation information and the attribute information.
(Mental steps of observing attribute information, third knowledge, analyzing that knowledge, making a judgement as to candidate medicines, side effects, and resistance/ susceptibility, to make a judgment as to what should be recommended. This also represents methods of organizing human activity, as these steps are routinely carried out by medical personnel. MPEP 2106.04(a)(2))
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)):
“Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).”
Claim(s) 1, 6, 11 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim(s) 1, 6, 11 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 2-5, 7-10, 12-15 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 1, 6, 11: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
The data gathering step requires 1) disease information, 2) gene mutation information and 3) current medication information.
MATSUGUCHI (US 2018/0119137 A1; PTO-1449) provides 1) disease information, 2) gene mutation information and 3) current medication information.
Barry (US 6,081,786 B1) 1) obtains disease information, 2) gene mutation information and 3) current medication information.
Zakim (US 2008/0177578 A1) acquires 1) disease information, 2) gene mutation information and 3) current medication information.
Boucher (US 2002/0107705 A1) obtains 1) disease information, 2) gene mutation information and 3) current medication information.
Yao (US 2011/0313790 A1) acquires 1) disease information, 2) gene mutation information and 3) current medication information.
Sheffield (US 2014/0316821 A1) obtains 1) disease information, 2) gene mutation information and 3) current medication information.
Katwala (US 2018/0046764 A1) acquires 1) disease information, 2) gene mutation information and 3) current medication information.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claim(s) 1, 6, 11: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Each of Matsuguchi, Barry, Zakim, Boucher, Yao, Sheffield and Katwala disclose computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 2-5, 7-10, 12-15 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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 limitation(s) is/are:
In claims 1-5 the generic placeholder is “instructions to:”, and the specialized functions are: “generate/ generating candidate medicine information…” (at each recitation)
“determining one or more candidate medicines…” (at each recitation)
“determining resistance or susceptibility…” (at each recitation)
“determining … a side effect…” (at each recitation)
In claims 11-15 the generic placeholder is “program that causes a computer to execute”, and the specialized functions are: “generate/ generating candidate medicine information…” (at each recitation)
“determining one or more candidate medicines…” (at each recitation)
“determining resistance or susceptibility…” (at each recitation)
“determining … a side effect…” (at each recitation)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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 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.
Claims 1-5, 11-15 are rejected under 35 USC 112(b) or 112 (second paragraph) as they fail to particularly point out and distinctly claim the subject matter which applicant regards as his invention.
As set forth above, claim limitations identified above invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Claim 1 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing the specialized function of “generate” candidate medicine information “using” the collected data. Claim 1 does not set forth how the data is to be “used” to achieve the desired results of the recommendation of a specific medicine. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 2 the generic placeholder is “instructions to:” from claim 1, and the first specialized function is “determining one or more candidate medicine” using first knowledge and disease information. The second specialized function is “determining resistance or susceptibility” using second knowledge and gene mutation information. The third specialized function is “determining… a side effect…” using third information and current medication information. The fourth specialized function is “generating the candidate medicine information indicating one or more candidate medicines…” Claim 2 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 3, the generic placeholder is “instructions to:” from claim 1, and the specialized functions are: “computing… a recommendation score…” and “generating the candidate medicine information…” Claim 3 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 4, the generic placeholder is “instructions to” and the specialized function is “generation of the candidate medicine information… determining resistance or susceptibility” using second knowledge and an attribute. Claim 4 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 5, the generic placeholder is “instructions to” and the specialized function is “generation of the candidate medicine information… determining a side effect” using third knowledge and an attribute. Claim 5 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Claim 11 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing the specialized function of “generating” candidate medicine information “using” the collected data. Claim 1 does not set forth how the data is to be “used” to achieve the desired results of the recommendation of a specific medicine. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 12 the generic placeholder is “program” from claim 11, and the first specialized function is “determining one or more candidate medicine” using first knowledge and disease information. The second specialized function is “determining resistance or susceptibility” using second knowledge and gene mutation information. The third specialized function is “determining… a side effect…” using third information and current medication information. The fourth specialized function is “generating the candidate medicine information indicating one or more candidate medicines…” Claim 12 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 13, the generic placeholder is “program” from claim 11, and the specialized functions are: “computing… a recommendation score…” and “generating the candidate medicine information…” Claim 13 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 14, the generic placeholder is “program” from claim 11, and the specialized function is “generation of the candidate medicine information… determining resistance or susceptibility” using second knowledge and an attribute. Claim 14 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 15, the generic placeholder is “program” from claim 11, and the specialized function is “generation of the candidate medicine information… determining a side effect” using third knowledge and an attribute. Claim 15 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing these specialized functions. Each of the determining steps allegedly use differing information, but the claim fails to set forth how the data at hand is specifically to be analyzed to achieve the desired results. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
MPEP 2181.II.B: “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, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008).” Additionally, “To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239.” Finally, “Mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239...”
Therefore, claims 1-5 and 11-15 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 1-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a written description rejection.
The function of the written description requirement is to ensure that the inventor had possession of the specific subject matter later claimed as of the filing date of the application relied on... In re Herschler, 591 F.2d 693, 700-01, 200 USPQ 711, 717 (CCPA 1979), further reiterated in In re Kaslow, 707 F.2d 1366, 217 USPQ 1089 (Fed. Cir. 1983); see also MPEP §§ 2163 - 2163.04.
Original, amended, or new claims are each given their broadest reasonable interpretation in light of, and consistent with the written description of the invention. 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 how the result is to be achieved. For computer-implemented inventions, 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 or procedures 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 to achieve the desired results. See MPEP §§ 2163.02 and 2181, subsection IV. As set forth in MPEP 2161, 2181 and 2185, “the claims must be supported by adequate written description of the step-by-step directions, algorithms, or structures to carry out the claimed steps.”
The court in Biomedino, LLC v. Waters Technologies Corp., 490 F.3d 946, 953[, 83 USPQ2d 1118, 1123] (Fed. Cir. 2007), put the point this way: "The inquiry is whether one of skill in the art would understand the specification itself to disclose a structure, not simply whether that person would be capable of implementing that structure."
"Thus, the written description requirement may be satisfied through disclosure of function and minimal structure when there is a well-established correlation between structure and function." An invention described solely in terms of a method of its desired function lack written descriptive support where there is no described or art-recognized correlation between the disclosed function and the structure(s) responsible for the function. The description needed to satisfy the requirements of 35 U.S.C. 112 “varies with the nature and scope of the invention at issue, and with the scientific and technologic knowledge already in existence." Capon v. Eshhar, 418 F.3d at 1357, 76 USPQ2d at 1084.
Considering claims 1-15, the claimed technology of recommending a medication to a subject based on the analysis of disease, gene and medication information is considered in the art to be unpredictable.
The level of the skill and knowledge of one skilled in the art of bioinformatics is high. Bioinformatics combines biological and technical knowledge with skills related to computers and sophisticated data analysis. In particular, the prior art of record relating to pharmacogenomics illustrates the unpredictable nature of the technology, and underscores the requirement for a higher level of disclosure required.
In order to practice the claimed invention one of skill in the art must obtain undefined disease information, undefined gene mutation information, and undefined current medication information, then “use” that information to generate a specific medicine recommendation for the subject. One must be able to recommend a medication for any possible disease. Dependent claims add further “knowledge” categories, prior to generating the medicine recommendation. The actual calculations, algorithms, or steps for specifically performing the steps and specialized functions of the claims are not clearly provided by the disclosure. While the specification provides some algorithms or flowcharts related to one or more processes or calculations, there is not a clear basis linking specific algorithmic processes to specific steps within the entire scope of the claim, and an issue exists as to whether the disclosure is adequate to perform the entire claimed function(s).
The specification provides a high level, generic description of the invention in the summary pages 1-2.) Generic description of possible data, possible interpretations, and possible outcomes are set forth in [004-007]. Figures 1-14 provide flow charts and other black box depictions which provide only the same results based language of the claims, and do not link specific algorithms or step by step directions to any particular limitation of the claims. The examples of the disclosure, beginning at page 3, comprise a variety of steps and algorithms not clearly required in the independent claims to achieve the desired goals. Therefore, there is no clear linkage between this disclosure and each required specialized function. The claims fail to set forth the minimally sufficient set of steps required for achieving the specialized function, to meet the ultimate goal of the invention. The disclosure fails to link specific algorithms, structures or step-by-step instructions to the specialized function(s) and therefore the claims lack sufficient written description for the specialized functions.
The specification does not identify the diseases for which a specific genetic mutation is known to be associated with possible ADR for specific treatments. The subject to be treated is not human, thus the spectrum of possible diseases for any possible subject is enormous. Not all known diseases have an associated known drug treatment. Not all drugs have known efficacy, resistance or susceptibility information with respect to all diseases, or all genetic mutations.
The specification seeks to combine vast knowledge stores to make the drug recommendation without spelling out how each knowledge store, medical record, and test results are connected and acted upon such that the desired goals can be achieved. No specific algorithmic process, machine learning algorithms, training, or calculations are linked to the limitations of the claims. No strategy for handling conflicting information, drug-drug interactions, or other issues are provided. No particular ontology, rules, or information structures are provided. The specification fails to provide the information required, and the programming required, to make the recommendations commensurate with the scope of the claims.
The state of the art of pharmacogenomics and decision support algorithms can be represented by Xu (2020), O’Donnell (2017) and/or Roncato (2019).
O’Donnell (O’Donnell, PH et al. (2017) Pharmacogenomics-Based Point-of-Care Clinical Decision Support Significantly Alters Drug Prescribing. Clinical Pharmacology & Therapeutics vol 102, number 5, p859-871) is concerned with the reduction of adverse drug reactions by incorporated specifically identified genetic mutations, and knowledge regarding possible adverse side effects. O’Donnell notes: “Obstacles include lack of routine availability of genetic tests, limited provider education about genomics, and the need for facile clinical decision support (CDS) tools that integrate into workflows... Underlying causes of physician reluctance to follow clinical guidance include limitations in awareness, lack of agreement, and clinical inertia.” P860
O’Donnell points out that not all medications being taken have associated knowledge with respect to efficacy, or possible side effects, at p 860:
“Table 3 summarizes the pharmacogenomic information viewed by providers during the study. In total, 34.2% of all medications on patients’ active drug lists had associated alerts (i.e., either a favorable green light (20.9%), cautionary yellow light (12.8%), or high-risk red light (0.5%)). These alerts reflect the prevalence of clinical pharmacogenomic information that was available about the medications being taken by patients throughout the study. The remaining medications (65.8% of drugs on patients’ active drug lists) had no known actionable pharmacogenomic information.”
Roncato (Roncato et al. (2019) FARMAPRICE: A Pharmacogenetic Clinical Decision Support System for Precise and Cost-Effective Therapy. Genes, vol 10 no 276, 13 pages) discloses the incorporation of pharmacogenomic information into a decision support algorithm. Roncato notes:
“Pharmacogenetic (PGx) guidelines for the precise dosing and selection of drugs remain poorly implemented in current clinical practice. Among the barriers to the implementation process is the lack of clinical decision support system (CDSS) tools to aid health providers in managing PGx information in the clinical context.” (abstract)
Roncato reviews the individual variability in responding to a given drug or treatment.
“The response to drugs is highly variable among individuals. Indeed, genetic variants are estimated to affect between 20–95% of the response variability, depending on the drug [1]. Germline genetic variants can influence drug Adsorption, Distribution, Metabolism, and Elimination (ADME) and they can be responsible for reduced drug efficacy or increased toxicity.” P1
“To date, over 160 medications, ranging from heart disease medications to psychiatric drugs, currently have PGx labeling registered with the US Food and Drug Administration (FDA) [5]. The publicly available online knowledge base, PharmGKB [6], is an interactive tool that collects PGx recommendations.” P1-2.
“Despite a recent survey, which showed that 97.6% of clinicians agreed that genetic variations might influence drug response, only 12.9% of clinicians had ordered a PGx test during the prior six months. In fact, translating PGx knowledge into clinical practice has been slow and hindered by many barriers that have prevented its large-scale implementation. Apart from the established statistical associations between PGxs and drug therapy outcomes (clinical validity), large scale implementation of PGx translation requires evidence of clinical-utility and cost-effectiveness. Moreover, that evidence will likely result in favorable reimbursement decisions from payers [7]. Additionally, to aid the implementation of PGx in clinical practice, we need to set up a straightforward workflow from the test prescription to the application of the guidelines, combined with appropriate training and education programs about the clinical use of PGx for healthcare practitioners [8].
The poor application of PGx in the clinical routine is related to the need for a “physician- friendly” electronic “educational resource” that aids clinicians in managing PGx results during routine clinical practice [9]. The implementation of a point-of-care electronic clinical decision support system (CDSS) is urgently needed to guide drug prescriptions in a community-based practice setting [10].” P2
Not all diseases have a link to genetic mutations, nor do all medications have PGx associated information. Not all identified genetic mutations have a role in drug resistance or susceptibility.
“Gene-drug pairs to be integrated were selected based on their actionability and on the availability of the drug in Italy. The genetic variants of these pharmacogenes were selected based on the most recent scientific publications and the level of evidence on the functional effect of the genetic variant on the encoded protein, according to the most updated CPIC guidelines [14]. These genetic variants were classified according to their functional impact. Then, they were combined to obtain all possible genotypes and diplotypes that could be linked to a specific therapeutic recommendation, consistent with published guidelines.”
Xu (Xu et al. (2020) OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level. BMC Cancer, vol 20:740, 10 pages) recognized difficulties in precision oncology pharmacotherapy for individual patients and created a “therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one.”
Xu notes that very few genomic mutations have been identified as being “clinically actionable” as opposed to the multitudes of mutations that have been identified in the human genome.
“As only a small subset of alterations identified from the whole genome, whole exome or DNA methylation sequencing are driver mutations that are clinically actionable [1], it is necessary to ensure that these actionable alterations are recorded in the knowledge base; otherwise patients may receive genetic tests but receive little or no pharmacotherapy suggestions. An increasing number of studies have revealed the importance of epigenome as well as other omics factors in explaining the influence on drug responses, especially in anti-cancer drug responses [2–5], thereby incorporating genomic, epigenomic, and other omics technologies that may improve prediction accuracy of drug treatment responses. In addition, some pharmacotherapy drugs are still in the clinical trial stage and thus are not available to the public or have been approved by administration agencies but the prices are unaffordable for some cancer patients. In these situations, providing the most suitable clinical trials to those patients may be a better choice.” P2
Xu reviews deficiencies in prior art clinical decision support programs at page 2. For example:
“All of these resources collect knowledge from various silos while only providing a single term query via web interface or application programming interface and cannot interpret the bulk of alterations in a single query. The Cancer Genome Interpreter (CGI) is a platform that automates the interpretation of the newly sequenced cancer genome to identify driver alterations and their possible effects on treatment response [8]. It also organizes evidence at different levels but does not specify which treatment may be effective based on the evidence…” p2
The skilled practitioner would first turn to the instant specification to identify the specific algorithms or steps required by the specialized functions recited by the claims. However, the instant disclosure does not provide a written description of those specialized functions and fails to link the particular algorithms or processes disclosed by the specification to specific functional limitations of the claims. As such, the skilled practitioner would turn to the prior art for guidance as to any known correlations between the disclosed structures or algorithms, and the specialized functions of the claims, however, the prior art shows that the knowledge required to recommend a drug for any disease, using gene mutation information, disease information and current medication information is unpredictable. As such, the claims lack adequate written description.
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Xu et al (2020).
Xu, Q. et al (2020) OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level. BMC Cancer, vol 20:740, 10 pages.
Xu discloses the creation of a clinical decision support system that integrates pharmacogenomic information.
“To fulfill the urgent need to interpret genomic alterations in patients’ tumors, the Oncology Pharmacotherapy Decision Support System (OncoPDSS) web-based tool was developed to aid clinicians and oncologists with clinical pharmacotherapy decision making. This knowledge base consists of multi-omics alterations’ actionable evidence and drug indications as well as information on cancer clinical trials. A pharmacotherapy centric classification system was also developed to decipher the effectiveness and safety of each therapy based on the constructed knowledge base. OncoPDSS accepts individual-level genetic test results as input, followed by matching to alterations in the knowledgebase to retrieve all related evidence, and are then used to classify the pharmacotherapies. OncoPDSS bridges the communication of laboratory tests and clinical decision making, helping researchers and clinicians interpret the actionable meaning of each detected alteration at the individual level.” P2.
All of Xu’s methodologies are carried out using programmed general-purpose computers.
With respect to claims 1, 6, 11 and: “acquire disease information that is regarding a disease that a subject suffers from, gene mutation information that is regarding a gene mutation of the subject, and current medication information that is regarding a medicine that the subject is using; and”
Xu is largely concerned with oncology, therefore all subjects had a disease which was cancer. Xu obtains the ontologies required to identify cancer types from an electronic health record, P5. Xu obtains the genomic data for the patient, such as in VCF (variant call format) form p4. Other ‘omics data from the patient can be obtained, such as transcriptome information. P5. Current medications, and drug associated information are obtained. P5. See also p6-7 “usage and interface.”
With respect to claim 1, 6, 11 and: “generate candidate medicine information by using the disease information, the gene mutation information, and the current medication information, the candidate medicine information being information regarding a candidate for a medicine to be recommended to the subject to use or to be prescribed to the subject.”
Xu generates specific candidate medicine information, based on the acquired information and other assorted knowledge. See Fig 2 for the overall generator, and p7 for a specific example.
“When squamous cell carcinoma, large cell carcinoma, adenocarcinoma as well as non-small cell lung carcinoma were selected as cancer types, and EGFR L858R as input alteration, records including EGFR:L858R, EGFR: Activating Mutations, EGFR:Exon21mut, and EGFR: Mutations were matched, and 104 alteration-drug associations were retrieved. EGFR tyrosine kinase inhibitors (EGFR-TKIs) such as gefitinib, erlotinib, afatinib, osimertinib are listed as positive pharmacotherapies, and due to the abundant evidence of these drugs, their TScores are the highest, which causes these therapies to be sorted on the top of the list. During the treatment of EGFR-TKIs, most NSCLC patients may become drug resistant due to the development of T790M mutation in
EGFR [19–21]. When considering both EGFR L858R and EGFR T790M as input alterations, EGFR:T790M and four aforementioned alteration records were matched, and 137 alteration-drug associations were retrieved. Osimertinib is still listed as positive and sorted in the front of the positive therapies. Afatinib, gefitinib and erlotinib are changed to the “negative” category, as there are guideline-level negative evidence indicating that the existence of EGFR:T790M, or the co-existence of EGFR:L858R and EGFR:T790M mutations, is associated with acquired resistance to these drugs. Furthermore, recent clinical studies revealed that the substitution mutation C797S in EGFR can affect the binding of TKIs to EGFR, which confers resistance to all third-generation EGFR-TKIs, including osimertinib [22]. When EGFR L858R, T790M, and C797S were input together, 139 alteration-drug associations related to EGFR: L858R, EGFR: Activating Mutations, EGFR:Exon21mut, EGFR: Mutations, EGFR:T790M and EGFR:C797S records were retrieved, and none of the above EGFR-TKIs was listed as positive pharmacotherapies. In addition to the aforementioned EGFR-TKIs, there are many other therapies, such as several chemotherapies and immune check point inhibitors, listed in each category, which can greatly broaden the scope of pharmacotherapy consideration.”
As such, claims 1, 6 and 11 are anticipated.
With respect to claims 2, 7 and 12, and using first, second and third knowledge information, Xu provides knowledge bases containing the required information. Xu provides knowledge base information which provide disease-medication information, gene mutation and resistance/ susceptibility information, and side effect information. See Figs 1 and 2, and: P5 “At the time of writing (OncoPDSSkb version 1.0), 7692 actionable evidence, 526 drug indications, and 19,922 cancer related clinical trials are included in OncoPDSSkb. In addition, 2676 anti-cancer agents, 528 cancer or pharmacotherapy related genes, 372 cancer types and 13,889 alterations related to 29 alteration types are also included (see Additional file 1, Table S6).”
With respect to claims 3, 8 and 13 and computing a score representing a degree of recommendation, Xu calculates various scores and the interface provides an indication of the degree of recommendation. See Figs 2, and 3. As can be seen in Fig 3, scores are calculated and the interface provides a positive degree of recommendation to the top ranked score. P5 provides the TScore algorithm.
With respect to claims 4, 9 and 14, and providing a subject attribute in combination with the above information, Xu notes patient information can be added as desired, such as multiple cancer types, various personalized information, clinical trial parameters etc. p6.
With respect to claims 5, 10 and 15, and side effect information, that information is provided by the knowledge bases as identified above, figs 1 and 2.
Claim(s) s 1, 2, 5-7, 10-12, 15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Zarei et al (2020).
Zarei, S. et al. (2020) A Web-Based Pharmacogenomics Search Tool for Precision Medicine in Perioperative Care. Journal of Personalized Medicine, vol 10: 65, 14 pages.
“Our tool provides a comprehensive pharmacogenetic drug table including 258 drugs with a total of 461 drug–gene interactions and their corresponding gene variations that might cause modifications in drug efficacy, pharmacokinetics, pharmacodynamics and adverse reactions. This tool is freely accessible online and can be applied as a web-based search instrument for drug–gene
interactions in different fields of medicine, including perioperative medicine.” Abstract.
All of Zarei’s methodologies are carried out using programmed general-purpose computers. (p4).
With respect to claims 1, 6, 11 and: “acquire disease information that is regarding a disease that a subject suffers from, gene mutation information that is regarding a gene mutation of the subject, and current medication information that is regarding a medicine that the subject is using; and”
Zarei obtains patient information, including disease, genomic information, and current medication information:
“A patient’s response to a treatment depends on a variety of factors, which include the concomitant medications and genetic factors. Therefore, we consider the significance of having the most recent information of the pharmacogenomics of a variety of medications in which a perioperative patient may be on [1].” P2
“… anesthesiologists are usually challenged with managing the multimodal pharmacotherapy of each patient perioperatively. These engagements include, but are not limited to, titration of medications for pain, anesthesia induction and maintenance, muscle relaxation, antiemetic, anticoagulants, and beta-blockers. Different enzymes are involved in the mechanism of actions and metabolism of all these drugs. Genetic variations among individual patients play an important role in drug efficacy, toxicity, and interactions with other medications.” P3
Zarei notes that leveraging the information in a patient’s electronic health record would improve recommendations. (p12).
With respect to claim 1, 6, 11 and: “generate candidate medicine information by using the disease information, the gene mutation information, and the current medication information, the candidate medicine information being information regarding a candidate for a medicine to be recommended to the subject to use or to be prescribed to the subject.”
Zarei provides candidate medicine information using all the gathered information, and various other knowledge bases. See section 3, methods beginning at page 3. Disease-drug information is obtained, gene-disease and gene-drug information is obtained, as well as side effect information, and drug-drug interaction data. See also Fig 1. Table 1 links disease categories, and associated gene mutation information that is related to ADR to drugs that treat those diseases. Figure 2 illustrates specific “pharmacogenes” and their contributions to ADR. One example is set forth at section 5.1, p10.
“Reactions to anesthetic medications have a wide variability of responses owing to the high degree of allelic variants that code for metabolizing enzymes, drug receptors, nuclear receptors, and transport proteins and receptors [12]. Thus, both sub and supratherapeutic drug levels can lead to serious consequences such as those observed in postoperative pain management with the use of opioids such as codeine or morphine [27]. Codeine is converted to morphine, the active metabolite, which induces the necessary anesthesia [28]. Morphine toxicity, however, can result in respiratory depression and may even lead to death, if not managed appropriately. Polymorphisms in CYP2D6 can affect the rate of codeine conversion to morphine, which can lead to either decreased amounts of active metabolite or toxicity at standard dosage [28]. CYP2D6*1*1 × N and CYP2D6*1/*2 × N alleles are denoted to be ultra-rapid metabolizers and might increase the risk of respiratory depression. On the other hand, carrying the poor metabolizer (PM) CYP2D6*4/4, CYP2D6*4/5, CYP2D*5/*5, CYP2D*4/6 could decrease the alleviation of pain due to less active metabolite formation [29].”
“For instance, in patients with CYP2D6 poor metabolizer enzyme, beta-blockers such as metoprolol and propranolol can cause a rash, bradyarrhythmia, shortness of breath, hypotension and heart failure [34]. Therefore, to prevent or lessen these adverse effects, anesthesiologists and other
physicians should consider drugs’ dose adjustments based on patients’ CYP2D6 status especially
during the perioperative period.”
“If a cancer patient requires a surgical procedure, all involving physicians should be aware of the type of chemotherapeutic drugs that the patient is taking before surgery and check for any adverse reaction due to their pharmacogenetics. According to Table 1, the ABCB1 gene was one of the most common genes in oncology that are associated with adverse effect. This gene encodes an important efflux transporter of P-glycoprotein (P-gp), also known as multidrug resistance-associated protein 1 (MDR1), at the blood–brain barrier that controls the central nervous system rate of drug absorption and exposure. Genetic polymorphism in the ABCB1 gene has been associated with significant changes in CNS pharmacokinetics of opioid drugs by either limiting their uptake or increasing their clearance form brain into the blood [36]. Therefore, even though this specific gene is mostly associated with the pharmacokinetics of oncology-related drugs such as anastrozole, doxorubicin, everolimus, and cyclosporine [37], its adverse drug–gene interaction can manifest at any point in the perioperative period.”
As such, claims 1, 6 and 11 are anticipated.
With respect to claims 2, 7 and 12, and using first, second and third knowledge information, Zarei provides the required information from FDA databases and other sources as set forth above. See fig 1 and Methods section.
With respect to claims 5, 10 and 15, and side effect information, that information is provided by the knowledge bases as identified above, figs 1 and Methods section.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102a2 as being anticipated by Wright et al (2022).
Wright, C A et al. systems and methods for detecting drug-disease state interactions in patients. US 2022/0328147 A1, published 10/13/2022, having priority to 4/9/2021.
Wright is directed to:
“A drug-disease interaction detection server determines a presence or absence of contraindications or relevant precautionary conditions for a treatment for a patient by parsing database(s) based on the treatment, determines values of factors corresponding to the patient based on a real-time analysis of data obtained from an EMR system, generates a total score by computing a total sum of at least a subset of the values of factors, determines whether the total score is greater than or less than or equal to a predetermined threshold, generates a recommendation for prescribing the treatment requested for the patient in response to the determination of the total score and an indication being treated in the patient, determines an availability or unavailability of one or more alternative treatments, generates an additional recommendation for the patient, and receives an order for a selected treatment for the patient based on the additional recommendation.”
All of Wright’s methods are carried out on programmed general purpose computers. (Fig 1, [0022-0023]).
With respect to claims 1, 6, 11 and: “acquire disease information that is regarding a disease that a subject suffers from, gene mutation information that is regarding a gene mutation of the subject, and current medication information that is regarding a medicine that the subject is using; and”
Wright obtains subject information, including a disease, gene mutation information, and current medication information from the electronic health record/ electronic medical record of the subject. See at least [0007-0009, 0022, “EMR system” element 110, 0026-0029…].
“[0028] In some embodiments, EMR system 110 may comprise one or more EMR databases (not shown) that store patient data and medical history data regarding the health and treatment of patients. In some embodiments, one or more EMR databases in the EMR system 110 may store records for each patient, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information for each patient. A provider may be able to access the stored data in the EMR system 110 using the provider device 102. Records in the EMR system 110 may include records of observational data, patient encounters, lab results, prescriptions, messages (e.g., messages transmitted to patients from their providers), and also biographical information about a patient, such as name, address, date of birth, and the like.”
Genotype and gene mutation information can be obtained as set forth at least at [0077].
“[0077] … In some embodiments, providers may obtain genotypes of a patient to assess if the patient is more prone to having a drug-disease interaction based on the presence or absence of certain alleles in the patient's cells (e.g., genetic variants). In some embodiments, specific drugs may be used with caution or avoided for patients that have certain genetic variants because patients with the certain genetic variants may be more at risk for an allergic or adverse reaction to the specific drugs. For example, Abacavir (a medication used to treat HIV/AIDS) is contraindicated in patients who are HLA-B*5701positive, so testing/screening for the HLA-B*5701allele is recommended prior to ordering Abacavir for patients.
[0078] Thus, patients may need to be genetically tested for a genotype before being prescribed certain drugs. If a genetic or genomic test is not needed, then the method 400 in this example proceeds to step 436. If a genetic or genomic test is needed, then the method 400 in this example proceeds to step 430. At step 430, the drug-disease interaction detection server 200 may determine if a genetic test result is available. In some embodiments, the drug-disease interaction detection server 200 may access the EMR system 110 to determine if a genetic test result is available in the corresponding electronic medical record of the patient. If a genetic test result is unavailable, then the method 400 in this example proceeds to step 432. If a genetic test result is available, then the method 400 in this example proceeds to step 434.”
With respect to claim 1, 6, 11 and: “generate candidate medicine information by using the disease information, the gene mutation information, and the current medication information, the candidate medicine information being information regarding a candidate for a medicine to be recommended to the subject to use or to be prescribed to the subject.”
Wright uses the collected information, and additional knowledge, to suggest a candidate or alternative medication. Beginning at [0039] Wright’s process takes the collected patient information, information from other sources, et al and carries out pattern recognition, text extraction, identifies contraindications, calculates scores, and provides a list of possible therapies.
“[0052] Recommendation module 216 may generate alerts, notifications, and/or recommendations for healthcare providers based on the drug-disease interaction detection. In some embodiments, recommendation module 216 may generate and transmit one or more recommendations to provider device 102. In some embodiments, recommendation module 216 may receive data regarding a total score for the drug-disease interactions for a requested treatment for a patient from interaction detection module 212 and generate a recommendation based on the total score determined and an indication (e.g., a condition or disease) being treated in the patient. In some embodiments, if the total score is less than or equal to a predetermined threshold, recommendation module 216 generates a recommendation indicating a moderate drug-disease interaction for prescribing the treatment requested for the patient. In some embodiments, if the total score is greater than the predetermined threshold, recommendation module 216 generates a recommendation indicating a major drug-disease interaction for prescribing the treatment requested for the patient. In some embodiments, recommendation module 216 generates recommendations to provider device 102 based on identifying one or more contraindications of a requested treatment for a patient.”
As such, claims 1, 6 and 11 are anticipated by Wright.
With respect to claims 2, 7 and 12, and using first, second and third knowledge information, Wright provides knowledge bases containing the required information. Wright provides knowledge base information which provide disease-medication information, gene mutation and resistance/ susceptibility information, and side effect information. See Figs 1 and 2, and their description in the text. Wright provides a drug-disease interaction database [0030 et al], an EMR database [0029 et al], a drug manufacturer database that includes dosage and side effect information [0031, 0042 et al], a medical literature database [0032], et al. [0040] adds additional knowledge sources such as the FDA and regulatory agency websites.
“[0046] For example, the provider device 102 may transmit a data request for a medication for treating diabetes in a patient. Interaction detection module 212 may query the drug-disease interaction database in the plurality of databases 112 to determine that the medication is contraindicated in patients with kidney disease (e.g., stage 3b) and that patients with kidney disease should not be prescribed the medication. In another example, interaction detection module 212 may query the drug-disease interaction database to identify that a medication requested by the provider device 102 to treat a first disease counteracts and/or alters the disease state of a second disease in the patient. In some embodiments, interaction detection module 212 may communicate with recommendation module 216 to generate one or more recommendations for the provider device 102 regarding the drug-disease interactions for a particular treatment and whether or not the particular treatment should be prescribed to the patient, as will be described in further detail below.”
With respect to claims 3, 8 and 13 and computing a score representing a degree of recommendation, Wright calculates various scores and the interface provides an indication of the degree of recommendation. [0007, 0048, 0052, 0074, 0076, et al.]
“[0007] … The method further includes generating a total score by computing a total sum of at least a subset of the values of the plurality of factors corresponding to the patient, determining whether the total score is less than or greater than or equal to a predetermined threshold, and generating a recommendation for prescribing the treatment requested for the patient in response to the determination of the total score and an indication being treated in the patient. In response to determining that the total score is less than the predetermined threshold, a recommendation indicating a moderate drug-disease interaction for prescribing the treatment requested for the patient is generated. In response to determining that the total score is greater than or equal to the predetermined threshold, a recommendation indicating a major drug-disease interaction for prescribing the treatment requested for the patient is generated.”
With respect to claims 4, 9 and 14, and providing a subject attribute in combination with the above information, Wright notes patient information can be added as desired, such as anything present in the electronic medical record. [0028 et al.]
With respect to claims 5, 10 and 15, and side effect information, that information is provided by the knowledge bases as identified above, figs 1 and 2. [0005, 0042]
Conclusion
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686
