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 .
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.
Step 1
Claims 1, 3-4, 7-12, 14-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 3-4, and 7-8 are directed to a method. Claims 9-12 and 14-18 are directed to a logic engine (defined by the specification as a computer program in the specification at [0020]). Software per se is not a process, machine, manufacture or composition of matter and are therefore ineligible.
Step 2A Prong One
Claim 1, representative of the claimed invention, recites the steps of collecting data from the cancer patient being dosed including electronic medical records, past and current nutritional intake habits, laboratory results, and food and nutrients to be taken by the cancer patient to improve the cancer patient's condition and inputting the data into a database with a central artificial intelligence (AI) stored on computer readable media; the central AI analyzing the cancer patient's collected data from the database in view of dosing criteria established based on outside clinical trial data, wherein the AI extracts from the outside clinical trial data all features relating variables that effect food and nutrient metabolism in a patient and creates a model relating dosing to patient condition and effect of food and nutrients on the condition that effect efficacy of all food and nutrients taken by the patient, wherein the variables include age of patient, weight of patient, disease state, effect of disease state on nutrition, drugs currently being taken along with known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50, efficacy ranges, and chronic treatment effect versus acute treatment, wherein the Al identifies nearest neighbors of patients from the clinical trial data having similar patient data and/or underwent a treatment plan with similar food and nutrient combinations to the individual patient and identifies related study and trial data with a K-Nearest Neighbor algorithm, wherein the AI compares the individual patient data to neighboring clinical trial patient data with weighting schemes; and the central AI determining a dose for each food and nutrient taken by the cancer patient and maximizing therapeutic effect of each food and nutrient for the individual patient while minimizing adverse effects for the combination of food and nutrients taken for the individual patient, and displaying the dose in a readable report for a practitioner.
The limitations above, as drafted, recite a process that, under its broadest reasonable interpretation, encompass mental processes. The claimed steps recite several steps that include observations, evaluations, judgments and opinions, and “can be performed in the human mind, or by a human using a pen and paper” which have been considered by the courts to be a mental process. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Apart from the use of generic technology (discussed further below), each of the limitations recited above describes activities that would encompass actions performed in collecting patient data and determining a recommended nutrient. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claims 1 and 9, recites the additional elements of a central artificial intelligence and a computer readable medium. The medium is recited at a high-level of generality (i.e., as a computer readable media) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Likewise, the machine learning model (central AI) is implemented as a tool to perform an abstract idea. The claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor to perform the steps of “collecting data from the cancer patient being dosed including electronic medical records, past and current nutritional intake habits, laboratory results, and food and nutrients to be taken by the cancer patient to improve the cancer patient's condition and inputting the data into a database with a central artificial intelligence (AI) stored on computer readable media; the central AI analyzing the cancer patient's collected data from the database in view of dosing criteria established based on outside clinical trial data, wherein the AI extracts from the outside clinical trial data all features relating variables that effect food and nutrient metabolism in a patient and creates a model relating dosing to patient condition and effect of food and nutrients on the condition that effect efficacy of all food and nutrients taken by the patient, wherein the variables include age of patient, weight of patient, disease state, effect of disease state on nutrition, drugs currently being taken along with known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50, efficacy ranges, and chronic treatment effect versus acute treatment, wherein the Al identifies nearest neighbors of patients from the clinical trial data having similar patient data and/or underwent a treatment plan with similar food and nutrient combinations to the individual patient and identifies related study and trial data with a K-Nearest Neighbor algorithm, wherein the AI compares the individual patient data to neighboring clinical trial patient data with weighting schemes; and the central AI determining a dose for each food and nutrient taken by the cancer patient and maximizing therapeutic effect of each food and nutrient for the individual patient while minimizing adverse effects for the combination of food and nutrients taken for the individual patient, and displaying the dose in a readable report for a practitioner” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Similarly, use of a computer performing a machine learning model is a tool to perform the abstract idea. See MPEP 2106.05(f): “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” An example where the courts have found the additional elements to be mere instruction to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process includes a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223 (MPEP 2106.05(f)(2)). The use of a machine learning model emulates what the medical or pharmacy practitioner does in reading the clinical trial and patent data and determining a treatment. Thus, even considering the additional elements in combination, the claims do not include elements that are significantly more than the judicial exception.
Step 2B
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
Claims 1 and 9 are not similar to any of these limitations.
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to 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 to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Looking at the limitations of claims 1 and 9 as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, effects a transformation of subject matter to a different state or thing, applies the use of a particular machine, integrate the abstract idea into a practical application or provide any meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, claims 1 and 9 is not patent eligible.
The dependent claims further describe the abstract idea and do not recite a practical application or significantly more than the judicial exception.
Thus, claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 12,205,705. The claims are directed to a broader embodiment of the patented claims. Instant claims 1, 2, 5-6 correspond to patented claim 1. And claims 9 and 13 to patented claim 6. Claims 3, 4, 7-8 correspond to patented claims 2-5 and claims 10-12 and 14-18 correspond to patented claims 7-14.
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.
Claim(s) 1, 3, 7-12, 14-17 is/are rejected under 35 U.S.C. 103 as being obvious over U.S. Patent 7,295,889 to Lahteenmaki in view of U.S. Patent Application 2019/0267128 to DeCombel in further view of U.S. Patent Application 2005/0176057 to Bremer et al. in further view of U.S. Patent Application 2004/0265874 to Binder et al.
As to claim 1 and 9, Lahteenmaki discloses a method and logic engine of dosing food and nutrients for an individual patient, including the steps of:
collecting data from the individual cancer patient being dosed including electronic medical records (“information about the user”), past and current nutritional intake habits (consumed nutrition), laboratory results (genetic properties, blood sugar, etc.) and food and nutrients to be taken by the patient (nutrients and/or medical substances) to improve the patients condition and inputting the data into a database (database arrangement in an information system) with a central artificial intelligence (AI) (learning neurofuzzy systems and methods) stored on a computer readable medium (Lahteenmaki column 7 lines 1-67 and claim 13 see “illnesses caused by alcohol, dementia, and hormone-dependent cancer”);
the central AI analyzing the individual patient’s collected data from the database in view of dosing criteria established based on outside clinical trial data (Lahteenmaki column 7 lines 1-67 see medical and biological researches); and
the central AI determining a dose for each food and nutrient taken by the individual patient and maximizing therapeutic effect of each food and nutrient for the individual patient while minimizing adverse effects for the combination of food and nutrients taken for the individual patient, and displaying the dose in a readable report for a practitioner (Lahteenmaki column 11 lines 33-57 see dose of nutrition).
However, Lahteenmaki does not explicitly teach and DeCombel teaches wherein the AI extracts from the outside clinical trial data all features relating variables that effect food and nutrient metabolism in a patient and creates a model relating dosing to patient condition and effect of food and nutrients on the condition that effect efficacy of all food and nutrients taken by the patient (DeCombel see “Artificial intelligence, such as IBM Watson® may be employed to predict the likely response of the user and/or to predict an optimal wellness regime [0047]), wherein the variables include age of patient (DeCombel [0019]), weight of patient (DeCombel “lean body mass” [0019] or [0131]), disease state (DeCombel [0080] see hypertention or dysipidaemia), effect of disease state on nutrition (DeCombel [0080]), wherein the Al identifies similar patients from the clinical trial data having similar patient data and/or underwent a treatment plan with similar food and nutrient combinations to the individual patient and identifies related study and trial data, wherein the Al compares the individual patient data to neighboring clinical trial patient data with weighting schemes (Decombel [0023]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention by applicant to consider similar patients to determine metabolic responses to medication/nutrients as in Decombel in the system of Lahteenmaki to improve the accuracy of the recommended nutrition
However, Lahteenmaki and Decombel does not consider drugs currently being taken along with known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50, efficacy ranges, and chronic treatment effect versus acute treatment.
Bremer discloses a model including drugs currently being taken along with known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50 (Bremer [0309] and [0319]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention by Applicant to consider therapeutic index of a combination of drugs as in Bremer in the system of dose optimization of Lahteenmaki and Decombel to ensure safe dosages are delivered to the patient.
However, Lahteenmaki, Decombel, and Bremmer do not teach using a KNN algorithm.
Binder discloses wherein said analyzing step further includes identifying nearest neighbor data with a K-nearest neighbor (KNN) algorithm to find neighboring patients most similar to the individual patient (Binder [0032]-[0033]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention by applicant to utilize a KNN algorithm to determine a similar patient as in Binder in the dose optimization system of Lahteenmaki, Decombel, and Bremmer to utilize actual efficacy to better optimize a recommended dose.
As to claim 2, see the discussion of claim 1, additionally, Lahteenmaki discloses the method wherein said collecting step is further defined as collecting electronic medical records, laboratory results, (Lahteenmaki column 7 lines 1-67) and past and current nutritional intake habits, and food and nutrients to be taken (Lahteenmaki column 8 lines 30-62)
As to claim 3, see the discussion of claim 1, additionally, Lahteenmaki discloses the method wherein the individual patient data and outside data is chosen from the group consisting of efficacy determinations (Lahteenmaki column 4 lines 44-67).
As to claim 7, see the discussion of claim 1, additionally, Lahteenmaki discloses the method wherein said analyzing step includes analyzing a dose of a food or nutrient in combination with a drug chosen from a class consisting of classes vitamins (Lahteenmaki column 7 lines 1-67)
As to claim 8, see the discussion of claim 1, additionally, Lahteenmaki discloses the method further including the step of dispensing the food and nutrients to the patient in the determined dose (Lahteenmaki claim 19).
As to claim 10, see the discussion of claim 9, additionally, Lahteenmaki discloses the logic engine wherein said algorithm is defined as data input -> central Al <- > healthcare professional (Lahteenmaki column 20 lines 9-27).
As to claim 11, see the discussion of claim 10, additionally, Lahteenmaki discloses the logic engine wherein said data input is chosen from the group consisting of electronic medical records (EMRs), and wherein said healthcare professional inputs data including data from EMRs, insurance information (Lahteenmaki column 7 lines 52-58 and column 10 lines 20-36)
As to claim 12, see the discussion of claim 9. Examiner notes that the claim recites an optional step where the logic engine “can request supplemental data based on the individual patient data and weight data by importance, invasiveness, cost, and availability”. It is therefore not required for Lahteenmaki to teach this feature to anticipate the claim.
As to claim 14, see the discussion of claim 9, additionally, Lahteenmaki discloses the logic engine wherein said logic engine includes model logic having a series of classifiers and expert rules implemented in series, and said classifiers and a model are run simultaneously across all possible dosage ranges, and outputs are weighted and combined to determine an optimal dose (Lahteenmaki column 7 lines 30-41 and column 8 lines 6-29).
As to claim 15, see the discussion of claim 9, additionally, Lahteenmaki discloses the logic engine wherein said logic engine provides an output of a practitioner readable report and is sent to a place chosen from the group consisting of a self- dispensing machine (Lahteenmaki column 14 lines 33-49).
As to claim 17, see the discussion of claim 15, additionally, Lahteenmaki discloses the logic engine wherein said output is sent to a device that creates a personalized supplement or food item including the necessary nutrition that the patient requires (Lahteenmaki column 13 lines 48-62).
Claim(s) 4 is/are rejected under 35 U.S.C. 103as being obvious over U.S. Patent 7,295,889 to Lahteenmaki in view of U.S. Patent Application 2019/0267128 to DeCombel in further view of U.S. Patent Application 2005/0176057 to Bremer et al. in further view of U.S. Patent Application 2004/0265874 to Binder et al.in view of OFFICIAL NOTICE
As to claim 4, see the discussion of claim 1, additionally, Lahteenmaki discloses the method wherein said collecting step is further defined as collecting fixed demographics, temporal values, genetic components, and unstructured data (Lahteenmaki column 7 lines 1-67). However, Lahteenmaki does not teach imaging data. Examiner takes OFFICIAL NOTICE that imaging data (e.g. bone density measurements from imaging and calcium or medication supplementation and HGH treatment and growth plate measurements) Is exceedingly well known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date to use these known relationships to determine an optimal dose as in Lahteenmaki to ensure that the supplementation or medication is appropriate.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being obvious over U.S. Patent 7,295,889 to Lahteenmaki in view of U.S. Patent Application 2019/0267128 to DeCombel in further view of U.S. Patent Application 2005/0176057 to Bremer et al. in further view of U.S. Patent Application 2004/0265874 to Binder et al. in view of OFFICIAL NOTICE
As to claim 16, see the discussion of claim 15, however, Lahteenmaki does not explicitly teach the logic engine wherein said output includes instructions of how to take each food and nutrient, side effects to watch out for, and contraindications with commonly taken over the counter medications, supplements, and food. Examiner takes official notice that providing instructions including how to take each food and nutrient, side effects to watch out for, and contraindications with commonly taken over the counter medications, supplements, and food are exceedingly well known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide output with instructions of how to take each food and nutrient, side effects to watch out for, and contraindications with commonly taken over the counter medications, supplements, and food in the system of Lahteenmaki to make the patient better apprised of their health.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being obvious over U.S. Patent 7,295,889 to Lahteenmaki in view of U.S. Patent Application 2019/0267128 to DeCombel in further view of U.S. Patent Application 2005/0176057 to Bremer et al. in further view of U.S. Patent Application 2004/0265874 to Binder et al. in view of 2014/0094745 to Bashan et al.
As to claim 18, see the discussion of claim 9, however, Lahteenmaki does not explicitly teach the logic engine wherein said logic engine is in electronic communication with drug administration devices chosen from the group consisting of transdermal patches, intravenous drips, self-injection and auto-injection devices, wearable injection devices, and implantable drug delivery devices. Bashan discloses the logic engine wherein said logic engine is in electronic communication with drug administration devices including an implantable drug delivery device (Bashan [0045]). It would have been a matter of simple substitution to substitute one of transdermal patches, intravenous drips, self-injection and auto-injection devices, wearable injection devices, and implantable drug delivery devices with the dispenser of Lahteenmaki with the results being predictable (able to accommodate different forms of nutrition and medication).
Response to Arguments
Applicant argues that the claims recite using a central AI that gathers and analyzes patient data with a nearest neighbor algorithm and that this is not performed by a human and therefore overcomes the 101 rejection. This amounts to mere instruction to apply machine learning to the abstract idea. Examiner maintains that the claims recite an abstract idea and that the additional elements, including machine learning, do not provide a practical application or significantly more than the abstract idea.
With respect to the prior 102 and 103 rejections, applicant’s arguments are moot in view of new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Eliza Lam whose telephone number is (571)270-7052. The examiner can normally be reached Monday-Friday 8-4:30PST.
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/ELIZA A LAM/Primary Examiner, Art Unit 3686