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 .
Status of claims
Claims 1-20 have been examined. Claims 1-2, 4-6, 9-11, 13-14, 17-20 have been amended.
Claim Rejections - 35 USC § 103
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 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 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) 1-13, 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groen et al. (US.20080008991 A1) in view of Hendrickson et al. (US20230325396 hereinafter Hendrickson) and further in view of Parry et al. (US20199922411A1 hereinafter Parry)
With respect to claim 1, Groen teaches a method of dosing cancer therapies, including the steps of:
collecting patient data including cancer therapies and drugs to be taken and storing the patient data in a database (‘991; Para 0100: by disclosure, Groen describes the process starts with the gathering or collection of patient data (step 100). Patient data may be collected by a physician, a doctor or another entity (including clinicians, health care providers, etc.). The patient data may also include the patient's actual drug concentration for one drug, or as many drugs as the patient is taking at that time, and resistance data that is determined from a patient sample taken at, or close to, that time. In one embodiment, all of the gathered patient data may be stored in a database, such as local database 46 of therapy optimization system 40 (see FIG. 3);
Hendrickson teaches
analyzing the data in view of outside data compiled from prior clinical data studies other than the patient data, using a logic engine comprising an algorithm stored on non-transitory computer readable media that applied artificial intelligence techniques selected from the group including classifiers and expert rules; (‘396; Para 0050: By disclosure, Hendrickson describes, as in FIG. 1, providing an overview of the systems and methods of the invention as exemplarily applied to news content. A news-based review and scoring system, here labeled NewsCheck receives content, research, knowledge, processes, and/or practices from outside sources such as individual contributors, consumers or third party organizations providing and/or seeking validation, scoring and control for their content and communications either by automated or manual queueing …..content may enter the system from an automated machine queue or human participant or consumer. Initial analysis of the content can be conducted using machine analysis such as natural language processing customized for specific applications (e.g., for news generally or for specific subsets of news such as news for a certain region or of a certain type). Hendrickson further discloses Machine learning and artificial intelligence analysis of the data identify Relevant Factors and/or other information that may be Relevant Factors or impact Existing Relevant Factors in the data, compare to current information, and determine an impact on a given piece of financial research, Relevant Factor or displayed content (e.g., a report on a company hosted by a financial reporting or research firm). Natural language processing (NLP) analysis of the received data can be used to identify weighted facts, categories, and entities. The initial NLP, Al, and ML analyses can inform review assignments for and categorization of data before funneling the data to machine review or a human network of experts in the identified categories and Boosting algorithms consist of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier. The added classifiers are typically weighted in based on their accuracy as described in Para 0056);
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Groen with the technique of Real-time content analysis of Hendrickson and the motivation is to provide the dosing criteria based on outside data using classifiers and expert rules.
Parry teaches
determining a dose for each cancer therapy and drug taken, wherein the dose determination is an optimization of maximizing therapeutic effect while minimizing likelihood of adverse effects for the combination of cancer therapies taken (‘411; Abstract: Parry describes treating tumors by administering FLASH radiation and a therapeutic agent to a patient with cancer are disclosed. The methods provide the dual benefits of anti-tumor efficacy plus normal tissue protection when combining therapeutic agents with FLASH radiation to treat cancer patients. The methods described herein also allow for the classification of patients into groups for receiving optimized radiation treatment in combination with a therapeutic agent based on patient-specific biomarker signatures. Also provided are radiation treatment planning methods and systems incorporating FLASH radiation and therapeutic agents; Para 0161: Parry further discloses the beam shaping can be performed by a combination of magnetic, electrostatic and mechanical elements with the purpose of maximizing the conformality of the dose to the tumor and generating the radiation treatment plan, one goal is to determine beam paths that minimize the irradiation dose of each sub-volume or voxel of the tissue outside the target as described in Para 0166)
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Groen/Hendrickson with the technique of use of ultra-high dose rate radiation and therapeuticagent as taught by Parry in order to optimize the dosing criteria based on maximizing therapeutic effect and minimizing adverse effect of cancer therapy.
Claim 17 is rejected as the same reason with claim 1.
With respect to claim 2, the combined art teaches the method of claim 1, wherein said collecting step includes inputting data from a site chosen from the group consisting of clinics, electronic medical records (EMRs), pharmaceutical companies, private databases, and clinical research organizations to a central artificial intelligence (AI), and inputting data from monitors, EMRs, and insurance information to the central AI (‘991; Para 0042: data generated from on-site/off-site; Para 0043: private network; Para 0096: various system components and analytical tools, such as neural networks or artificial intelligence, can be utilized to further optimize a drug therapy for the treatment of a disease).
With respect to claim 3, the combined art teaches the method of claim 1, wherein said collecting step is further defined as collecting data on symptoms, diagnoses, proposed cancer therapies, drugs, or treatments, fixed demographics, temporal values, genetic components, imaging, and unstructured data (‘991; Paras 0099: diagnosis, Para 0100: drug; Para 0131: cancer therapy).
With respect to claim 4, the combined art teaches the method of claim 1, wherein said analyzing step includes analyzing data relating to pharmacokinetics, distribution, prior toxicity and efficacy determinations, age, metabolism, and tumor scans to ensure that patients receive safe and effective dose of each drug (‘991; Paras 0055: pharmacokinetics; Para 0067: pharmacokinetics process, i.e. distribution; Para 0122: toxicity).
With respect to claim 5, the combined art teaches the method of claim 1, wherein the outside data includes prior clinical data studies selected from the group of Phase 1 through Phase 4 trial data and criteria related to toxicity and efficacy outcomes (‘991; Para 0042: Clinical data may include previously recorded patient data).
With respect to claim 6, the combined art teaches the method of claim 1, wherein said analyzing step further includes the AI creating a model by extracting any and all variables that effect cancer therapy and drug metabolism and extracting features of how these variables are affected by dosing of additional consumed cancer therapies and drugs (‘991; Paras 0185-0187)).
With respect to claim 7, the combined art teaches the method of claim 6, wherein the model includes variables of age of patient, weight of patient, known side effects of cancer therapies and drugs alone and in combinations with other cancer therapies and drugs, known toxicity range as related to ED 50, efficacy ranges, and chronic treatment effect versus acute treatment (‘991; Para 0056: weight; Para 0131).
With respect to claim 8, the combined art teaches the method of claim 1, wherein said determining step provides a dose that is an optimization of maximizing therapeutic effect while minimizing likelihood of adverse effects for a combination of cancer therapies taken (‘991; Paras 0104, 0132).
With respect to claim 9, the combined art teaches the method of claim 1, further including the step of dispensing each cancer therapy and drug to the patient after ensuring sufficient evaluation criteria was provided before suggesting an outcome (‘991; Para 0067).
With respect to claim 10, the combined art teaches the method of claim 9, further including the step of performing said collecting, analyzing, and determining steps after patient treatment and updated patient scans (‘991; Paras 0055, 0100, 0152.
With respect to claim 11, the combined art teaches the method of claim 1, wherein the cancer therapy is chosen from the group consisting of radiation, surgery, chemotherapy, pain management drugs, and nausea drugs provided as partofa treatment plan generated by the logic engine and displayed in a practitioner- readable report. (‘991; Para 0136: chemotherapy)..
With respect to claim 12, the combined art teaches, according to the method of claim 1, wherein the cancer therapy is chosen from the group consisting of abiraterone acetate, methotrexate, paclitaxel albumin-stabilized nanoparticle formulation, ABVC (doxorubicin hydrochloride, bleomycin, vinblastine sulfate, dacarbazine combination), ABVE (doxorubicin hydrochloride, bleomycin, vinblastine sulfate, etoposide combination), ABVE-PC (doxorubicin hydrochloride, bleomycin, vinblastine sulfate, etoposide, prednisone, cyclophosphamide combination), AC (doxorubicin hydrochloride and cyclophosphamide combination), AC-T (doxorubicin hydrochloride, cyclophosphamide, paclitaxel combination), brentuximab vedotin, ADE (cytarabine, daunorubicin hydrochloride, etoposide combination), ado-trastuzumab emtansine, doxorubicin hydrochloride, fluorouracil, afatinib dimaleate, everolimus, imiquimod, aldesleukin, alemtuzumab, pemetrexed disodium, palonosetron hydrochloride, chlorambucil, aminolevulinic acid, anastrozole, aprepitant, pamidronate disodium, exemestane, nelarabine, arsenic trioxide, ofatumumab, asparaginase erwinia chrysanthemi, bevacizumab, axitinib, azacitidine, BEACOPP (bleomycin, etoposide, doxorubicin hydrochloride, cyclophosphamide, vincristine sulfate, procarbazine hydrochloride, prednisone combination), carmustine, belinostat, bendamustine hydrochloride, BEP (bleomycin, etoposide, cisplatin combination), bevacizumab, bexarotene, tositumomab, I 131 Iodine tositumomab, bicalutamide, carmustine, bleomycin, bortezomib, bosutinib, busulfan, cabazitaxel, cabozantinib-S-malate, CAF (cyclophosphamide, doxorubicin hydrochloride, fluorouracil combination), irinotecan hydrochloride, capecitabine, CAPOX (capecitabine, oxaliplatin combination), carboplatin, carboplatin-taxol combination, carfilzomib, carmustine implant, lomustine, ceritinib, daunorubicin hydrochloride, recombinant HPV bivalent vaccine, cetuximab, chlorambucil, chlorambucil-prednisone combination, CHOP (cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, prednisone combination), cisplatin, cyclophosphamide, clofarabine, CMF (cyclophosphamide, methotrexate, fluorouracil combination), COPP (cyclophosphamide, vincristine sulfate, procarbazine hydrochloride, prednisone combination), COPP-ABV (cyclophosphamide, vincristine sulfate, procarbazine hydrochloride, prednisone, doxorubicin hydrochloride, bleomycin, vinblastine sulfate combination), dactinomycin, crizotinib, CVP (cyclophosphamide, vincristine sulfate, prednisone combination), ifosfamide, ramucirumab, cytarabine, liposomal cytarabine, dabrafenib, dacarbazine, decitabine, dactinomycin, dasatinib, degarelix, denileukin diftitox, denosumab, dexrazoxane hydrochloride, docetaxel, doxorubicin hydrochloride liposome, fluorouracil, rasburicase, epirubicin hydrochloride, oxaliplatin, eltrombopag olamine, enzalutamide, EPOCH (etoposide, prednisone, vincristine sulfate, cyclophosphamide, doxorubicin hydrochloride combination), eribulin mesylate, vismodegib, erlotinib hydrochloride, etoposide phosphate, etoposide, everolimus, raloxifene hydrochloride, toremifene, fulvestrant, FEC (fluorouracil, epirubicin hydrochloride, cyclophosphamide combination), letrozole, filgrastim, fludarabine phosphate, fluorouracil, FOLFIRI (leucovorin calcium, fluorouracil, irinotecan hydrochloride combination), FOLFIRI-bevacizumab combination, FOLFIRI-cetuximab combination, FOLFIRINOX (leucovorin calcium, fluorouracil, irinotecan hydrochloride, oxaliplatin combination), FOLFOX (leucovorin calcium, fluorouracil, oxaliplatin combination), pralatrexate, FU-LV (fluorouracil, leucovorin calcium combination), recombinant HPV quadrivalent vaccine, obinutuzumab, gefitinib, gemcitabine hydrochloride, gemcitabine-cisplatin combination, gemcitabine-oxaliplatin combination, gemtuzumab ozogamicin, imatinib mesylate, glucarpidase, goserelin acetate, trastuzumab, topotecan hydrochloride, hyper-CVAD (cyclophosphamide, vincristine sulfate, doxorubicin hydrochloride, dexamethasone combination), ibritumomab tiuxetan, ibrutinib, ICE (ifosfamide, carboplatin, etoposide combination), ponatinib hydrochloride, idarubicin hydrochloride, idelalisib, ifosamide, axitinib, recombinant interferon α-2b, ipilimumab, irinotecan hydrochloride, romidepsin, ixabepilone, ruxolitinib phosphate, palifermin, pembrolizumab, lapatinib ditosylate, lenalidomide, letrozole, leucovorin calcium, leuprolide acetate, vincristine sulfate liposome, procarbazine hydrochloride, mechlorethamine hydrochloride, megestrol acetate, trametinib, mercaptopurine, mesna, temozolomide, mitomycin C, mitoxantrone hydrochloride, MOPP (mechlorethamine hydrochloride, vincristine sulfate, procarbazine hydrochloride, prednisone combination), plerixafor, vinorelbine tartrate, nelarabine, sorafenib tosylate, nilotinib, tamoxifen citrate, romiplostim, obinutuzumab, ofatumumab, omacetaxine mepesuccinate, pegaspargase, OEPA (vincristine sulfate, etoposide, prednisone, doxorubicin hydrochloride combination), OFF (oxaliplatin, fluorouracil, leucovorin calcium combination), OPPA (vincristine sulfate, procarbazine hydrochloride, prednisone, doxorubicin hydrochloride combination), paclitaxel, PAD (bortezomib, doxorubicin hydrochloride, dexamethasone combination), palifermin, palonosetron hydrochloride, pamidronate disodium, panitumumab, pazopanib hydrochloride, peginterferon α-2b, pembrolizumab, pemetrexed disodium, pertuzumab, plerixafor, pomalidomide, ponatinib hydrochloride, pralatrexate, prednisone, procarbazine hydrochloride, sipuleucel-T, radium 223 dichloride, R-CHOP (rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, prednisone combination), R-CVP (rituximab, cyclophosphamide, vincristine sulfate, prednisone combination), reforafenib, rituximab, romidepsin, ruxolitinib phosphate, talc, siltuximab, sipuleucel-T, sorafenib tosylate, STANFORD V (mechlorethamine hydrochloride, doxorubicin hydrochloride, vinblastine sulfate, vincristine sulfate, bleomycin, etoposide, prednisone combination), sunitinib malate, thalidomide, TAC (docetaxel, doxorubicin hydrochloride, cyclophosphamide combination), temozolomide, temsirolimus, topotecan hydrochloride, toremifene, TPF (docetaxel, cisplatin, fluorouracil combination), trametinib, trastuzumab, vandetanib, VAMP (vincristine sulfate, doxorubicin hydrochloride, methotrexate, prednisone combination), VeIP (vinblastine sulfate, ifosamide, cisplatin combination), vinblastine sulfate, vemurafenib, vincristine sulfate, vincristine sulfate liposome, vinorelbine tartrate, VIP (etoposide, ifosfamide, cisplatin combination), vismodegib, vorinostat, XELOX (capecitabine, oxaliplatin combination), ziv-aflibercept, zoledronic acid, QBECO, QBKPN, QBSAU, QBECP, and combinations thereof.
However, the limitation of cancer therapy is chosen from a group…. as listed above have been considered but are given little patentable weight because they fail to add any structural limitations and are thereby regarded as intended use language listing is intended materials
With respect to claim 13, the combined art teaches the method of claim 1, wherein said collecting step further includes the step of capturing daily activities, intake, and patient symptoms with an application stored on non-transitory computer readable media and determining hidden patterns and effects of the cancer therapies and drugs using an analysis module including regressions, classifiers, and neural networks. (‘991; Paras 0065, 0067).
With respect to claim 15, the combined art teaches the method of claim 1, wherein the patient has cancer chosen from the group consisting of cancer cells associated with adenoid cystic carcinoma, adrenal gland tumors, amyloidosis, anal cancer, appendix cancer, astrocytoma, ataxia-telangiectasia, attenuated familial adenomatous polyposis, Beckwith-Wiedermann Syndrome, bile duct cancer, Birt-Hogg-Dube Syndrome, bladder cancer, bone cancer, brain stem glioma, brain tumors, breast cancer, carcinoid tumors, Carney complex, central nervous system tumors, cervical cancer, colorectal cancer, Cowden syndrome, craniopharyngioma, desmoplastic infantile ganglioglioma, endocrine tumors, ependymoma, esophageal cancer, Ewing sarcoma, eye cancer, eyelid cancer, fallopian tube cancer, familial adenomatous polyposis, familial malignant melanoma, familial non-VHL clear cell renal cell carcinoma, gallbladder cancer, Gardner Syndrome, gastrointestinal stromal tumor, germ cell tumor, gestational trophoblastic disease, head and neck cancer, diffuse gastric cancer, leiomyomatosis and renal cell cancer, mixed polyposis syndrome, pancreatitis, papillary renal cell carcinoma, HIV and AIDS-related cancer, islet cell tumors, juvenile polyposis syndrome, kidney cancer, lacrimal gland tumor, laryngeal and hypopharyngeal cancer, acute lymphoblastic leukemia, acute lymphocytic leukemia, acute myeloid leukemia, B-cell prolymphocytic leukemia, hairy cell leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic T-cell lymphocytic leukemia, eosinophilic leukemia, Li-Fraumeni Syndrome, liver cancer, lung cancer, Hodgkin lymphoma, Non-Hodgkin lymphoma, Lynch Syndrome, mastocytosis, medulloblastoma, melanoma, meningioma, mesothelioma, Muir-Torre Syndrome, multiple endocrine neoplasia type 1, multiple endocrine neoplasia type 2, multiple myeloma, myelodysplastic syndromes, MYH-associated polyposis, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, neuroendocrine tumors, neurofibromatosis type 1, neurofibromatosis type 2, nevoid basal cell carcinoma syndrome, oral and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, parathyroid cancer, penile cancer, Peutz-Jeghers Syndrome, pituitary gland tumors, pleuropulmonary blastoma, prostate cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, alveolar soft part and cardiac sarcoma, Kaposi sarcoma, skin cancer, small bowel cancer, stomach cancer, testicular cancer, thymoma, thyroid cancer, tuberous sclerosis syndrome, Turcot Syndrome, unknown primary, uterine cancer, vaginal cancer, Von Hippel-Lindau Syndrome, Wilms tumors, and Xeroderma pigmentosum (‘991; Para 0131).
However, the limitation of patient cancer therapy is chosen from a group…. as listed above have been considered but are given little patentable weight because they fail to add any structural limitations and are thereby regarded as intended use language listing is intended materials
With respect to claim 16. The method of claim 1, wherein the drugs are chosen from the group consisting of antihistamines, anti-infective agents, antineoplastic agents, autonomic drugs, blood derivatives, blood formation agents, coagulation agents, thrombosis agents, cardiovascular drugs, cellular therapy, central nervous system agents, contraceptives, dental agents, diagnostic agents, disinfectants, electrolytic, caloric, and water balance, enzymes, respiratory tract agents, eye, ear, nose, and throat preparations, gold compounds, heavy metal antagonists, hormones and synthetic substitutes, oxytocics, radioactive agents, serums, toxoids, and vaccines, skin and mucous membrane agents, smooth muscle relaxants, and vitamins (‘991; Claim 16, 17,and 18).
However, the limitation of wherein the drugs are chosen from the group …. as listed above have been considered but are given little patentable weight because they fail to add any structural limitations and are thereby regarded as intended use language listing is intended materials
With respect to claim 18, the combined art teaches the logic engine of claim 17, wherein said algorithm identifies nearest neighbors and gathers data from persons that have similar patient data or underwent a treatment plan with similar cancer therapies and drug combinations, analyzes classifiers across all possible dosage ranges of cancer therapies/drugs, and updates models based on updated patient scans (‘411; Abstract).
With respect to claim 19, the combined art teaches the logic engine of claim 18, wherein said algorithm provides an output in the form of a practitioner readable report displaying comprehensive analysis of the patient, diagnosis, and recommended dosages for each drug, including findings related to drug interaction, decreased efficacy, or side-effect management.(‘411; Para 0158)
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferguson (US. 20090167531A1) in view of Hendrickson et al. (US20230325396 hereinafter Hendrickson).
With respect to claim 20, Ferguson teaches a method of adjusting treatment of a cancer patient, including the steps of:
a patient inputting, by way of a processor executing an application stored on non-transitory computer readable media, data about nutrition, medication, lifestyle, symptoms, and user defined metrics in an application stored on non-transitory computer readable media; integrating data from outside devices and outside databases, including updated patient scans (‘531; Para 0050: the largely non-standard area of over-the-counter medications and nutraseuticals can be integrated into the management regime, by creating electronic medication records within an MMS of over-the-counter medications and nutraseuticals as they are purchased and subsequently consumed. Standard dosage, composition, and other information about the over-the-counter and nutraseuticals purchased can be automatically integrated as electronic medication records with one or more MMS at point of purchase, at the consumers home, or at other locations. The conversion of purchased items to electronic medication records can occur automatically from MMS-enabled cash registers at a retailer, by scanning the barcode on the over-the-counter medication or nutraceutical container (either at the retailer or at the consumers home or other location using an MMS-enabled device), or by entering the barcode or other identifying information into an MMS-enabled device or kiosk. Integrating the management of over-the-counter medicines and nutraseuticals in this way permits one or more MMS Stakeholders to have a more accurate view of the medications and nutraseuticals being taken by a patient. An immediate benefit is the ability of MMS stakeholders to detect medication interactions. Extending this benefit to include a patient's non-prescribed medications extends the view of medical professionals to include more of the patient's lifestyle, and reduces medication interactions between prescription and non-prescription medications and nutraseuticals; Para 0114: using input/output devices);
Hendrickson teaches
performing, by the processor, an analysis on the integrated data using artificial- intelligence techniques selected from the group consisting of regressions, classifiers, and neural networks to identify correlations between the patient's inputs and outcomes (‘396; Para 0050: By disclosure, Hendrickson describes, as in FIG. 1, providing an overview of the systems and methods of the invention as exemplarily applied to news content. A news-based review and scoring system, here labeled NewsCheck receives content, research, knowledge, processes, and/or practices from outside sources such as individual contributors, consumers or third party organizations providing and/or seeking validation, scoring and control for their content and communications either by automated or manual queueing …..content may enter the system from an automated machine queue or human participant or consumer. Initial analysis of the content can be conducted using machine analysis such as natural language processing customized for specific applications (e.g., for news generally or for specific subsets of news such as news for a certain region or of a certain type). Hendrickson further discloses Machine learning and artificial intelligence analysis of the data identify Relevant Factors and/or other information that may be Relevant Factors or impact Existing Relevant Factors in the data, compare to current information, and determine an impact on a given piece of financial research, Relevant Factor or displayed content (e.g., a report on a company hosted by a financial reporting or research firm). Natural language processing (NLP) analysis of the received data can be used to identify weighted facts, categories, and entities. The initial NLP, Al, and ML analyses can inform review assignments for and categorization of data before funneling the data to machine review or a human network of experts in the identified categories and Boosting algorithms consist of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier. The added classifiers are typically weighted in based on their accuracy as described in Para 0056);
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Ferguson with the technique of Real-time content analysis of Hendrickson and the motivation is to provide the dosing criteria based on outside data using classifiers and expert rules);
outputting a result from the data to medical professionals; and
Ferguson in view of Hendrickson discloses
the medical professionals adjusting the treatment of the patient based on the data (‘531; Para 0050: An immediate benefit is the ability of MMS stakeholders to detect medication interactions. Extending this benefit to include a patient's non-prescribed medications extends the view of medical professionals to include more of the patient's lifestyle, and reduces medication interactions between prescription and non-prescription medications and nutraseuticals.).
Ferguson does not disclose explicitly a patient inputting data about nutrition. However, Ferguson discloses integrated into the management regime, by creating electronic medication records within an MMS of over-the-counter medications and nutraseuticals as they are purchased and subsequently consumed. Standard dosage, composition, and other information about the over-the-counter and nutraseuticals purchased can be automatically integrated as electronic medication records with one or more MMS at point of purchase, at the consumers home, or at other locations (‘531; Para 0050).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groen et al. (US.20080008991 A!) in view of Hendrickson et al. (US20230325396 hereinafter Hendrickson) and further in view of Parry et al. (US20199922411A1 hereinafter Parry) and further in view of Hong (US. 20150025394A1)
With respect to claim 14, the combined art does not teach, according to the method of claim 13, further including the step of integrating data from outside devices that measure physiological properties of the patient chosen from the group consisting of general fitness trackers, heartbeat trackers, heart rate trackers, skin temperature trackers, respiratory rate trackers, body posture trackers, eyesight trackers, blood oxygen trackers, glucose level trackers, sleep trackers, body temperature trackers, skin conductance trackers, and combinations thereof hereof, wherein the data are analyzed in combination with outside databases to predict adverse events..
However, Hong discloses the aforementioned feature (‘395; Abstract; Para 0109). It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Groen/Hendrickson/Parry with the technique of providing a wearable fitness monitoring device of Hong and the motivation is to integrate data from different outside device
Response to Arguments
Applicant’s arguments, see Remark, filed 11/24/2025, with respect to the rejection(s) of amended claim(s) 1, 17, 20 under 35USC101 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection for amended claims 1, 17 is made in view of Groen/Hendrickson/Parry.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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