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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/18/2025 has been entered.
Claims 1, 10-12 and 20 have been amended. Claim 15 has been canceled. Claims 1, 5-13, 16-17, and 20-21 have been examined.
Response to Arguments/Amendments
The rejection of claim 10 is withdrawn in view of the claim amendments.
Applicant's arguments filed 8/18/2025 have been fully considered but they are not persuasive.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Kenedy expressly suggests the combination in order to provide better outcomes and higher levels of satisfaction, thereby reducing waste and increasing efficiency in the healthcare industry as well as potentially minimizing adverse reactions, complications and deaths (see Kenedy col. 2, line 63 – col. 3, line 2). Therefore, Applicant’s argument is not persuasive.
Applicant’s arguments with respect to claim(s) 11 and 20 (see p. 11 of Applicant’s remarks) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 6-13, 17 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent 12070338 (filed as application 17207434) in view of U.S. Patent Application 20200027539 by Xie et al. ("Xie") and U.S. Patent 7917438 to Kenedy et al. ("Kenedy").
In regard to claim 1, 12070338 claims:
1. A method for training machine learning models to recommend treatments for a living organism to address a medical condition, comprising: See 12070338, claim 1: “A method for predicting susceptibility of a living organism to a medical condition based on one or more machine learning models, comprising: … taking one or more actions to recommend treatments for the living organism based on the predicted susceptibility of the living organism to the medical condition”
a) receiving a data set of attributes, each respective record in the data set of attributes being associated with a living organism and including information related to one or more attributes, an indication of a medical condition …; See 12070338, claim 1: “for each historical living organism of a plurality of historical living organisms, a plurality of data points in medical history for the historical living organism with an indication of whether the historical living organism has the medical condition”
12070338 does not expressly claim: a treatment applied to the living organism, information about side effects of the treatment and a severity of the side effects, and an indication of treatment success. However, this is taught by Xie and Kenedy. See Xie, ¶ 0032, 0033, 0035, and 0036 as cited in the rejection under 35 USC § 103 below. Also see Kenedy, col. 12, lines 27-44, as cited in the rejection under 35 USC § 103 below. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set and treatment of 12070338 with the attributes taught by Xie in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set and treatment of 12070338 and Xie with the attributes taught by Kenedy in order to provide considerations for varied and complicated measures of success as suggested by Kenedy.
b) replacing null values for features in the received data set with an indication that the features do not apply to the living organism; See claim 5, “replacing null values for features in the data set with an indication that the features do not apply to the living organism.”
c) generating a training data set by featurizing the one or more attributes, the indicated medical condition, the treatment applied, the information about side effects of the treatment and the severity of the side effects, and the indication of treatment success; See 12070338, claim 1: “generating a feature vector based on the data set of living organism attributes.” Also see the teaching of Xie (e.g. ¶ 0034) and Kenedy as cited above.
wherein featurizing the one or more attributes comprises, for each respective medical attribute of the one or more attributes, assigning one of a plurality of values, each value indicating a classification of the respective medical attribute into one of a plurality of categories, and See claim 3, “wherein generating the feature vector comprises: for each attribute in the data set, assigning one of a plurality of numerical values for the attribute based on a value of the attribute in the data set, each value indicating a classification of the respective attribute into one of a plurality of categories.”
wherein generating the training data set comprises: scaling a value of an item in the data set based on a scaling factor associated with an accuracy of a source from which the value was obtained, and featurizing the scaled value of the item. See claim 4, “wherein generating the feature vector comprises: scaling a value of an attribute in the data set based on a scaling factor associated with an accuracy of a source from which the value was obtained; and featurizing the scaled value of the item.”
d) training one or more machine learning models to recommend one or more treatments to apply to the living organism to treat the medical condition based on the generated training data set; and See 12070338, claim 1: “predicting susceptibility of the living organism to the medical condition by generating a prediction using one or more trained machine learning models, the one or more trained machine learning models having been trained based on a featurized data set.”
12070338 does not expressly claim the following limitations, but they are taught by Xie and Kenedy as follows:
e) deploying the trained one or more machine learning models to a computing system for use in treating a living organism. See Xie, Fig. 1, depicting a deployed machine learning model.
wherein the data set of attributes comprises medical … information about the living organism, received from a plurality of data sources. See Xie, Fig. 3 and associated text at least at ¶ 0040, “The text encoding module 308 is configured to receive a medication record 310 … a medication record 312 … a clinical information records 314 … The conditions may include, for example, one or more of the patient's current medication, vital signs, symptoms, laboratory results, past medical history, family history, social history, and allergies.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Xie’s attributes and deployment in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
Xie does not expressly disclose: activity, and environmental information. This is taught by Kenedy. See col. 3, lines 24-25, “features of the customer such as … diet, lifestyle, and zip code (i.e., location).” Note that a broad but reasonable interpretation allows lifestyle to read on activity information, and diet and zip code to read on environmental information. Also see fig. 2 and col. 11, lines 34-36 “provide exercise therapy.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kenedy’s information with the data of 12070338 in order to provide better outcomes and higher levels of satisfaction, thereby reducing waste and increasing efficiency in the healthcare industry as well as potentially minimizing adverse reactions, complications and deaths, as suggested by Kenedy (see col. 2, line 63 – col. 3, line 2).
In regard to claim 6, 12070338 does not expressly claim the limitations. However, they are taught by Xie:
6. The method of claim 5, further comprising: aggregating information from the plurality of external data sources into a single record for each living organism. See Xie, ¶ 0032, “Regarding the text encoding module 102, it is configured to extract information from the clinical record, and derive the clinical-information vector x 108 from the extracted information.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the information of 12070338 with Xie’s data sources in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
In regard to claim 7, 12070338 does not expressly claim the limitations. However, they are taught by Xie:
7. The method of claim 5, wherein the plurality of external data sources comprises a secure medical records data source and one or more other data sources. However, this is taught by Kenedy. See col. 8, lines 6-8, “The identities of the consumers can be masked or anonymized for privacy or security purposes.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data source of 12070338 with Kenedy’s security in order to retain privacy as suggested by Kenedy.
In regard to claim 8, 12070338 does not expressly claim the limitations. However, they are taught by Xie:
8. The method of claim 7, wherein the one or more other data sources include one or more of a physical activity records data source, or a medicine usage data source. See Xie, Fig. 2 element 208. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data sources of 12070338 with Xie’s treatments in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
In regard to claim 9, 12070338 claims:
9. The method of claim 1, wherein the one or more machine learning models comprise clustering-based machine learning models. See 12070338 claim 2, “clustering model.”
In regard to claim 10, 12070338 claims:
10. The method of claim 1, wherein the one or more machine learning models comprise probabilistic models in which … is represented by a probability distribution … See 12070338 claim 1: “wherein the one or more trained machine learning models comprise one or more probabilistic models trained to generate a probability distribution.”
12070338 does not expressly claim: efficacy of each of a plurality of treatments represented by probability over each treatment in a set of treatments for the medical condition. However, this is taught by Xie. See Xie, e.g. ¶ 0043, “the medication correlation module 402 implements a determinantal point process (DPP) that captures the correlations among the medications and outputs scalar measures 404 indicating the correlation of a medication i and a medication j.” Also e.g. ¶ 0047, “the kernel matrix L is computed and probability defined over medication-subset.” Also ¶ 0052, “The synergy interaction suggests that two medications are frequently used simultaneously to treat a disease.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the probabilistic model of 12070338 with Xie’s treatments in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
In regard to claim 11, 12070338 claims:
11. A method for identifying treatments for a living organism to treat a medical condition based on one or more machine learning models, comprising: See 12070338, claim 7, “A method for predicting susceptibility of a living organism to a medical condition based on one or more machine learning models, comprising:” Also see claim 7: “recommend treatments for the living organism.”
receiving a request to identify one or more recommended treatments for a medical condition, the request including a data set of living organism attributes; See 12070338, claim 11, “receiving a request to predict susceptibility of the living organism to the medical condition, the request including a data set of living organism attributes;”
generating a feature vector based on the data set of living organism attributes; See 12070338, claim 7, “generating a feature vector based on the data set of living organism attributes;”
identifying the one or more recommended treatments by generating a prediction using one or more trained machine learning models, See 12070338, claim 7, “predicting susceptibility of the living organism to the medical condition by generating a prediction using one or more trained machine learning models, … recommend treatments for the living organism based on the predicted susceptibility of the living organism to the medical condition.”
the one or more trained machine learning models having been trained based on a featurized data set including, for each historical living organism of a plurality of historical living organisms, one or more attributes, an indication of a medical condition … ; and See 12070338, claim 7, “the one or more trained machine learning models having been trained based on a featurized data set associating, for each historical living organism of a plurality of historical living organisms, a plurality of data points in medical history for the historical living organism with an indication of whether the historical living organism has the medical condition;” Also see Xie and Kenedy as cited above.
12070338 does not expressly claim: … a treatment applied to the living organism, information about side effects of the treatment and a severity of the side effects, and an indication of treatment success. However, this is taught by Xie and Kenedy. See Xie, ¶ 0032, 0033, 0035, and 0036 as cited in the rejection under 35 USC § 103 below. Also see Kenedy, col. 12, lines 27-44, as cited in the rejection under 35 USC § 103 below. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set and treatment of 12070338 with the attributes taught by Xie in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set and treatment of 12070338 and Xie with the attributes taught by Kenedy in order to provide considerations for varied and complicated measures of success as suggested by Kenedy.
outputting information about the identified one or more treatments for the living organism. See 12070338, claim 7, “taking one or more actions to recommend treatments for the living organism based on the predicted susceptibility of the living organism to the medical condition.”
wherein identifying the one or more recommended treatments comprises:
identifying, in the set of matching historical living organisms, a set of treatments applied to living organisms in the set of matching historical living organisms; 12070338 claim 7, “receiving a request to identify one or more recommended treatments for a medical condition, the request including a data set of living organism attributes; generating a feature vector based on the data set of living organism attributes; identifying the one or more recommended treatment.”
12070338 does not expressly claim the following limitations:
for each treatment of the set of treatments applied to historical living organisms in the set of matching historical living organisms, calculating an … [score] based on … [treatment] information associated with each historical living organism; and Xie, ¶ 0037, “The score function of each identified medication may be processed against a threshold score to determine whether the medication is included in the set of predicted medications 112 to be output by the medication prediction module 104.”
Xie does not expressly disclose: average success rate based on success information. This is taught by Kenedy. See col. 12, lines 8-14, “Where evaluations of the success of a service or provider are obtained from multiple sources (e.g., from customer, provider and/or a third party), the results of the evaluations can be indicated separately in the dataset, or they can be used to derive a single value for outcome by averaging or weighted averaging of the evaluations, for example.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kenedy’s success rate with Xie’s score calculation in order to utilize a standardized scoring for evaluating multiple measures of successful treatment as suggested by Kenedy (see col. 11, line 62 – col. 12, lines 26).
Xie also discloses:
selecting treatments from the set of treatments having average success rates exceeding a threshold success rate. Xie, ¶ 0037, “The score function of each identified medication may be processed against a threshold score to determine whether the medication is included in the set of predicted medications 112 to be output by the medication prediction module 104.” Note that as cited above, Kenedy teaches average success and success rate. See Kenedy col. 12, lines 8-14, e.g. “… averaging of the evaluations …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the treatments of 12070338 with Xie’s scoring in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
In regard to claim 12, 12070338 claims:
12. The method of claim 11, wherein the one or more trained machine learning models comprise one or more probabilistic models trained to generate a probability distribution corresponding to a likelihood … for the living organism having the medical condition … See 12070338 claim 1, “generate a probability distribution corresponding to a likelihood of the living organism having the medical condition.” 12070338 does not expressly claim: of each of a plurality of treatments being successful … and any potential side effects and severity of side effects. However, this is taught by Kenedy as cited above.
In regard to claim 13, 12070338 claims:
13. The method of claim 12, wherein identifying the one or more treatments comprises: for each of a plurality of treatments, generating a probability score for the treatment as a weighted average of a likelihood of success generated by each of the one or more trained machine learning models, each model of the one or more trained learning model being associated with a weighting value to assign to a likelihood of the living organism having the medical condition; and … See 12070338 claim 9, “wherein the predicted susceptibility of the living organism comprises a weighted average of the probability that the living organism is susceptible to the medical condition” Also see Kenedy as cited above.
12070338 does not expressly claim: and selecting treatments in the plurality of treatments having a probability score higher than a threshold probability score. However, this is taught by Xie. See Xie, ¶ 0037, “The score function of each identified medication may be processed against a threshold score to determine whether the medication is included in the set of predicted medications 112 to be output by the medication prediction module 104.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the score of 12070338 with Xie’s threshold selection in order to delineate potential treatments as essentially suggested by Xie.
In regard to claim 17, 12070338 claims:
17. The method of claim 11, wherein generating the feature vector comprises: for each attribute in the data set, assigning one of a plurality of numerical values for the attribute based on a value of the attribute in the data set, each value indicating a classification of the respective attribute into one of a plurality of categories. See claim 3, “wherein generating the feature vector comprises: for each attribute in the data set, assigning one of a plurality of numerical values for the attribute based on a value of the attribute in the data set, each value indicating a classification of the respective attribute into one of a plurality of categories.”
In regard to claim 20, 12070338 does not expressly claim:
20. A system for identifying treatments for living organism to treat a medical condition based on one or more machine learning models, comprising: a memory having instructions stored thereon; and a processor configured to execute the instructions to cause the system to: However, this is taught by Xie. See Xie, Fig. 6, elements 604 and 602, depicting a computer system with memory and a processor. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Xie’s system with the method of 12070338 in order to implement modules of the system as suggested by Xie (see ¶ 0077).
All further limitations of claim 20 have been addressed in the above rejection of claim 11.
Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 11 and 21 of U.S. Patent 12070338 in view of Xie and Kenedy as cited above, and further in view of U.S. Patent 11056242 to Jain et al. ("Jain").
In regard to claim 21, 12070338 claims:
21. The method of claim 11, wherein the medical condition comprises respiratory complications caused by SARS-COV-2, and … See 12070338 claim 8: “wherein the medical condition comprises respiratory complications caused by SARS-COV2.”
12070338 does not expressly claim: the recommend treatment comprises one or more of vaccination against SARS-COV-2 or use of a ventilator for a patient having respiratory complications caused by SARS-COV-2. However, this is taught by Jain. See Jain, col. 25, lines 50-55, “The techniques in the present application can also be used to support research to strengthen the healthcare response to Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and future public health emergencies, including pandemics.” Also col. 51, lines 58-65, “Changing a treatment can include may different types of actions, and a few examples include providing a vaccine, providing a medication, providing digital therapeutics interventions, changing settings of a medical treatment device (e.g., ventilator) or a medical monitoring device, and so on. Once selected, changes to monitoring or treatment can be recommended to the user 102a …” Also col. 59, lines 6-12, “For instance, an individual with respiratory illnesses, such as chronic obstructive pulmonary disorder (COPD), may have reduced pathways for breathing and increased risk in delivering oxygen to vital organs. When combined with an illness like COVID-19, which creates additional risks to breathing, it becomes increasingly important to measure the health of the body under varying situations.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Jain’s consideration of Covid treatment with the respiratory complications of 12070338 in order to provide disease prevention and treatment recommendations as suggested by Jain (see col. 22, lines 39-40).
Claims 1 and 6-10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-40 of copending Application No. 17207485 in view of Xie, Kenedy, and U.S. Patent 6151069 to Dunton et al. ("Dunton").
In regard to claim 1, 17207485 claims:
1. A method for training machine learning models to recommend … for a living organism to address a medical condition, comprising: See 17207485 claim 1, “1. A method for predicting efficacy of preventative measures to mitigate spread of a pathogen and illnesses caused therefrom, comprising:”
17207485 does not expressly claim: treatments. However, this is taught by Xie. See Xie, ¶ 0018, “In one aspect of the disclosure, a method of predicting medications to prescribe to a patient.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the predictions of 17207485 with Xie’s treatments in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
a) receiving a data set of attributes, each respective record in the data set of attributes being associated with a living organism and including information related to one or more attributes, an indication of a medical condition …; See 17207485 claim 1, “receiving a data set including a plurality of records, each respective record including at least information identifying a preventative measure and an efficacy of the preventative measure; … in response to a pathogen.” Also see claim 2, “each respective attribute in the data set.”
17207485 does not expressly claim: a treatment applied to the living organism, information about side effects of the treatment and a severity of the side effects, and an indication of treatment success. However, this is taught by Xie and Kenedy. See Xie, ¶ 0032, 0033, 0035, and 0036 as cited in the rejection under 35 USC § 103 below. Also see Kenedy, col. 12, lines 27-44, as cited in the rejection under 35 USC § 103 below. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set of 17207485 with the attributes taught by Xie in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set and treatment of 17207485 and Xie with the attributes taught by Kenedy in order to provide considerations for varied and complicated measures of success as suggested by Kenedy.
b) replacing null values for features in the received data set with an indication that the features do not apply to the living organism; See claim 4, “replacing null values for attributes in the data set with an indication that the attributes do not apply to the preventative measure.”
c) generating a training data set by featurizing the one or more attributes, the indicated medical condition, the treatment applied, the information about side effects of the treatment and the severity of the side effects, and the indication of treatment success; See 17207485 claim 2, “generating a training data set by featurizing the received data set.” Also see Xie and Kenedy as cited above.
wherein featurizing the one or more attributes comprises, for each respective medical attribute of the one or more attributes, assigning one of a plurality of values, each value indicating a classification of the respective medical attribute into one of a plurality of categories, and See claim 2, “featurizing the received data set by assigning, for each respective attribute in the data set, one of a plurality of values, each value indicating a classification of the respective attribute into one of a plurality of categories.”
wherein generating the training data set comprises: scaling a value of an item in the data set based on a scaling factor associated with an accuracy of a source from which the value was obtained, and featurizing the scaled value of the item. See claim 3, “adjusting values associated with an attribute in the data set based on a scaling factor associated with an accuracy of a source from which the value was obtained.”
featurizing the scaled value of the item. See claim 2, “featurizing the received data set.” While clearly directed to featurizing a data set used for training, 17207485 does not expressly disclose featurizing the scaled item. However, this is taught by Dunton. See Dunton, col. 5, lines 39-54, “By scaling the more detailed corrected image data in this way during operation in video mode, the system 200 compensates for the increased noise due to lower light levels that are typically encountered with video operation, such as during videoconferencing.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the featurized data set of 17207485 with Dunton’s scaling in order to yield smaller images that may be easier to store or transmit as suggested by Dunton (see col. 5, lines 31-38).
d) training one or more machine learning models to recommend one or more treatments to apply to the living organism to treat the medical condition based on the generated training data set; and See 17207485 claim 1, “training one or more machine learning models to predict an efficacy of a preventative measure based on the received data set; … recommending one or more preventative measures to implement in response to a pathogen.” Note that Xie teaches treatment as cited above.
e) deploying the trained one or more machine learning models to a computing system for use in treating a living organism. See 17207485 claim 1, “deploying the trained one or more machine learning models to a computing system for use in recommending one or more preventative measures to implement in response to a pathogen.”
wherein the data set of attributes comprises medical, activity, and environmental information about the living organism, received from a plurality of data sources. Claim 7, “wherein the plurality of external data sources comprises a secure medical records data source and one or more other data sources.” Claim 8, “a physical activity records data source, or a patient medicine usage data source.” Claim 19, “information about the physical environment.”
Claims 6-10 of the instant application correspond similarly with claims 2-4 and 6-10, respectively, of 17207485.
This is a provisional nonstatutory double patenting rejection.
Claims 11-13, 17 and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-40 of copending Application No. 17207485 in view of Xie, Kenedy, and U.S. Patent Application 20200152320 by Ghazaleh et al. ("Ghazaleh").
In regard to claim 11, 17207485 claims:
11. A method for identifying … [options] for a living organism to treat a medical condition based on one or more machine learning models, comprising: See 17207485, claim 11, “11. A method for recommending preventative measures to implement to mitigate spread of a pathogen and illnesses caused therefrom, comprising: … identifying the recommended preventative measures based on … one or more trained machine learning models.”
17207485 does not expressly claim: treatments. However, this is taught by Xie. See Xie, ¶ 0018, “In one aspect of the disclosure, a method of predicting medications to prescribe to a patient.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the predictions of 17207485 with Xie’s treatments in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007).
receiving a request to identify one or more recommended treatments for a medical condition, the request including a data set of living organism attributes; See 17207485, claim 11, “receiving a request for recommended preventative measures to implement, the request including at least an identification of the pathogen;” Also see 17207485, claim 21, “receiving a data set including a plurality of records, each respective record including at least information identifying a preventative measure, an efficacy of the preventative measure, a pathogen against which the preventative measure is targeted, and information about a built environment in which the preventative measure is installed;”
generating a feature vector based on the data set of living organism attributes; See 17207485, claim 22, “generating a training data set by featurizing the received data set.”
identifying the one or more recommended treatments by generating a prediction using one or more trained machine learning models, See 17207485, claim 11, “identifying the recommended preventative measures based on at least the identification of the pathogen and one or more trained machine learning models”
the one or more trained machine learning models having been trained based on a featurized data set including, for each historical living organism of a plurality of historical living organisms, one or more attributes, an indication of a medical condition, See 17207485 claim 21, “preventative measures to implement in response to a pathogen.” Also claim 22, “generating a training data set by featurizing the received data set by assigning, for each respective attribute in the data set, one of a plurality of values,”
17207485 does not expressly claim: a treatment applied to the living organism, information about side effects of the treatment and a severity of the side effects, and an indication of treatment success; and However, this is taught by Xie and Kenedy. See Xie, ¶ 0032, 0033, 0035, and 0036 as cited in the rejection under 35 USC § 103 below. Also see Kenedy, col. 12, lines 27-44, as cited in the rejection under 35 USC § 103 below. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set of 17207485 with the attributes taught by Xie in order to make data-driven and intelligent diagnostic predictions for making healthcare recommendations as suggested by Xie (see ¶ 0007). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the data set and treatment of 17207485 and Xie with the attributes taught by Kenedy in order to provide considerations for varied and complicated measures of success as suggested by Kenedy.
outputting information about the identified one or more treatments for the living organism. See 17207485, claim 11, “outputting information about the identified preventative measures.”
wherein the one or more trained machine learning models comprise one or more clustering models. Claim 14, “wherein the one or more trained machine learning models comprise one or more clustering models.”
17207485 does not expressly claim: models … trained to identify a set of matching historical living organisms of the plurality of historical living organisms having similar data sets of attributes to the living organism. However, this is taught by Ghazaleh. See Ghazaleh, ¶ 0006, “Patient features vectors of the plurality of patients can be clustered into a first set of first clusters based on, for example, cosine distance clustering, and a cluster features vector can be computed for each cluster of the first set of first clusters.” Also see ¶ 0011, e.g. “similarities.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the clustering of 17207485 with Ghazaleh’s similarities in order to characterize data for machine learning as suggested by Ghazaleh.
wherein identifying the one or more recommended treatments comprises:
identifying, in the set of matching historical living organisms, a set of treatments applied to living organisms in the set of matching historical living organisms; Xie, ¶ 0028, “a plurality K of candidate medications Y={1, . . . , K}.” Also ¶ 0030, “For example, the machine-learned algorithm that correlates medications may be configured to learn representations of the medication records of numerous medications, compute similarities of the representations in a latent space, and generate a score that indicates similarities among the medications.”
for each treatment of the set of treatments applied to historical living organisms in the set of matching historical living organisms, calculating an average success rate based on success information associated with each historical living organism; and claim 13, “generating a probability score as a weighted average of efficacy probabilities generated by each of the one or more trained machine learning models.”
selecting treatments from the set of treatments having average success rates exceeding a threshold success rate. Claim 15, “selecting, from the identified set of preventative measures, one or more measures having a calculated average efficacy above a threshold value.”
In regard to claim 12, 17207485 claims:
12. The method of claim 11, wherein the one or more trained machine learning models comprise one or more probabilistic models trained to generate a probability distribution corresponding to a likelihood of each of a plurality of treatments being successful for the living organism having the medical condition and any potential side effects and severity of side effects. See 17207485, claim 12, 12. The method of claim 11, wherein the one or more trained machine learning models comprise one or more probabilistic models trained to generate a probability distribution over a plurality of efficacy categories. Also see Xie and Kenedy as cited above.
In regard to claim 13, 17207485 claims:
13. The method of claim 12, wherein identifying the one or more treatments comprises: for each of a plurality of treatments, generating a probability score for the treatment as a weighted average of a likelihood of success generated by each of the one or more trained machine learning models, See 17207485, claim 13, “13. The method of claim 12, wherein identifying the preventative measures comprises generating a probability score as a weighted average of efficacy probabilities generated by each of the one or more trained machine learning models,
each model of the one or more trained learning model being associated with a weighting value to assign to a likelihood of the living organism having the medical condition; and See 17207485, claim 13, “each model of the one or more trained learning model being associated with a weighting value to assign to a predicted efficacy of the preventative measures.
17207485 does not expressly claim: selecting treatments in the plurality of treatments having a probability score higher than a threshold probability score. However, this is taught by Xie. See Xie, ¶ 0037, “The score function of each identified medication may be processed against a threshold score to determine whether the medication is included in the set of predicted medications 112 to be output by the medication prediction module 104.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the score of 17207485 with Xie’s threshold selection in order to delineate potential treatments as essentially suggested by Xie.
Claims 17 and 20 of the instant application correspond similarly with 17207485 claims 20, and 24, respectively.
This is a provisional nonstatutory double patenting rejection.
Claim 21 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-40 of copending Application No. 17207485 in view of Xie and Kenedy as applied above, and further in view of Jain.
In regard to claim 21, 17207485 does not expressly claim:
21. The method of claim 11, wherein the medical condition comprises respiratory complications caused by SARS-COV-2, and the recommend treatment comprises one or more of vaccination against SARS-COV-2 or use of a ventilator for a patient having respiratory complications caused by SARS-COV-2. However, this is taught by Jain. See Jain, col. 25, lines 50-55, “The techniques in the present application can also be used to support research to strengthen the healthcare response to Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and future public health emergencies, including pandemics.” Also col. 51, lines 58-65, “Changing a treatment can include may different types of actions, and a few examples include providing a vaccine, providing a medication, providing digital therapeutics interventions, changing settings of a medical treatment device (e.g., ventilator) or a medical monitoring device, and so on. Once selected, changes to monitoring or treatment can be recommended to the user 102a …” Also col. 59, lines 6-12, “For instance, an individual with respiratory illnesses, such as chronic obstructive pulmonary disorder (COPD), may have reduced pathways for breathing and increased risk in delivering oxygen to vital organs. When combined with an illness like COVID-19, which creates additional risks to breathing, it becomes increasingly important to measure the health of the body under varying situations.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Jain’s consideration of Covid treatment with the respiratory complications of 17207485 in order to provide disease prevention and treatment recommendations as suggested by Jain (see col. 22, lines 39-40).
This is a provisional nonstatutory double patenting rejection.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 6-12, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application 20200027539 by Xie et al. ("Xie") in view of U.S. Patent 7917438 to Kenedy et al. ("Kenedy"), U.S. Patent Application 20190180882 by Han et al. ("Han"), U.S. Patent Application 20200152320 by Ghazaleh et al. ("Ghazaleh"), and U.S. Patent 6151069 to Dunton et al. ("Dunton").
In regard to claim 1, Xie discloses:
1. A method for training machine learning models to recommend treatments for a living organism to address a medical condition, comprising: See Xie, ¶ 0018, “In one aspect of the disclosure, a method of predicting medications to prescribe to a patient.”
a) receiving a data set of attributes, each respective record in the data set of attributes being associated with a living organism and including information related to one or more attributes, an indication of a medical condition, a treatment applied to the living organism, information about side effects of the treatment and …; See Xie, ¶ 0032, “records identifying one or more conditions of the patient.” Also ¶ 0033, “a machine-learned algorithm 110a for use in the medication prediction module 104 is previously trained in accordance with a training set K of medication i vectors ai 202, where { ai }i=1K and a training set of clinical information vectors x 204 to associate clinical information with medications.” Also ¶ 0035, “what conditions/diseases the medication can treat, and its side effects, dosage, and so on.” Also ¶ 0036, “current medication, vital signs, symptoms, laboratory results, past medical history.” Also ¶ 0052, “The antagonism interaction indicates that when used together, two medications may bring in a negative medical effect. Medications with antagonism interactions should be prohibited from being used together. The synergy interaction suggests that two medications are frequently used simultaneously to treat a disease.”
Xie does not expressly disclose: a severity of the side effects, and an indication of treatment success. However, this is taught by Kenedy. See Kenedy, col. 12, lines 27-44, e.g. “the outcome data used to derive outcomes such as success levels can include considerably more varied and complicated measures of success … ratings for factors such as product cost, ease of product usage, number of side effects, severity of side effects, number of symptoms resolved and speed of symptom resolution.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kenedy’s data with Xie’s data in order to provide considerations for varied and complicated measures of success as suggested by Kenedy.
Xie does not expressly disclose: b) replacing null values for features in the received data set with an indication that the features do not apply to the living organism; However, this is taught by Han. See Han, ¶ 0056, “The masking data may be configured to distinguish featur