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 .
DETAILED ACTION
Status of Claims
Claim(s) 1-16 have been examined.
Claim(s) 17-20 have been canceled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
I. Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the pharmacy selection model of the individual". There is insufficient antecedent basis for this limitation in the claim. For purposes of examination, it will be interpreted as “the pharmacy selection model[s] of the individual”.
II. Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation "wherein the plurality of factors include". There is insufficient antecedent basis for this limitation in the claim. For purposes of examination, it will be interpreted as “wherein the first factor and the second factor are determined from a plurality of factors including:”
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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) 1-2, 4-8, 10-11, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohan (US 2013/0054258) in view of Reference U (see PTO-892).
Referring to Claim 1, Cohan teaches a computer system for selecting a pharmacy, the computer system comprising one or more processors configured to:
data including a) input data comprising features; and b) output data labeled according to: i) which pharmacy or pharmacies the individual has previously used, ii) travel times to the previously used pharmacies, iii) wait times at the previously used pharmacies, iv) prices of medications the individual has purchased at the previously used pharmacies, v) whether another product or class of products was available at the previously used pharmacies, and/or (vi) whether a locker was available at the previously used pharmacies (see Cohan ¶0035 and Fig. 13-19);
receive: an electronic indication of a medication for the individual (see Cohan Fig. 10 and ¶0073), or (ii) a location of the individual (see Cohan ¶0046);
determine one or more pharmacies to be presented to the individual, the one or more pharmacies determined based on the data and the electronic indication of the medication or the location of the individual (see Cohan ¶0035).
Cohan does not explicitly teach using a machine learning algorithm and an initial training dataset to build a plurality of pharmacy selection models of an individual, wherein the initial training dataset comprises the data and wherein individual pharmacy selection models of the plurality of pharmacy selection models are selected from a group of models including K-nearest neighbors (KNN), support vector machines (SVM), deep neural networks (DNN), decision trees, random forests, and recurrent neural networks (RNN), and wherein the one or more pharmacies are determined based on the pharmacy selection models.
However, Reference U teaches using machine learning algorithm and an initial training dataset to build a plurality of models and wherein the initial training dataset comprises labeled data (see Reference U p.2 a set of labeled data; p.11-13 training a set of models) and wherein models of the plurality of models are selected from a group of models including K-nearest neighbors (KNN), support vector machines (SVM), deep neural networks (DNN), decision trees, random forests, and recurrent neural networks (RNN) (see Reference U p.11-13, SVM and KNN) and wherein data is determined using the models (see Reference U p.15-18). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the prior art of Cohan would continue to teach determining one or more pharmacies based on data, except that now the data would be used to train machine learning models according to the teachings of Reference U and those models would be used be used in the determination of the pharmacies. This is a predictable result of the combination.
Referring to Claim 2, the combination teaches the computer system of claim 1, wherein the one or more processors are further configured to: using the machine learning algorithm, continuously update plurality of pharmacy selection models of the individual based on subsequent pharmacy use by the individual (see Reference p.2-11, the models can be trained again using updated training data).
Referring to Claim 4, the combination teaches the computer system of claim 1, wherein the one or more processors are further configured to: display the determined plurality of pharmacies as a list in an order according to: (i) a prescription fill time, and (ii) a travel time from the location of the individual (see Cohan ¶0035).
Referring to Claim 5, the combination teaches the computer system of claim 1, wherein the initial data training dataset comprises the data regarding (iii) wait times at the previously used pharmacies or (vi) whether the locker was available at the previously used pharmacies (see Cohan ¶0035).
Referring to Claim 6, Cohan teaches a computer system for selecting a pharmacy, the computer system comprising one or more processors configured to:
receive, from an individual, an indication of a medication (see Cohan Fig. 10);
determine a location of the individual (see Cohan ¶0046);
identify a plurality of pharmacies based on a first factor (see Cohan ¶0035);
select a preferred pharmacy from the plurality of pharmacies based on a second factor (see Cohan ¶¶0035,59).
Cohan does not explicitly teach using a machine learning algorithm and an initial training dataset to build a plurality of pharmacy selection models of an individual, wherein the initial training dataset comprises the data and wherein individual pharmacy selection models of the plurality of pharmacy selection models are selected from a group of models including K-nearest neighbors (KNN), support vector machines (SVM), deep neural networks (DNN), decision trees, random forests, and recurrent neural networks (RNN), and wherein the models are used to determine the first factor and second factor.
However, Reference U teaches using machine learning algorithm and an initial training dataset to build a plurality of models and wherein the initial training dataset comprises labeled data (see Reference U p.2 a set of labeled data; p.11-13 training a set of models) and wherein models of the plurality of models are selected from a group of models including K-nearest neighbors (KNN), support vector machines (SVM), deep neural networks (DNN), decision trees, random forests, and recurrent neural networks (RNN) (see Reference U p.11-13, SVM and KNN) and wherein models are used to determine data (see Reference U p.15-18). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the prior art of Cohan would continue to teach determining one or more pharmacies based on data, except that now the data would be used to train machine learning models according to the teachings of Reference U and those models would be used be used in the determination of the first factor and second factor, which comprises data. This is a predictable result of the combination.
Referring to Claim 7, the combination teaches the computer system of claim 6, wherein the plurality of factors include: whether the pharmacy has a medication in stock, wait time at the pharmacy, geographic distance to the individual, travel time from the location of the individual, urgency of filling a prescription, price of a prescription, whether another product or class of products available at the pharmacy, whether a locker is available at the pharmacy (see Cohan ¶0035).
Referring to Claim 8, the combination teaches the computer system of claim 6, wherein the first factor is geographic distance from the location of the individual (see Cohan ¶0035).
Referring to Claim 10, the combination teaches the computer system of claim 6, wherein the second factor is a price of the indicated medication based on an insurance carrier of the individual (see Cohan ¶0042).
Referring to Claim 11, the combination teaches the computer system of claim 6, wherein: the second factor is an urgency of filling a prescription, and the one or more processors are further configured to receive an input from the individual of an indication of the urgency as a time period (see Cohan Fig. 17 item 1710).
Referring to Claim 13, the combination teaches the computer system of claim 6, wherein: the preferred pharmacy is a first preferred pharmacy; the one or more processors are further configured to: select a second preferred pharmacy from the plurality of pharmacies based on the second factor; display the first and second preferred pharmacies to allow the individual to select between the first and second preferred pharmacies (see Cohan Fig. 20).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohan (US 2013/0054258) in view of Reference U (see PTO-892) and in further view of Porter (US 2015/0142479).
Referring to Claim 9, the combination teaches the computer system of claim 6, but does not teach wherein the second factor is a travel time including road traffic. However, Porter teaches wherein a factor can be road traffic (see Porter ¶0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the prior art of Cohan and Reference U would continue to teach selecting a preferred pharmacy except that now a factor would include road traffic according to Porter. This is a predictable result of the combination.
Claim(s) 3 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohan (US 2013/0054258) in view of Reference U (see PTO-892) and in further view of OGA (JP 2018073066).
Referring to Claim 3, the combination teaches the computer system of claim 1, but does not teach wherein the one or more processors are configured to display, on a display, a map showing pharmacies of the determined plurality of pharmacies with: pharmacies with a short fill time displayed as green, pharmacies with an intermediate fill time displayed as yellow, pharmacies with a long fill time displayed as red. However, OGA teaches this (see OGA Page 5 middle of page). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the prior art of the combination would continue to teach determining one or more pharmacies that could fulfill the prescription except that now they would be displayed on a map with colors indicating fill times according to OGA. This is a predictable result of the combination.
Referring to Claim 14, the combination teaches the computer system of claim 6, but does not teach wherein the one or more processors are further configured to: assign scores to each pharmacy of the plurality of pharmacies, display the plurality of pharmacies on a map, and color code each displayed pharmacy according to the assigned scores. However, OGA teaches this (see OGA Page 5 middle of page). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the combination would continue to teach determining one or more pharmacies that could fulfill the prescription except that now they would be displayed on a map with colors indicating fill times according to OGA. This is a predictable result of the combination.
Referring to Claim 15, the combination teaches the computer system of claim 6, but does not explicitly teach wherein the one or more processors are further configured to: assign scores to each pharmacy of the plurality of pharmacies and display the plurality of pharmacies as a list in an order according to the assigned scores. However, OGA teaches this (see OGA Page 5 middle of page). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the combination would continue to teach determining one or more pharmacies that could fulfill the prescription except that now they would be displayed on a map with colors indicating fill times according to OGA. This is a predictable result of the combination.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohan (US 2013/0054258) view of Reference U (see PTO-892) and in further view of Wanker (US 7,302,429).
Referring to Claim 12, the combination teaches the computer system of claim 6, but does not teach wherein the second factor is whether groceries are available at the pharmacy. However Wanker teaches a search factor can be food (see Wanker Col. 13 lines 48-49 and Col. 22 lines 4-6). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the prior art of the combination would continue to teach selecting a preferred pharmacy except that now a factor would include road traffic according to Wanker. This is a predictable result of the combination.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohan (US 2013/0054258) in view of Reference U (see PTO-892) and in further view of Moreno (US 2002/0035515).
Referring to Claim 16, the combination teaches the computer system of claim 6, but does not teach wherein the one or more processors are further configured to: send a prescription corresponding to the indicated medication to the preferred pharmacy, receive a locker assignment for storage of medication of the prescription, send the locker assignment to the individual. However, Moreno teaches sending an order to a store and receive a locker assignment for storage of medication of the prescription, send the locker assignment to the individual (see Moreno ¶¶0058,57,54). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine the references because the results would be predictable. Specifically, the combination would continue to teach selecting a preferred pharmacy except that now the prescription would be sent there and lockers would be used for fulfillment. This is a predictable result of the combination.
Remarks
The double patenting rejection has been withdrawn because the applicant has amended the claims and overcome the rejection.
The claim objection has been withdrawn because the applicant has amended the claims and overcome the rejection.
In regards to the rejection under 35 U.S.C. 101, the rejection has been withdrawn because the applicant has amended the claims and overcome the rejection. The applicants arguments on page 13 of the remarks directed to paragraph [0052] of the specification regarding how an ensemble of models can overcome the technical deficiency of a single machine learning model.
In regards to the rejections under 35 U.S.C. 103, these arguments made by the applicant are fully addressed in the rejection above as they are directed to newly amended limitations which have been rejected using newly found prior art.
Conclusion
Additional prior art relevant to the invention but not relied upon includes:
Sweard (US 2016/0307265) which teaches order throttling.
Angel (US 2013/0325494) which teaches prescription fulfillment.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW E ZIMMERMAN whose telephone number is (571)270-5278. The examiner can normally be reached 8-4pm M-T, 8-12pm W.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeff Smith can be reached at (571)272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW E ZIMMERMAN/Primary Examiner, Art Unit 3688