DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claim(s) 8 is/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.
The term “bad” in claim 8 is a relative term which renders the claim indefinite. The term “bad” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. How is a physical condition considered “bad”? Appropriate clarification is requested for the proper interpretation of the claim limitations, as the ambiguity renders the metes and bounds of the claim unclear.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to an apparatus which is within the four statutory categories (i.e., machine). Claim 9 is drawn to a method which is within the four statutory categories (i.e., method). Claim 10 is drawn to a non-transitory computer-readable recording medium which is within the four statutory categories (i.e., manufacture).
Independent claim 1 (which is representative of independent claims 9-10) recites… identify a customer; acquire information regarding a prescription issued to the identified customer and information indicating a work status of medicine dispensing based on the prescription; predict a time for the customer to wait based on at least one of the information regarding the prescription and the information indicating the work status of medicine dispensing based on the prescription; and output information to be provided to the customer when the predicted time for the customer to wait exceeds a threshold value.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. That is, other than reciting “one or more processors” (claim 1), “computer” (claims 9-10), the claim encompasses rules or instructions to collect data, analyze the collected data, and output data based on the analysis (i.e., predicted wait time). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Claim 1 recites additional elements (i.e., An information providing apparatus comprising: a memory storing instructions; and one or more processors). Claim 9 recites additional elements (i.e., a computer). Claim 10 recites additional elements (i.e., A non-transitory computer-readable recording medium that records a program; a computer). Looking to the specifications, a computer having a memory storing instructions, one or more processors, a non-transitory computer-readable recording medium that records a program is described at a high level of generality (page 21, line 20 – page 22, line 25), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computer having a memory storing instructions, one or more processors, a non-transitory computer-readable recording medium that records a program) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-8 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-8 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. US 2019/0163876 A1 (hereinafter referred to as "Remme") in view of U.S. Patent No. US 11,900,228 B1 (hereinafter referred to as "Snopek").
Regarding claim 1, Remme teaches an information providing apparatus comprising:
a memory storing instructions (Remme: ¶ 0233); and
one or more processors configured to execute the instructions (Remme: ¶ 0233) to:
identify a customer (Remme: ¶ 0095, i.e., “the kiosk incorporates biometrics technology for authenticating the identity of a user of the kiosk”);
Yet, Remme does not explicitly teach, but Snopek teaches, in the same field of endeavor,
acquire information regarding a prescription issued to the identified customer and information indicating a work status of medicine dispensing based on the prescription (Snopek: column 10, lines 19-32, i.e., “patient information including patient demographics 224, payor information 212, 214, 216, 218, 220 and 222 (block 306), and medication order information 201, 202, 203, 204, 206, 207 208 209, 210 and 211 is obtained. Prescriber information 240 is also received from the requester such as the prescriber name or identifier 242 and the type of prescriber 244. Multiple items of store information 250 are also collected such as store staffing information 252, the number of patients waiting 254, the number of prescriptions waiting to be filled 256, store hours 258, and store geographic information 260. Ancillary information 224 may also be obtained (block 308), such as a socioeconomic status 230 for the patient, a health status 238 (e.g., the patient smokes) for the patient, etc.”);
predict a time for the customer to wait based on at least one of the information regarding the prescription and the information indicating the work status of medicine dispensing based on the prescription (Snopek: column 10, lines 32-37, i.e., “A machine learning input vector 122 is formed from the prescription 104 and the obtained prescription characteristics (block 310), and passed through the machine learning model implemented by the machine learning module 120 (block 312) to obtain a waiting time estimate 128 for the prescription 104”); and
output information to be provided to the customer when the predicted time for the customer to wait exceeds a threshold value (Snopek: column 10, line 67 – column 11, line 5, i.e., “If the estimated waiting time is shorter at an alternative retail pharmacy, the prescription filler 102 may provide an indication of the alternative retail pharmacy and a recommendation for the patient to have the prescription filled at the alternative retail pharmacy to the patient's client device”).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include acquire information regarding a prescription issued to the identified customer and information indicating a work status of medicine dispensing based on the prescription; predict a time for the customer to wait based on at least one of the information regarding the prescription and the information indicating the work status of medicine dispensing based on the prescription; and output information to be provided to the customer when the predicted time for the customer to wait exceeds a threshold value, as taught by Snopek, within the system of Remme, with the motivation of “estimating and reducing patient waiting time for retail prescriptions to be filled” (Snopek: column 1, lines 16-17).
Regarding claim 2, Remme and Snopek teach the information providing apparatus according to claim 1, wherein
the one or more processors are further configured to execute the instructions to:
identify the customer by using biological information of the customer (Remme: ¶ 0095, i.e., “the kiosk incorporates biometrics technology for authenticating the identity of a user of the kiosk”).
Regarding claim 3, Remme and Snopek teach the information providing apparatus according to claim 1, wherein
the one or more processors are further configured to execute the instructions to:
acquire the information regarding the prescription from an electronic prescription corresponding to the prescription or image data acquired by photographing the prescription (Snopek: column 2, lines 46-49, i.e., “The prescription waiting time estimation system may receive the prescription…via an image of the prescription captured by the patient at the patient's client device”; column 15, lines 19-26).
The obviousness of combining the teachings of Remme and Snopek are discussed in the rejection of claim 1, and incorporated herein.
Regarding claim 4, Remme and Snopek teach the information providing apparatus according to claim 1, wherein
the one or more processors are further configured to execute the instructions to:
predict the time for the customer to wait based on a type of a medicine used for medicine dispensing based on the prescription (Snopek: column 8, lines 4-17, i.e., “An example input vector 122 to the machine learning module 120 is shown in FIG. 2… A diagnosis field 201 represents a medical diagnosis associated with the prescription. An rx_sign_norm field 202 represents usage information, i.e., how the medication is to be used (e.g., take 1 dose two times daily), a drug strength field 203 represents an amount of the medication per dose, a generic_prod_id field 204 provides information regarding generic equivalents, and a drug_type field 206 includes 14 characters that indicate drug group (e.g., decongestant), drug class (e.g., sympathomimetics), drug subclass (e.g., systemic decongestants), drug name (e.g., pseudoephedrine), drug name ext. (e.g., hydrochloride), dosage form (e.g., tablet) and strength (e.g., 60 mg)”; column 10, lines 21-37, i.e., “medication order information 201, 202, 203, 204, 206, 207 208 209, 210 and 211 is obtained…A machine learning input vector 122 is formed from the prescription 104 and the obtained prescription characteristics (block 310), and passed through the machine learning model implemented by the machine learning module 120 (block 312) to obtain a waiting time estimate 128 for the prescription 104”).
The obviousness of combining the teachings of Remme and Snopek are discussed in the rejection of claim 1, and incorporated herein.
Regarding claim 5, Remme and Snopek teach the information providing apparatus according to claim 1, wherein
the one or more processors are further configured to execute the instructions to:
predict the time for the customer to wait based on the information indicating the work status of medicine dispensing based on the prescription (Snopek: column 8, lines 4-45, i.e., “An example input vector 122 to the machine learning module 120 is shown in FIG. 2… The input vector 122 further includes the number of refills remaining 207, whether prior authorization (referred to as pre-auth in figure 2o) is required 209, whether prior authorization has already been obtained 211…Particular store information may also be included such as the number of pharmacists 240 or pharmacy technicians currently on duty 242, a pharmacy stock indication (to indicate if the medicine is out of stock) 244, the number of prescriptions currently waiting to be filled 246, an indicator that the patient is waiting in the store 248, store hour information such as the closing time 250 or an indication that it is a 24 hour store”; column 10, lines 21-37, i.e., “medication order information 201, 202, 203, 204, 206, 207 208 209, 210 and 211 is obtained…Multiple items of store information 250 are also collected such as store staffing information 252, the number of patients waiting 254, the number of prescriptions waiting to be filled 256, store hours 258…A machine learning input vector 122 is formed from the prescription 104 and the obtained prescription characteristics (block 310), and passed through the machine learning model implemented by the machine learning module 120 (block 312) to obtain a waiting time estimate 128 for the prescription 104”).
The obviousness of combining the teachings of Remme and Snopek are discussed in the rejection of claim 1, and incorporated herein.
Regarding claim 6, Remme and Snopek teach the information providing apparatus according to claim 1, wherein
the one or more processors are further configured to execute the instructions to:
determine content of the information to be provided to the customer in accordance with a length of the predicted time to wait (Snopek: column 10, line 67 – column 11, line 5, i.e., “If the estimated waiting time is shorter at an alternative retail pharmacy, the prescription filler 102 may provide an indication of the alternative retail pharmacy and a recommendation for the patient to have the prescription filled at the alternative retail pharmacy to the patient's client device”).
The obviousness of combining the teachings of Remme and Snopek are discussed in the rejection of claim 1, and incorporated herein.
Regarding claim 7, Remme and Snopek teach the information providing apparatus according to claim 1, wherein
the one or more processors are further configured to execute the instructions to:
authenticate the customer by using authentication information issued based on the information regarding the prescription (Remme: ¶ 0065, i.e., “The patient may also be prompted to enter the PIN that was e-mailed to the patient, thereby facilitating a dual authentication required receipt of a scan of the computer readable symbol and the patient's assigned PIN”).
Regarding claim 8, Remme and Snopek teach the information providing apparatus according to claim 1.
Yet, Remme and Snopek do not explicitly teach, but Snopek teaches, in the same field of endeavor, wherein
the one or more processors are further configured to execute the instructions to:
estimate a physical condition of the customer based on at least one of biological information of the customer and the information regarding the prescription (Snopek: column 8, lines 5-7, i.e., “A diagnosis field 201 represents a medical diagnosis associated with the prescription”; column 10, lines 21-37, i.e., “medication order information 201…is obtained…A machine learning input vector 122 is formed from the prescription 104 and the obtained prescription characteristics (block 310), and passed through the machine learning model implemented by the machine learning module 120 (block 312) to obtain a waiting time estimate 128 for the prescription 104”); and
allow, in a case where the customer is estimated to be in a bad physical condition, not to output the information to be provided to the customer even when the predicted time for the customer to wait exceeds the threshold value (Snopek: column 10, line 67 – column 11, line 5, i.e., “If the estimated waiting time is shorter at an alternative retail pharmacy, the prescription filler 102 may provide an indication of the alternative retail pharmacy and a recommendation for the patient to have the prescription filled at the alternative retail pharmacy… to a pharmacy workstation of the retail pharmacy filling the prescription for display to the…pharmacy personnel”).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include wherein the one or more processors are further configured to execute the instructions to: estimate a physical condition of the customer based on at least one of biological information of the customer and the information regarding the prescription; and allow, in a case where the customer is estimated to be in a bad physical condition, not to output the information to be provided to the customer even when the predicted time for the customer to wait exceeds the threshold value, as taught by Snopek, with the system of Remme and Snopek, with the motivation of “estimating and reducing patient waiting time for retail prescriptions to be filled” (Snopek: column 1, lines 16-17).
Regarding claim 9, claim 9 recites substantially similar limitations analogous to those already addressed in claim 1, and thus, claim 9 is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Regarding claim 10, claim 10 recites substantially similar limitations analogous to those already addressed in claim 1, and thus, claim 10 is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2007/0136376 A1 teaches authenticating patient information and determining waiting times for a prescription at a plurality of pharmacies.
WO 2018/035147 A1 teaches digitally capturing patient information and controlling the dispensing time of a prescription.
“Reducing the outpatient waiting time based on CBR algorithm using smartphone” teaches using a smartphone application to reduce predicted patient waiting times.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683