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 .
Foreign Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/12/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-18 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. Claims 1-6 are drawn to a system, claims 7-12 are drawn to a method, and claims 13-18 are drawn to a medium, each of which is within the four statutory categories. Claims 1-18 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a prediction system for predicting a use end time of a medical device to be lent in a medical device lending system, the prediction system being a system that:
1) stores a learned model that has undergone machine learning to output an end time prediction result that is a prediction result of predicting the use end time of the medical device by inputting electronic chart data describing information indicating a necessity of use of the medical device and lending device data indicating the medical device that is being lent, using learning data including lending record data indicating a lending record that is a record of the medical device that has been lent, the lending record including a record indicating that the use of the medical device is ended, and the electronic chart data describing information indicating the necessity of the use of the medical device that has been lent; and
2) inputs the lending device data indicating the medical device that is being lent and the electronic chart data describing the information indicating the necessity of the use of the medical device into the learned model to acquire the end time prediction result.
Claim 1 recites, in part, performing the steps of 1) stores a learned model that has undergone machine learning to output an end time prediction result that is a prediction result of predicting the use end time of the medical device by inputting electronic chart data describing information indicating a necessity of use of the medical device and lending device data indicating the medical device that is being lent, using learning data including lending record data indicating a lending record that is a record of the medical device that has been lent, the lending record including a record indicating that the use of the medical device is ended, and the electronic chart data describing information indicating the necessity of the use of the medical device that has been lent and 2) inputs the lending device data indicating the medical device that is being lent and the electronic chart data describing the information indicating the necessity of the use of the medical device into the learned model to acquire the end time prediction result. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how a person can determine a time prediction for how long something will be lent out using an equation. Independent claims 7 and 13 recite similar limitations and are also directed to an abstract idea under the same analysis.
Depending claims 2-6, 8-12, and 14-18 include all of the limitations of claims 1, 7, and 13, and therefore likewise incorporate the above described abstract idea. Depending claims 3, 9, and 15 add the additional steps of “a different learned model is stored for each kind or each model number of the medical device as the learned model” and “the end time prediction result is acquired using the corresponding learned model for each kind or each model number of the medical device” and claims 6, 12, and 18 add the additional steps of “the medical device lending system includes a reservation system for temporarily reserving lending of the medical device” and “the lending record data includes data in which information indicating the medical device temporarily reserved by the reservation system is associated with information indicating a record of actual lending based on a temporary reservation”. Additionally, the limitations of depending claims 2, 4-5, 8, 10-11, 14, and 16-17 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-6, 8-12, and 14-18 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 7, and 13 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a computer (from claims 7 and 13) and b) a reservation system (from claims 6, 12, and 18) to perform the claimed steps.
The a) computer and b) reservation system in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification for a lack of description of anything but what may be considered as generic parts for the elements above, see MPEP 2106.05(f)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) a computer and b) a reservation system to perform the claimed steps amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity), a general linking to a particular technological field, or mere instructions to apply the exception using a generic computer component that does not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mental steps, certain method steps of organizing human activity, or certain mathematical steps. Specifically, MPEP 2106.05(f) recites that the following limitations are not significantly more:
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates a prediction utilizing a) computer and b) reservation system, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-18 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claims 1-2, 4-8, 10-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2018/0176148 to Ku et al. in view of U.S. Patent No. 12,552,035 to Cella et al.
As per claim 1, Ku et al. teaches a prediction system for predicting a use end time of a resource to be lent in a resource lending system, the prediction system being a system that:
--stores a model to output an end time prediction result that is a prediction result of predicting the use end time of the resource by inputting electronic chart data describing information indicating a necessity of use of the resource and lending device data indicating the resource that is being lent, (see: paragraph [0040] where there is a stored algorithm which predicts the end time of using a resource) using learning data including lending record data indicating a lending record that is a record of the resource that has been lent, (see: 230 of FIG. 2 where there is learning data of past resource usage data the device. This indicates a lending record) the lending record including a record indicating that the use of the resource is ended, (see: paragraphs [0003] and [0033] and 230 of FIG. 2 where there is a lending record including a record indicating that usage has ended via knowing the time period of previous usage. The ending is the end of the previous time period) and the electronic chart data describing information indicating the necessity of the use of the resource that has been lent; (see: paragraph [0006] where the future workload is taken into account. The workload indicates the necessity of use for the device that is being borrowed) and
--inputs the lending device data indicating the resource that is being lent and the electronic chart data describing the information indicating the necessity of the use of the resource into the model to acquire the end time prediction result (see: paragraph [0057] where data is being received based on the usage. Also see: paragraphs [0003] and [0047] where there is dynamic allocation of resources based on the received data. This data would include the workload and the resource. The end result is a prediction of a distribution of characteristics of resource usage).
Ku et al. may not further, specifically teach
1) --medical device as a resource; and
2) –model as a learned model that has undergone machine learning.
Cella et al. teaches:
1) --medical device as a resource; (see: column 46, lines 24-54 where there is a medical resource as a resource) and
2) –model as a learned model that has undergone machine learning (see: 5720 and 5750 of FIG. 58 where there is a machine learning model and FIG. 117 where there is training of machine learning systems).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute 1) medical device as a resource as taught by Cella et al. for the resource as disclosed by Ku et al. since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, Ku et al. already teaches of resource allocation thus replacing the type of resource being allocated would obtain predictable results or resource allocation. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute 2) a learned model that has undergone machine learning as taught by Cella et al. for model as disclosed by Ku et al. since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, Ku teaches of using a model thus replacing that model with a learned model would obtain predictable results of using a model to make a prediction. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
As per claim 2, Ku et al. and Cella et al. in combination teaches the system of claim 1, see discussion of claim 1. Ku et al. further teaches wherein the lending record data and the lending device data, or the electronic chart data include staff information indicating at least one of a staff member who uses the resource and a group to which the staff member belongs (see: paragraph [0004] where there is a client from a plurality of clients. The plurality is the group, and the client is the user of the resource).
Cella et al. teaches a-medical device as a resource (see: column 46, lines 24-54 where there is a medical resource as a resource).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 4, Ku et al. and Cella et al. in combination teaches the system of claim 1, see discussion of claim 1. Cella et al. further teaches wherein the lending record data and the lending device data, or the electronic chart data include information indicating a transporter (see: column 11, lines 34-48 where there is an autonomous mobile robot. Also see: column 14, lines 1-38 where there is robot assignment to a job. Also see: column 245, lines 1-9 where there is assignment of staff to a job. The chart data is the assignment data and this data is indicative of a transporter).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 5, Ku et al. and Cella et al. in combination teaches the system of claim 4, see discussion of claim 4. Cella et al. further teaches wherein the transporter includes an autonomously movable mobile robot and a hospital staff member (see: column 11, lines 34-48 where there is an autonomous mobile robot. Also see: column 14, lines 1-38 where there is robot assignment to a job. Also see: column 245, lines 1-9 where there is assignment of staff to a job).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 6, Ku et al. and Cella et al. in combination teaches the method of claim 1, see discussion of claim 1. Ku et al. further teaches wherein:
--the medical device lending system includes a reservation system for temporarily reserving lending of the resource (see: paragraph [0044] where there is a reservation system for temporarily reserving resources).
Cella et al. further teaches a-medical device as a resource; (see: column 46, lines 24-54 where there is a medical resource as a resource) and
--the lending record data includes data in which information indicating the medical device temporarily reserved by the reservation system is associated with information indicating a record of actual lending based on a temporary reservation (see: column 162, lines 3-10 where there is data of historical records. Also see: column 454, lines 39-52 where there is assignment of fleet resources. Also see: column 498, lines 8-12 where there is historical data of the statuses of resources).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 7, claim 7 is similar to claim 1 and is therefore rejected in a similar manner.
As per claim 8, claim 8 is similar to claim 2 and is therefore rejected in a similar manner.
As per claim 10, claim 10 is similar to claim 4 and is therefore rejected in a similar manner.
As per claim 11, claim 11 is similar to claim 5 and is therefore rejected in a similar manner.
As per claim 12, claim 12 is similar to claim 6 and is therefore rejected in a similar manner.
As per claim 13, claim 13 is similar to claim 1 and is therefore rejected in a similar manner.
As per claim 14, claim 14 is similar to claim 2 and is therefore rejected in a similar manner.
As per claim 16, claim 16 is similar to claim 4 and is therefore rejected in a similar manner.
As per claim 17, claim 17 is similar to claim 5 and is therefore rejected in a similar manner.
As per claim 18, claim 18 is similar to claim 6 and is therefore rejected in a similar manner.
Claims 3, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2018/0176148 to Ku et al. in view of U.S. Patent No. 12,552,035 to Cella et al. as applied to claims 1, 7, and 13, and further in view of U.S. Patent No. 12,340,281 to Helwani et al.
As per claim 3, Ku et al. and Cella et al. in combination teaches the system of claim 1, see discussion of claim 1. Ku et al. teaches of the end time prediction result (see: paragraph [0057] where data is being received based on the usage. Also see: paragraphs [0003] and [0047] where there is dynamic allocation of resources based on the received data. This data would include the workload and the resource. The end result is a prediction of a distribution of characteristics of resource usage).
Cella et al. further teaches a-medical device as a resource (see: column 46, lines 24-54 where there is a medical resource as a resource).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Ku et al. and Cella et al. in combination may not further, specifically teach wherein:
1) --a different learned model is stored for each kind or each model number of the resource as the learned model; and
2) --the result is acquired using the corresponding learned model for each kind or each model number of the resource.
Helwani et al. teaches:
1) --a different learned model is stored for each kind or each model number of the resource as the learned model; (see: claim 15 where there is a different model stored for different devices) and
2) --the result is acquired using the corresponding learned model for each kind or each model number of the resource (see: claim 15 where there is selection of a model and a result being attained from that model).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) a different learned model is stored for each kind or each model number of the resource as the learned model and have 2) the result is acquired using the corresponding learned model for each kind or each model number of the resource as taught by Helwani et al. in the system as taught by Ku et al. and Cella et al. in combination with the motivation(s) of improving the quality and usability in real-time (see: column 11, lines 4-15 of Helwani et al.).
As per claim 9, claim 9 is similar to claim 3 and is therefore rejected in a similar manner.
As per claim 15, claim 15 is similar to claim 3 and is therefore rejected in a similar manner.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684