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 .
Response to Amendment
In light of the amendments, the previous 35 U.S.C. 101 rejections have been withdrawn.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 05/08/2026, the following has occurred: claims 1, 7, and 13 have been amended; claims 2-6, 8-12, and 14-18 remain unchanged; and no new claims have been added.
Claims 1-18 are pending.
Effective Filing Date: 07/05/2022
Response to Arguments
35 U.S.C. 101 Rejections:
Examiner withdraws the previous 101 rejections in view of the claim amendments.
35 U.S.C. 103 Rejections:
Applicant argues with respect to the previous 103 rejections. The rejections have been adjusted to account for the amendments to the claims. The arguments are deemed moot in view of the newly-cited art and rejections.
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, 6-8, 12-14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0072750 to LEE in view of U.S. 2018/0176148 to Ku et al. and further in view of U.S. 2010/0241751 to Sonoda et al.
As per claim 1, LEE teaches 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 comprising:
--a host management system (see: 100 of FIG. 2 where there is such a system) including:
--a storage unit, (see: 170 of FIG. 2 where there is a memory)
--one or more processors; (see: 180 of FIG. 2 where there is a processor) and
--a communication unit; (see: paragraphs [0043] and [0056] where there is a communication unit) and
--a mobile robot, (see: 100 of FIG. 1 where there is a mobile robot) the mobile robot being capable of:
--autonomously traveling to transport a medical device within a medical welfare facility, (see: paragraph [0085] where the robot can travel and transport articles to a medical facility. Also see: paragraph [0034] where the robot can be classified in the medical field of use) that is configured to receive a conveyance request for the medical device from a user, (see: paragraph [0121] where there is a service/conveyance request with destination information. This is a request for an article from a user) and
--conveying the medical device to a destination included in the conveyance request; (see: paragraphs [0121] and [0132] where there is conveyance of the article (medical device) to a destination included in the request)
--wherein:
--a learned model that has undergone machine learning as a model; (see: paragraph [0037] where there is a model that has undergone machine learning)
--medical device as a resource; (see: paragraph [0034] where the robot can be classified in the medical field of use. Also see: paragraph [0085] where there is a transportation of articles (resources). An article where the field of use is the medical field is the medical device)
--the one or more processors are programmed to:
--generate route planning information for the mobile robot based on the conveyance request information received from the medical device lending system in response to the notification; (see: paragraph [0131] where there is a route guide service which is used to create a route (generation of route planning information). This is based on the service/conveyance request) and
--generate a control signal based on the generated route planning information; (see: paragraph [0055] where there is generation of a control signal based on route mapping) and
--transmit the control signal to the mobile robot, the control signal including information on passing points and a destination; (see: paragraphs [0082] and [0121] where there is transmission of control signals to the robot including route or destination information) and
--the mobile robot receives the transmitted control signal and, based on the control signal, autonomously moves so as to sequentially pass through the passing points toward the destination (see: paragraphs [0130], [0133], and [0148] where there is reception of a transmitted control signal and autonomous movement of the robot on the route).
Ku et al. may not further, specifically teach
1) --the storage unit 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, using learning data including lending record data indicating a lending record that is a record of the resource that has been lent, the lending record including a record indicating that the use of the resource is ended, and the electronic chart data describing information indicating the necessity of the use of the resource that has been lent;
2) --the one or more processors are programmed to:
2a) --predict an end time by inputting 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;
2b) --control the communication unit to notify the medical device lending system of the acquired end time prediction result.
Ku et al. teaches:
1) --the storage unit 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)
2) --the one or more processors are programmed to:
2a) --predict an end time by inputting 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).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) the storage unit store 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, using learning data including lending record data indicating a lending record that is a record of the resource that has been lent, the lending record including a record indicating that the use of the resource is ended, and the electronic chart data describing information indicating the necessity of the use of the resource that has been lent and 2) the one or more processors are programmed to: 2a) predict an end time by inputting 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 as taught by Ku et al. in the system as taught by LEE with the motivation(s) of improving performance with respect to a task (see: paragraph [0045] of LEE).
Sonoda et al. teaches:
2) --the one or more processors are programmed to:
2b) --control the communication unit to notify the medical device lending system of the acquired end time prediction result (see: paragraph [0160] where there is notification of an end time).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 2) the one or more processors are programmed to: 2b) control the communication unit to notify the medical device lending system of the acquired end time prediction result as taught by Sonoda et al. in the system as taught by LEE and Ku et al. in combination with the motivation(s) of efficiently using the resources (see: paragraph [0148] of Sonoda et al.).
As per claim 2, LEE, Ku et al., and Sonoda et al. in combination teaches the system of claim 1, see discussion of claim 1. LEE teaches a medical device as a resource (see: paragraph [0034] where the robot can be classified in the medical field of use. Also see: paragraph [0085] where there is a transportation of articles (resources). An article where the field of use is the medical field is the medical device).
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).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 6, LEE, Ku et al., and Sonoda et al. in combination teaches the method of claim 1, see discussion of claim 1. LEE teaches a medical device as a resource (see: paragraph [0034] where the robot can be classified in the medical field of use. Also see: paragraph [0085] where there is a transportation of articles (resources). An article where the field of use is the medical field is 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 (see: paragraph [0101] where there is a lending history in the form of service use history data)
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).
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 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 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. 2021/0072750 to LEE in view of U.S. 2018/0176148 to Ku et al. and further in view of U.S. 2010/0241751 to Sonoda 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, LEE, Ku et al., and Sonoda et al. in combination teaches the system of claim 1, see discussion of claim 1. LEE teaches a medical device as a resource (see: paragraph [0034] where the robot can be classified in the medical field of use. Also see: paragraph [0085] where there is a transportation of articles (resources). An article where the field of use is the medical field is the medical device).
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).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
LEE, Ku et al., and Sonoda 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 LEE, Ku et al., and Sonoda 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.
Claims 4-5, 10-11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0072750 to LEE in view of U.S. 2018/0176148 to Ku et al. and further in view of U.S. 2010/0241751 to Sonoda et al.as applied to claims 1, 7, and 13, and further in view of U.S. Patent No. 12,552,035 to Cella et al.
As per claim 4, LEE, Ku et al., and Sonoda et al. in combination teaches the system of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the lending record data and the lending device data, or the electronic chart data include information indicating a transporter.
Cella et al. 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).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the lending record data and the lending device data, or the electronic chart data include information indicating a transporter as taught by Cella et al. in the system of LEE, Ku et al., and Sonoda et al. in combination since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case the combination of LEE, Ku et al., and Sonoda et al. teaches of using information and adding more information to that information would maintain the same functionality of the combination of LEE, Ku et al., and Sonoda et al., making the results predictable to one of ordinary skill in the art (MPEP 2143).
As per claim 5, LEE, Ku et al., Sonoda 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 4, and incorporated herein.
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 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
Prosecution Insights