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
Claims 1 and 19-20 have been amended. Claims 2-18 and 23 have not been modified. Claims 21 and 22 have been cancelled. Claim 24 has been added. Claims 1-20 and 23-24 are pending and are provided to be examined upon their merits.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 2, 2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 2, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to Remarks filed on June 2, 2026 have been considered but are not fully persuasive. Response has been provided below.
Applicant argues 35 U.S.C. §112 Rejection, starting pg. 11 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the 112a rejection.
Applicant argues 35 U.S.C. §101 Rejection, starting pg. 11 of Remarks:
Regarding A, Applicant argues that the amended limitation of “altering lighting in the operating room” provides an improvement in the field of computer-assisted surgery, makes the claim more analogous to Example 25, and represents a specific, technical improvement to the surgical instrument.
Examiner respectfully disagrees. An improvement to the field of computer-assisted surgery, as described, is an abstract field of improving the surgical process, which is a human activity typically performed by surgeons or surgical auditors. As stated by Applicant remarks, the invention addresses a problem of “communicat[ing] protocol deviations to the surgical team immediately, reliably, and without increasing the cognitive burden on the surgeon”, which is an abstract idea monitoring a surgical process. Addressing cognitive burden of the surgeon is a problem rooted in the abstraction identified above and is not an issue rooted in the underlying technology. Providing an alert through lighting changes, as argued, provides an instruction to surgical staff that a violation has occurred.
Regarding increased visibility, the claims do not provide a specific, control mechanism to optimize the lighting conditions to be suitable for the “certain corrective actions that may demand increased visibility”. For example, dropping a scalpel during surgery may require the brightness of the entire room to be raised to help quickly find the dropped scalpel, but not above a threshold lumen intensity that may blind the staff. Another example may be, increasing brightness on a specific spot where one surgical staff member may have accidentally cut tissue they were not supposed to. Both of these examples require different control mechanisms and lighting conditions, but neither are fully supported by Applicant specification as [0030] only recites: “Optionally, the intervention comprises one or more of: altering the OR lighting, such as dimming the lights or setting the lights to higher brightness”.
Applicant further argues that surgery does not fall under any abstract idea. Examiner respectfully disagrees, as surgery is performed by surgeons on patients using human means. Thus, it falls under certain methods of organizing human activity. Furthermore, providing guidance to a surgeon is providing an instruction, which, although it is not an exclusive list, MPEP 2106.04(a)(2)IIC specifically is an abstract idea that falls under the Managing Personal Behavior or Relationships or Interactions Between People sub-grouping (“The sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions.”). Recitation of a computer to perform the abstract idea as a computer would perform the analysis with better efficiency than a human would (“providing computer guidance to a surgeon during a procedure enhances the precision and safety of the procedure beyond what could be achieved without computer-assisted analysis”) is insufficient to amount to a practical application under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”).
As stated previously, an improvement to the abstract idea does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Regarding Example 25, Examiner maintains that simply providing a notification encompasses an abstract idea of providing an instruction and no support exists for automatic control of lighting systems for optimizing the brightness of a room for specific corrective actions, and thus, does not provide a practical application as outlined by MPEP 2106.05.
Furthermore, under the broadest reasonable interpretation of the claim language, altering lighting may include an instruction provided to a surgical staff member to stop an ongoing action. For example, in the case that the surgical instrument is a non-electronic surgical instrument, such as a retractor or clamp, a staff member may be instructed to stop operating the instrument, which effectively blocks operation of the instrument. Such an interpretation, which is supported by Applicant specification ([0075], “If non-compliance is detected, the system may intelligently select one or more follow-up actions to take from a plurality of potential follow-up actions. The plurality of potential follow-up actions may include, but are not limited to: outputting an alert on a dashboard (e.g., in the operating room, in a control room), sending a message (e.g., an email, a text message), logging the non-compliance in a database, updating a report, recommending training and retraining, recommending protocol changes, performing downstream analytics, etc.”), falls under an abstract idea of certain methods of organizing human activity as managing personal behaviors or relationships or interactions between people by providing an instruction to stop an action.
Regarding B, Applicant argues that elevating the brightness addresses surgical process steps that may require brighter lighting in order to be performed. However, as noted above, the claims do not provide a specific, control mechanism to optimize the lighting conditions to be suitable for the “certain corrective actions that may demand increased visibility”. Additionally, increasing brightness may encompass instructing a person to increase the brightness of the room, which still falls under the abstract idea. Lastly, the brightness elevation is directed towards “improv[ing] surgical safety and effectiveness”, which is an improvement to the abstract idea of surgical monitoring and performance by surgical staff and not the underlying technology. As such, Examiner submits that claim 24 only serves to narrow the alteration of the lighting and does not provide a practical application as defined by MPEP 2106.05.
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 1-20 and 23-24 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1-20 and 23-24). Accordingly, claims 1-20 and 23-24 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis, and is similar to system claim 19 and product claim 20.
Claim 1:
A method for determining non-compliance to surgical protocols in an operating room, the method comprising:
receiving one or more images of the operating room captured by one or more cameras;
detecting, by a first set of one or more trained machine learning models, a surgical milestone of a plurality of surgical milestones associated with a surgery in the operating room based on the received one or more images, each of the plurality of surgical milestones associated with a respective surgical protocol, wherein detecting the surgical milestone comprises detecting one or more objects using an object detection algorithm and tracking the one or more objects using an object tracking algorithm;
selecting a second set of one or more trained machine learning models based on the detected surgical milestone, wherein each trained machine learning model in the second set of one or more trained machine learning models is trained to identify an instance of non- compliance to the surgical protocol associated with the detected surgical milestone;
detecting, by the selected second set of one or more trained machine learning models, one or more activities in the operating room based on the received one or more images;
determining, based on the detected one or more activities and the surgical protocol associated with the detected surgical milestone, that the instance of non-compliance to the surgical protocol has occurred in the operating room, wherein the detected one or more activities correspond to the instance of non-compliance with the surgical protocol associated with the detected surgical milestone and are acceptable for a different surgical milestone having a different surgical protocol; and
based on determining that the instance of non-compliance to the surgical protocol has occurred in the operating room, automatically altering lighting in the operation room.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards “detecting a surgical milestone associated with a surgery”, “detecting one or more activities in the operating room”, “determining,…, that an instance of non-compliance to the surgical protocol has occurred in the operating room”, and “based on determining that the instance of non-compliance to the surgical protocol has occurred in the operating room, automatically altering lighting”. All of these claim limitations are directed towards monitoring and managing the personal behaviors of the surgical staff and their compliance to established rules during surgery. As stated by Applicant in the Remarks provided on June 2, 2026, the lighting is simply a notification, which encompasses an instruction or teaching to the surgical staff that a non-compliance event has occurred.
Further regarding the lighting alteration, under the broadest reasonable interpretation of the claim language, altering lighting may include an instruction provided to a surgical staff member to stop an ongoing action. Such an interpretation, which is supported by Applicant specification ([0075], “If non-compliance is detected, the system may intelligently select one or more follow-up actions to take from a plurality of potential follow-up actions. The plurality of potential follow-up actions may include, but are not limited to: outputting an alert on a dashboard (e.g., in the operating room, in a control room), sending a message (e.g., an email, a text message), logging the non-compliance in a database, updating a report, recommending training and retraining, recommending protocol changes, performing downstream analytics, etc.”), falls under an abstract idea of certain methods of organizing human activity as managing personal behaviors or relationships or interactions between people by providing an instruction to perform an action.
Accordingly, the claim recites at least one abstract idea.
Claims 19 and 20 are abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
one or more cameras (1,19-20); first set of one or more trained machine-learning models (1,12,18-20); second set of one or more trained machine-learning models (1,14,19-20); lighting (1,19-20); one or more processors (19-20); memory (19); non-transitory computer-readable storage medium (20); electronic device (20); video (23)
Any computing devices and their associated components (processor, memory, storage medium, electronic device) that would be able to perform the method and the modules that are used within the computing environment are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0071] of Applicant specification recites: “This device may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, computer readable storage medium, such as, but not limited to, any type of disk, including floppy disks, USB flash drives, external hard drives, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.” No specific, technical improvements are being made to computing devices as generic devices are used with software to perform the abstract idea.
Machine learning is also taught at a high level of generality. [0105] recites: “The one or more trained machine-learning models used herein can comprise a trained neural network model, such as a 2D CNN, 3D-CNN, temporal DNN, etc. For example, the models may comprise ResNet50, AlexNet, Yolo, I3D ResNet 50, LSTM, MSTCN, etc... As examples, a number of exemplary models are described in G. Yengera et al., “Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks,” arXiv:1805.08569 [cs.CV], available at https://arxiv.org/abs/1805.08569.” No specific, technical improvements are being made to the field of machine learning as a variety of models are simply applied according to known methods to perform the abstract idea.
Lighting is also taught at a high level of generality. [0030] recites: “Optionally, the intervention comprises one or more of: altering the OR lighting, such as dimming the lights or setting the lights to higher brightness”. No specific, technical improvements are being made to lighting, as any generic surgical instrument is applied to perform an abstract idea of alerting surgical staff.
The cameras are also taught at a high level of generality. [0077] recites: “Multiple cameras can be placed in different locations in the operating room such as they can collectively capture a particular area or object of interest from different perspectives. The one or more cameras can include PTZ cameras. The one or more cameras can include a camera integrated into a surgical light in the operating room.” No specific, technical improvements are being made to cameras as they are only used to perform the insignificant extra-solution activity of collecting data.
The video is also taught at a high level of generality. [0110] recites: “The alert may optionally comprise a video or a textual description of the detected infraction and/or how severe it is.” No specific, technical improvements are being made to video technologies as they are only applied to perform the abstract idea of identifying detected infractions.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites the method further comprising: determining that a severity level of the instance of non-compliance to the surgical protocol meets a predefined severity threshold; in accordance with the determination that the determined severity level meets the predefined severity threshold: generating an alert; which teaches an abstract idea of certain methods of organizing human activity, as monitoring how poorly surgical staff are performing.
Claim 3: This claim recites the method further comprising: determining that a severity level of the instance of non-compliance to the surgical protocol does not meet a predefined severity threshold; in accordance with the determination that the severity level does not meet the predefined severity threshold: foregoing generating an alert; which teaches an abstract idea of certain methods of organizing human activity, as monitoring how poorly surgical staff are performing.
Claim 4: This claim recites the method further comprising: calculating an audit score for the surgery based on the instance of non-compliance to the surgical protocol; which teaches an abstract idea of certain methods of organizing human activity, as auditing.
Claim 5: This claim recites wherein the surgical milestone is a first surgical milestone of the surgery and the surgical protocol associated with the surgical milestone is a first surgical protocol, the method further comprising: determining that an instance of non-compliance to a second surgical protocol associated with a second surgical milestone has occurred in the operating room; and calculating the audit score for the surgery based on the instance of non-compliance to the first surgical protocol and the instance of non-compliance to the second surgical protocol; which teaches an abstract idea of certain methods of organizing human activity, as determining non-compliance and auditing.
Claim 6: This claim recites wherein the audit score is based on a weighted calculation of the instance of non-compliance to the first surgical protocol and the instance of non-compliance to the second surgical protocol; which only serves to further limit the abstract idea of the audit score.
Claim 7: This claim recites the method further comprising: comparing the audit score against a predefined audit score threshold associated with a type of the surgery in the operating room; which further limits the abstract idea of auditing.
Claim 8: This claim recites the method further comprising: identifying a change to the surgical protocol; and outputting a recommendation based on the identified change to the surgical protocol; which teaches an abstract idea of certain methods of organizing human activity, as identifying and recommending changes to a protocol/set of instructions.
Claim 9: This claim recites wherein identifying a change to the surgical protocol comprises: identifying a correlation between an outcome of the surgery in the operating room and the instance of non-compliance to the surgical protocol; which teaches an abstract idea of certain methods of organizing human activity, as identifying correlations between non-compliance and surgical outcomes.
Claim 10: This claim recites the method further comprising: recommending retraining of the surgical protocol based on the instance of non-compliance to the surgical protocol; which teaches an abstract idea of certain methods of organizing human activity, as teaching staff how to perform a protocol.
Claim 11: This claim recites the method further comprising: determining an identity or a surgical function of a person associated with the instance of non-compliance; and determining whether to recommend a change to the surgical protocol or to recommend retraining of the surgical protocol at least partially based on the identity or the surgical function of the person associated with the instance of non-compliance; which teaches an abstract idea of certain methods of organizing human activity, as identifying the person involved in non-compliance and providing a recommendation.
Claim 12: This claim recites wherein the first set of one or more trained machine-learning models is the same as or different from the second set of one or more trained machine-learning models; which only serves to further limit the trained machine learning models.
Claim 13: This claim recites wherein the one or more activities include: introduction of a surgical equipment; entrance of a person into the operating room; or exiting of the person out of the operating room; which only serves to further limit the activities.
Claim 14: This claim recites wherein the second set of one or more trained machine-learning models is configured to detect and/or track one or more objects in the operating room; which teaches the machine learning models at a high level of generality such that they are only applied to perform an abstract idea of detecting and tracking objects.
Claim 15: This claim recites wherein the one or more objects include: one or more surgical tables; one or more surgical lights; one or more cleaning supplies; one or more disinfectants; one or more linens; one or more surgical equipment; one or more patients; one or more medical staff members; attire of the one or more medical staff members; one or more doors in the operating room; one or more blood units; one or more surgical sponges; one or more surgical swabs; or any combination thereof; which only serves to further limit the objects.
Claim 16: This claim recites wherein the attire of the one or more medical staff members includes: a surgical mask, a surgical cap, a surgical glove, a surgical gown, or any combination thereof and wherein the one or more surgical equipment includes: one or more imaging devices, one or more monitoring devices, one or more surgical tools, or any combination thereof; which only serves to further limit the attire and surgical equipment.
Claim 17: This claim recites the method further comprising: calculating a ratio between medical staff members and patients in the operating room; which teaches an abstract idea of mathematical processes, as calculating a ratio.
Claim 18: This claim recites wherein detecting the surgical milestone comprises: obtaining, from the first set of one or more trained machine-learning models, one or more detected objects or events; and determining, based upon the one or more detected objects or events, the surgical milestone; which serves to further limit the abstract idea of determining the surgical milestone. This claim teaches the machine learning models at a high level of generality such that it is applied to perform an insignificant extra-solution activity of obtaining data.
Claim 23: This claim recites wherein further comprising displaying a video of the instance of non-compliance to the surgical protocol; which teaches video display at a high level of generality such that it is only applied to perform an insignificant extra-solution activity of outputting data.
Claim 24: This claim recites wherein altering lighting in the operating room comprises elevating a brightness of the lighting in the operating room; which only serves to narrow the altering of the lighting.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Examiner notes that terminating current operations of a surgical instrument in response to a non-compliant activity detected by machine learning models using imaging devices is known, as evidenced by:
Saptharishi (US 20240408370): [0248], “ In such an example, image capture system 702 (e.g., sensor system 206, etc.), management system 102, and/or central computing system 202 may automatically document the first use and any reuse of a medical device, alert a nurse (e.g., via user device 208, etc.) that the medical device is being improperly reused, and/or control a medical device (e.g., a valve, an infusion pump, etc.) to stop of flow of fluid through a fluid flow path associated with the reused medical device, which may improve patient safety and/or reduce costs associated with complications arising from reuse of medical devices.” [0185], “The one or more machine learning models may be trained to provide an output including event data associated with a prediction or classification of an event or activity associated with one or more medical devices 712 in response to input including image data and/or object data.”
Shelton (US 20240215800): [0619], “Further, in such an aspect, the imaging module 238 may analyze the snapshots themselves to detect evidence of an improper/insufficient sealing temperature (e.g., charring, oozing/bleeding). In one alternative aspect, the surgical hub 206 may communicate the snapshots to the cloud-based system 205, and a component of the cloud-based system 205 may perform the various imaging module functions described above to detect evidence of an improper/insufficient sealing temperature and to report the detection to the surgical hub 206. According to the various aspects described above, in response to the detected and/or identified failure event, the surgical hub 206 may download a program from the cloud-based system 205 for execution by the surgical device/instrument 235 that corrects the detected issue (i.e., program that alters surgical device/instrument parameters to prevent misfired staples, program that alters surgical device/instrument parameters to ensure correct sealing temperature).” [0885], “the situational awareness system includes a pattern recognition system, or machine learning system (e.g., an artificial neural network), that has been trained on training data to correlate various inputs (e.g., data from databases 5122, patient monitoring devices 5124, and/or modular devices 5102) to corresponding contextual information regarding a surgical procedure. In other words, a machine learning system can be trained to accurately derive contextual information regarding a surgical procedure from the provided inputs… the situational awareness system includes a further machine learning system, lookup table, or other such system, which generates or retrieves one or more control adjustments for one or more modular devices 5102 when provided the contextual information as input.”
Torabi (US 20230210579): [0077], “if after checking the real-time control signal against the real-time presence/absence decisions, process 400 identifies the unsafe event that involves a newly-generated tool absence decision, process 400 next determines if the confidence level associated with the newly-generated tool absence decision is above a high confidence level threshold (step 410). Note that a high confidence level for a tool absence decision generally means the tool is completely missing in the given endoscope image. If so, process 400 immediately disables the energy tool so that the tool can not fire (step 412)… the mechanical/tactile feedback can be implemented as an interlock design that requires two-stop activation. More specifically, the first stop of the interlock design is used for generating the activation pulse and triggering the detection of an unsafe event.” [0010], “The process simultaneously applies a machine-learning model to the real-time endoscope video images to generate real-time decisions on a location of the energy tool in the real-time endoscope video images.”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-18 and 23-24 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-18 and 23-24, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-18 and 23-24, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). 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.
Therefore, whether taken individually or as an ordered combination, claims 1-20 and 23-24 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID CHOI whose telephone number is (571)272-3931. The examiner can normally be reached M-Th: 8:30-5:30 ET.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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