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 .
Status of Claims
In the reply to the application filed 2026 June 08 the following changes have been made: amendments to claims 1 and 11. Claims 7 & 17 have been canceled.
Claims 1-6, 8-16, and 18-20 are currently pending and have been examined.
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 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a machine (claims 1-6 and 8-10) and a process (claims 11-16 and 18-20).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
a first sensor configured to capture hospital room data;
a second sensor configured to capture patient data;
data processing hardware communicatively coupled to the first sensor and the
second sensor; and
memory hardware in communication with the data processing hardware and storing
instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising:
receiving procedure data captured during a procedure by at least one of the
first sensor and the second sensor;
detecting a first plurality of events in the procedure data captured by the
first sensor;
detecting a second plurality of events in the procedure data captured by the
second sensor;
for each event of the first plurality of events and the second plurality of
events, associating the event with a respective timestamp of when the event occurred
during the procedure;
merging the first plurality of events and the second plurality of events by
synchronizing the first plurality of events and the second plurality of events based on the
respective timestamps of the events;
generating a machine readable report including the merged first plurality of
events and the second plurality of events;
generating, using a large language model configured to receive the machine
readable report as input, a human readable report; and
performing, during inference of the large language model, reinforcement learning
that fine-tunes the large language model based on post-operative data to improve surgical outcome recommendations for subsequent procedures.
Claim 11 recites similar limitations as claim 1.
These steps for generating a report of events, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. That is, nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people through organizing the activity around generating a report of events. This could be analogized to considering historical usage information while inputting data. If a claim limitation, under its broadest reasonable interpretation, covers performance as organizing human activity but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1 and 11, does not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as
recitation of a first sensor; a second sensor; data processing hardware communicatively coupled to the first sensor and the second sensor; memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations; by at least one of the first sensor and the second sensor; using a large language model; and, performing, during inference of the large language model, reinforcement learning that fine-tunes the large language model amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving procedure data captured during a procedure amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity; and, add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) 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, and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as recitation of a first sensor; a second sensor; data processing hardware communicatively coupled to the first sensor and the second sensor; memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations; and, by at least one of the first sensor and the second sensor, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f); using a large language model; and, performing, during inference of the large language model, reinforcement learning that fine-tunes the large language model, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank., MPEP 2106.05(f)
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving procedure data captured during a procedure, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
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 generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-6, 8-10, 12-16, and 18-20 reciting particular aspects for generating a report of events such as
[Claims 2 & 12] wherein the first sensor comprises one or more of: a camera; or a microphone;
[Claims 3 & 13] detecting a first event in image data captured by the camera; and
confirming detection of the first event by processing one of either (1) an image data captured by the second sensor and (2) audio data captured by the microphone that is synchronized with the first event;
[Claims 4 & 14] a camera;
a microphone; or
a surgical instrument configured to relay instrument data indicating one or more
settings of the surgical instrument to the data processing hardware;
[Claims 5 & 15] identifying a first event in the first plurality of events, the first event captured by a camera and including image data;
identifying a second event in the second plurality of events, the second event
captured by a microphone, including audio data, and having a respective timestamp that is
synchronized with a respective timestamp of the first event; and
determining that the first event and the second event conflict;
[Claims 6 & 16] wherein generating the machine readable report comprises
omitting the second event from the machine readable report;
[Claims 8 & 18] wherein the operations further comprise receiving patient
data and generating the machine readable report based on the received patient data;
[Claims 9 & 19] a hospital room model configured to receive the hospital room data captured by the first sensor and generate, as output, events detected in the hospital room data;
[Claims 10 & 20] a patient model configured to receive the patient data captured by the second sensor and generate, as output, events detected in the patient data;
these italicized portions are methods of organizing human activity since they merely describe types of data and determinations that can be performed by humans.
Step 2A Prong 2
Dependent claims 2-5, 8-10, 12-15, and 18-20 recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 2 & 12 (wherein the first sensor comprises one or more of: a camera; or a microphone); claims 3 & 13 (by the camera; by the second sensor; and, by the microphone); claims 4 & 14 (a camera; a microphone; or a surgical instrument configured to relay instrument data indicating one or more settings of the surgical instrument to the data processing hardware); claims 5 & 15 (by a camera; and, by a microphone); claims 9 & 19 (by the first sensor); and, claims 10 & 20 (by the second sensor) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); claims 8 & 18 (receiving patient data); claims 9 & 19 (receive the hospital room data); and, claims 10 & 20 (receive the patient data) amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2-5, 9-10, 12-15, and 19-20 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, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). Also, see [0050]-[0051] which discloses off-the-shelf networking devices and computer devices, [0054] which discloses off-the-shelf memory types, [0057] which discloses off-the-shelf display devices, and [0041] which discloses off-the-shelf LLMs. Dependent claims 8-10 & 18-20 recite additional subject matter which amounts to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Therefore, in consideration of all the facts, this is a textbook USC 101 where the present invention is clearly not patent-eligible under USC 101. Additionally, it is evident that the present claims monopolize the fundamental administrative tasks for healthcare, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied 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.
Claims 1-4, 8, 11-14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (US20240055088A1) in view of Wiest et al. (LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models).
Regarding claim 1, Wolf discloses a first sensor configured to capture hospital room data ([0085] “For instance, camera 121 may capture video/image data of a surgeon 131 performing the surgery. In some cases, cameras may capture video/image data associated with surgical team personnel, such as an anesthesiologist, nurses, surgical tech and the like located in operating room 101.”)
a second sensor configured to capture patient data ([0085] “some of the cameras (e.g., cameras 115, 123 and 125) may capture video/image data of operating table 141 (e.g., the cameras may capture the video/image data at a location 127 of a body of patient 143 on which a surgical procedure is performed)”)
data processing hardware communicatively coupled to the first sensor and the second sensor ([0097] “As shown, instrument 301 may include cameras 311A and 311B […] Additionally, device 301 may include a processor for compressing video/image data.”)
and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations ([0188] “In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform”)
receiving procedure data captured during a procedure by at least one of the first sensor and the second sensor ([0088] “In various embodiments, the camera control application may be configured to coordinate the position, focus, and magnification of various cameras during a surgical procedure.” [0194] “Surgical footage may refer to any video, group of video frames, or video footage including representations of a surgical procedure. For example, the surgical footage may include one or more video frames captured during a surgical operation. Accessing the surgical footage may include retrieving video from a storage location, such as a memory device.”)
detecting a first plurality of events in the procedure data captured by the first sensor ([0154] “a phase may refer to a particular period or stage of a process or series of events” [0189] “At step 810, process 800 may include analyzing the video footage using one or more of the VCA techniques described above, to identify an event location of a particular intraoperative surgical event within the surgical phase.” [0195] “The image sensors may include, for example, cameras […] 121 [..]located in operating room 101.”)
detecting a second plurality of events in the procedure data captured by the second sensor ([0154] “a phase may refer to a particular period or stage of a process or series of events” [0158] “identify the video footage location associated with at least one of the surgical event or the surgical phase” [0195] “The image sensors may include, for example, cameras 115 [...] and 123, and/or 125 located in operating room 101.”)
for each event of the first plurality of events and the second plurality of events, associating the event with a respective timestamp of when the event occurred during the procedure ([0189] “In another example, the event characteristic may include time related characteristics of the event (such as start time, end time, duration, etc.), and such time related characteristics may be calculated by analyzing the interval in the video footage corresponding to the event.”)
merging the first plurality of events and the second plurality of events by synchronizing the first plurality of events and the second plurality of events based on the respective timestamps of the events ([0733] “As previously described, the reference frames may be historical frames captured during historical surgical procedures. In an example embodiment, the video frames and the reference frames depicting the mandatory sequence of events may be synchronized by an event (herein also referred to as a starting event) that may be the same (or substantially similar) to a corresponding starting event of the mandatory (or recommended) sequence of events.” [0751] “overlaying on the at least one video outputted for display a surgical timeline”)
and generating a machine readable report including the merged first plurality of events and the second plurality of events ([0410] “Disclosed systems and methods may involve analyzing surgical footage to identify features of surgery, patient conditions, and surgical intraoperative events to obtain information for populating the postoperative report. A postoperative report may be populated by analyzing surgical data obtained from a surgical procedure to identify features of surgery, patient conditions, and surgical intraoperative event and extracting information from the analyzed data for populating the postoperative report.”)
and performing, during inference of the large language model, reinforcement learning that fine-tunes the large language model based on post-operative data to improve surgical outcome recommendations for subsequent procedures ([0080] “In some examples, a trained machine learning algorithm may be used as an inference model that when provided with an input generates an inferred output.” [0722] “For example, an online machine learning algorithm and/or a reinforcement machine learning algorithm may be used to update the machine learning model based on the received information. […] the received postoperative information may be used to determine the realized surgical outcome.”)
Wolf does not explicitly disclose however Wiest teaches generating, using a large language model configured to receive the machine readable report as input, a human readable report ([pg. 10] “To accommodate documents in various formats (TXT, PDF, or CSV), our protocol standardizes the data into a uniform format (CSV) through automatic conversion and compilation.” [pg. 13] “If prompt, grammar and hyperparameters are correctly defined and the preprocessed file is uploaded, clicking the button “Run LLM Processing” initiates the process. […] After the process is finished, the user can download and store the processed zip file. This file contains all original reports as PDFs, the preprocessed CSV as well as an output CSV that contains all LLM answers as well as meta-information including prompt and hyperparameter settings.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the in the report generation techniques of Wolf generating, using a large language model configured to receive the machine readable report as input, a human readable report as taught by Wiest since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 2, Wolf discloses wherein the first sensor comprises one or more of: a camera; or a microphone ([0085] “For example, room 101 may include one or more microphones (e.g., audio sensor 111, as shown in FIG. 1), several cameras (e.g., overhead cameras 115, 121, and 123, and a tableside camera 125) for capturing video/image data during surgery.”)
Regarding claim 3, Wolf discloses wherein detecting the first plurality of events in the procedure data captured by the first sensor comprises: detecting a first event in image data captured by the camera ([0161] “Embodiments of the present disclosure may further include analyzing the video footage to identify an event location of a particular intraoperative surgical event within the surgical phase.” [0167] “Video footage 620 may also be associated with event tag 624, which may identify an intraoperative surgical event (in this instance an incision) within the surgical phase occurring at event location 623.”)
and confirming detection of the first event by processing one of either (1) an image data captured by the second sensor and (2) audio data captured by the microphone that is synchronized with the first event. ([0170] “For example, displaying may include sequentially presenting frames associated with the video footage and may further include presenting audio associated with the video footage.”)
Regarding claim 4, Wolf discloses wherein the second sensor comprises one or more of: a camera; a microphone; or a surgical instrument configured to relay instrument data indicating one or more settings of the surgical instrument to the data processing hardware ([0085] “some of the cameras (e.g., cameras 115, 123 and 125)”)
Regarding claim 8, Wolf discloses wherein the operations further comprise receiving patient data and generating the machine readable report based on the received patient data ([0458] “For example, a first portion of a post-operative report may include a first set of fields that may be populated by derived image-based information captured during a first portion of the surgical procedure” [0712] “The postoperative surgical report may include any suitable information related to the surgical procedure. For example, the report may include a name of the surgical procedure, patient characteristics, as discussed above, patient's medical history, including the medical report for the patient”)
Regarding claim 11, Wolf discloses when executed on data processing hardware, causes the data processing hardware to perform operations ([0188] “In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform”)
receiving procedure data captured during a procedure by at least one of a first sensor and a second sensor ([0088] “In various embodiments, the camera control application may be configured to coordinate the position, focus, and magnification of various cameras during a surgical procedure.” [0194] “Surgical footage may refer to any video, group of video frames, or video footage including representations of a surgical procedure. For example, the surgical footage may include one or more video frames captured during a surgical operation. Accessing the surgical footage may include retrieving video from a storage location, such as a memory device.”)
the first sensor configured to capture hospital room data ([0085] “For instance, camera 121 may capture video/image data of a surgeon 131 performing the surgery. In some cases, cameras may capture video/image data associated with surgical team personnel, such as an anesthesiologist, nurses, surgical tech and the like located in operating room 101.”)
and the second sensor configured to capture patient data ([0085] “some of the cameras (e.g., cameras 115, 123 and 125) may capture video/image data of operating table 141 (e.g., the cameras may capture the video/image data at a location 127 of a body of patient 143 on which a surgical procedure is performed)”)
each of the first sensor and the second sensor communicatively coupled to the data processing hardware ([0097] “As shown, instrument 301 may include cameras 311A and 311B […] Additionally, device 301 may include a processor for compressing video/image data.”)
detecting a first plurality of events in the procedure data captured by the first sensor ([0154] “a phase may refer to a particular period or stage of a process or series of events” [0189] “At step 810, process 800 may include analyzing the video footage using one or more of the VCA techniques described above, to identify an event location of a particular intraoperative surgical event within the surgical phase.” [0195] “The image sensors may include, for example, cameras […] 121 [..]located in operating room 101.”)
detecting a second plurality of events in the procedure data captured by the second sensor ([0154] “a phase may refer to a particular period or stage of a process or series of events” [0158] “identify the video footage location associated with at least one of the surgical event or the surgical phase” [0195] “The image sensors may include, for example, cameras 115 [...] and 123, and/or 125 located in operating room 101.”)
for each event of the first plurality of events and the second plurality of events, associating the event with a respective timestamp of when the event occurred during the procedure ([0189] “In another example, the event characteristic may include time related characteristics of the event (such as start time, end time, duration, etc.), and such time related characteristics may be calculated by analyzing the interval in the video footage corresponding to the event.”)
merging the first plurality of events and the second plurality of events by synchronizing the first plurality of events and the second plurality of events based on the respective timestamps of the events ([0733] “As previously described, the reference frames may be historical frames captured during historical surgical procedures. In an example embodiment, the video frames and the reference frames depicting the mandatory sequence of events may be synchronized by an event (herein also referred to as a starting event) that may be the same (or substantially similar) to a corresponding starting event of the mandatory (or recommended) sequence of events.” [0751] “overlaying on the at least one video outputted for display a surgical timeline”)
and generating a machine readable report including the merged first plurality of events and the second plurality of events ([0410] “Disclosed systems and methods may involve analyzing surgical footage to identify features of surgery, patient conditions, and surgical intraoperative events to obtain information for populating the postoperative report. A postoperative report may be populated by analyzing surgical data obtained from a surgical procedure to identify features of surgery, patient conditions, and surgical intraoperative event and extracting information from the analyzed data for populating the postoperative report.”)
and performing, during inference of the large language model, reinforcement learning that fine-tunes the large language model based on post-operative data to improve surgical outcome recommendations for subsequent procedures ([0080] “In some examples, a trained machine learning algorithm may be used as an inference model that when provided with an input generates an inferred output.” [0722] “For example, an online machine learning algorithm and/or a reinforcement machine learning algorithm may be used to update the machine learning model based on the received information. […] the received postoperative information may be used to determine the realized surgical outcome.”)
Wolf does not explicitly disclose however Wiest teaches generating, using a large language model configured to receive the machine readable report as input, a human readable report ([pg. 10] “To accommodate documents in various formats (TXT, PDF, or CSV), our protocol standardizes the data into a uniform format (CSV) through automatic conversion and compilation.” [pg. 13] “If prompt, grammar and hyperparameters are correctly defined and the preprocessed file is uploaded, clicking the button “Run LLM Processing” initiates the process. […] After the process is finished, the user can download and store the processed zip file. This file contains all original reports as PDFs, the preprocessed CSV as well as an output CSV that contains all LLM answers as well as meta-information including prompt and hyperparameter settings.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the in the report generation techniques of Wolf generating, using a large language model configured to receive the machine readable report as input, a human readable report as taught by Wiest since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 12, the limitations are rejected for the same reasons as stated above for claim 2.
Regarding claim 13, the limitations are rejected for the same reasons as stated above for claim 3.
Regarding claim 14, the limitations are rejected for the same reasons as stated above for claim 4.
Regarding claim 18, the limitations are rejected for the same reasons as stated above for claim 8.
In Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (US20240055088A1) in view of Wiest et al. (LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models) and further in view of Parikh et al. (US20190034475A1).
Regarding claim 5, Wolf discloses identifying a first event in the first plurality of events, the first event captured by a camera and including image data ([0085] “For instance, camera 121 may capture video/image data of a surgeon 131 performing the surgery.” [0161] “Embodiments of the present disclosure may further include analyzing the video footage to identify an event location of a particular intraoperative surgical event within the surgical phase.”)
identifying a second event in the second plurality of events, the second event captured by a microphone, including audio data ([0326] “audio data may be captured during the surgical procedure”)
and having a respective timestamp that is synchronized with a respective timestamp of the first event ([0468] “the time marker may identify an end of the identified phase, as discussed above. The transmitted data may include […] audio data” [0733] “After correlating a surgical event with corresponding reference events of the mandatory sequence, a frame depicting the start of the surgical event may be synchronized with a reference frame depicting the start of the corresponding mandatory event.”)
Wolf in view of Wiest does not explicitly disclose however Parikh teaches and determining that the first event and the second event conflict ([0017] “Each of the data sets 112-112-N ingested in the pipeline 100 may be related to at least one of a number of adverse events 113-113N.” [0018] “determining duplicate records within AE reporting data”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the medical communications techniques of Wolf and text conversion techniques of Wiest determining that the first event and the second event conflict as taught by Parikh since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 6, Wolf does not explicitly disclose however Parikh teaches wherein generating the machine readable report comprises omitting the second event from the machine readable report ([0014] “the identified duplicates can be de-duplicated or otherwise deleted”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the medical communications techniques of Wolf and text conversion techniques of Wiest determining that the first event and the second event conflict as taught by Parikh since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 15, the limitations are rejected for the same reasons as stated above for claim 5.
Regarding claim 16, the limitations are rejected for the same reasons as stated above for claim 6.
Claims 9-10 & 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (US20240055088A1) in view of Wiest et al. (LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models) Sivertsen et al. (US20200289033A1).
Regarding claim 9, Wolf in view of Wiest does not explicitly disclose however Sivertsen teaches a hospital room model configured to receive the hospital room data captured by the first sensor and generate, as output, events detected in the hospital room data ([0166] “The flexibility of the present invention with respect to selection of sensor types, inclusion of manually entered information, as well as inclusion of data from third party sensors (e.g. wearables) and even external data such as weather, humidity, time of day, time light has been on in a room, time television has been on in a room, etc. enables training of the pattern recognition model (or models) to a wide range of conditions and events that it has not previously been possible to monitor and detect.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the medical communications techniques of Wolf and text conversion techniques of Wiest a hospital room model configured to receive the hospital room data captured by the first sensor and generate, as output, events detected in the hospital room data as taught by Sivertsen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 10, Wolf in view of Wiest does not explicitly disclose however Sivertsen teaches a patient model configured to receive the patient data captured by the second sensor and generate, as output, events detected in the patient data ([0141] “The invention enables the construction of personalized models using advanced machine learning that learns the health patterns of the patient and is thus able to more accurately detect lowered health state than using one-fits-all algorithms. The models enable detection of patterns in the sensor data input that enable prediction or detection of events.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the medical communications techniques of Wolf and text conversion techniques of Wiest a patient model configured to receive the patient data captured by the second sensor and generate, as output, events detected in the patient data as taught by Sivertsen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 19, the limitations are rejected for the same reasons as stated above for claim 9.
Regarding claim 20, the limitations are rejected for the same reasons as stated above for claim 10.
Response to Arguments
Applicant’s arguments filed on 2026 June 08 have been considered but are not fully persuasive.
Regarding the USC 101 rejection, applicant argues on pages 7 to 8 that claims 1 and 11 have been amended to add a generating step that incorporates and LLM which makes the claims not directed to the abstract idea. Applicant states that the claims recite specific technical operations especially with the performing step which is not an activity that can be performed through human organization or mental processes. Paragraph [0041] is cited as support for the reinforcement learning with the assertion that there is an improvement to the machine learning system. Specifically, that the claimed reinforcement learning provides a concrete improvement to surgical outcome recommendations, which is a practical application in the medical field. For Step 2B, applicant argues that the claim limitations amounts to significantly more than any alleged abstract idea where ordered combination provides a specific technical solution for automatically generating and improving surgical reports. Applicant concludes that amended claims 1 & 11 are eligible for patenting.
Examiner disagrees with the applicant’s arguments. Applicant’s claims are clearly directed to an abstract idea. Detecting events, organizing items by timestamps, and merging data streams are methods of organizing human activity are administrative tasks that people have done manually for centuries. The MPEP makes it clear that claims can recite an abstract idea even if they are claimed as being performed on a computer. The courts have also found claims requiring a generic computer or nominally reciting a generic computer may still recite abstract idea even though the claim limitations are not performed entirely by a human. The limitations identified as abstract in the present application are very outcome-based or result-focused and don’t give much technical detail that goes beyond what a human can do. As can be seen, there is nothing in the claim that tells the examiner how the asserted reinforcement learning is being performed beyond what a human can do. Examiner points out the applicant didn’t invent the concept of an LLM or even the concept of reinforcement learning to now generically claim it and then call it non-abstract. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to ‘implement[ing] the abstract idea of intermediated settlement on a generic computer’, it cannot save OIP's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). Even if the claims nominally recite computer components that are rooted in technology, there is no recitation of how the computer components are specifically programmed to distinguish from generic computer processes
Examiner asserts the present amendments do not do much to advance prosecution because the present specification provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The MPEP provides that improvements to the functioning of a computer or to any other technology or technical field can signal eligibility, see MPEP 2106.05(a), and provides examples of improvements to computer functionality, MPEP 2106.05(a)(I), and improvements to any other technology of technical field, MPEP 2106.05(a)(I). “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool”. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather to an improvement to computer functionality. Id. It was the specification' s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general-purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts. Rather than improving a computer's algorithm (i.e., solving a technically based problem), the claimed invention purports to solve the non-technological problems manual report generation and human error ([0002] of specification) by using computers to automate generating a report of events. In other words, one of the main/glaring issues with the present invention is that the problems solved by the applicant are not technological problems. Examiner is confused by the applicant’s arguments because the applicant seems to be asserting two improvements: 1) an improvement to the machine learning system and 2) a concrete improvement to surgical outcome recommendations. Examiner asserts that there is zero improvement to technology. The claim describes the desired result, better surgical recommendations, rather than a new technological way to achieve it. The hardware is used in its normal, expected way to store and run code, rather than solving a unique technical problem inside the computer or sensor network itself. Applying a standard large language model (LLM) and reinforcement learning to a new field (surgery) is treated as a routine application of existing tools, not a technological fix. All the applicant is doing is applying known technology for their intended benefit(s) to a new data environment and calling it an improvement (see Customedia Techs., LLC v. Dish Network Corp., Case No.18-2239 (Fed. Cir. Mar. 6, 2020). Furthermore, applicant has clarified the record that their improvement is to the abstract idea with their assertion of “improvement to surgical outcome recommendations.”
The examiner asserts the following facts which the applicant will not be able to dispute:
1) the invention does NOT involve a novel algorithm or data structure that significantly improves the computer's functionality,
2) the invention does NOT involve a new hardware component or configuration that works with the computer to achieve a specific technical benefit, and
3) the computer is NOT used in a completely new way demonstrating a significant technical advancement.
It is evident from the specification and claims that the applicant is not improving computer technology, and instead providing an improvement to the abstract idea. An improvement to the abstract idea is not an improvement to computer technology. Thus, examiner does not see how the present claims improve the functioning of a computer or provide improvements to any other technology or technical field. The claimed invention appears similar to the example of improvements that are insufficient to show an improvement in computer-functionality such as arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). See MPEP 2106.05(a)(I)(viii). The broad claims are lacking concrete limitations to integrate the abstract idea into a practical application. Examiner points out that the claimed limitations have no indication in the specification that the operations recited invoke any inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (fed Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Most importantly, in DDR Holdings & unlike the present claims, the claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113 USPQ2d at 1106. The examiner also points out that there is no indication in the specification that the claimed invention affects a transformation or reduction of a particular article to a different state or thing. Examiner points to the recitation of large language model and reinforcement learning in the claim(s) as generic. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice Corp. v. CLS Banklnt'l, 573 U.S. 208 223 (2014). Applicant does not and cannot contend they invented the concept of large language model and reinforcement learning, nor does the specification disclose any new large language model and reinforcement learning technique. The alleged improvement of using large language model and reinforcement learning lies in the abstract idea itself, not to any technological improvement nor to any improvement to the functioning of a computer. See BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88 (Fed. Cir. 2018). The fact pattern of the applicant’s claims is congruent to the Recentive Analytics, Inc. v. Fox Corp., 2025 U.S.P.Q.2d 628 (Fed. Cir. 2025) decision by the Federal Circuit. Just like in Recentive, the present claims do not delineate steps through which the large language model and reinforcement learning achieves an improvement. See, e.g., IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that "d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way," because "[s]uch functional claim language, without more, is insufficient for patentability under our law." (quoting Two-Way Media Ltd v. Comcast Cable Commc'ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017))); see also Intell. Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (similar); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016) (similar). Claiming a mere concept or functional result without disclosing the implementation details does not overcome USC 101. Applying an established technique to a new field or data set is insufficient for patent eligibility. To show an involvement of a computer assists in improving technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.05(a)(II)). In Finjan, Inc. v. Blue Coat Systems the courts found that the claims were “directed to a non-abstract improvement in computer functionality…” (MPEP 2106.04(d)). The present invention clearly does not meet the condition set forth by the courts and thus is not integrated into a practical application.
An analysis was performed under Step 2B, with court case citations, which didn’t result in the claim being eligible under USC 101. In comparison to Bascom, examiner points out that Bascom is not similar to the present application because Bascom claimed a technical improvement in the art i.e., a technology-based solution to filter content on the internet while the present application is not presenting an improvement (as indicated above). There is nothing specialized about using off-the-shelf computers, large language model, and reinforcement learning on new data. The use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). The applicant has not demonstrated that their invention is inventive. There is no justification to withdraw the USC 101. Therefore, the USC 101 rejection is strongly maintained.
Regarding the USC 102 rejection, applicant argues that Wolf does not disclose the merging limitation and that the synchronization performed in Wolf is for comparison purposes and not merging separately detected events. Along with citing [0036], applicant emphasizes the where merging refers to generating an ordered list based on the respective timestamps in chronological order. Applicant explains that the merging operation in the claim involves the synchronizer module identifying two or more detected events that are synchronized and determine whether events correlate or conflict which Wolf does not disclose. Applicant also asserts that Wolf does not disclose the newly added limitations. Applicant requests that the USC 102 rejection be withdrawn.
Examiner disagrees with the applicant’s arguments. Examiner asserts that Wolf is a much narrower interpretation than the applicant’s generic claims. The video frames and reference frames in Wolf are labels for separately detected events. Otherwise, it doesn’t make sense why Wolf also performs the asserted synchronization (that the applicant argued Wolf does not perform). In response to applicant's arguments that Wolf fails to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e.,
merging refers to generating an ordered list based on the respective timestamps in chronological order) is not recited in the rejected claims 1 and 11. Although the claims are interpreted in light of the specification, limitations from the specification [0036] are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The broadest reasonable interpretation of the claim language of “merging,” would not be limited to specifically generating an ordered list based on the respective timestamps in chronological order. Applicant also argues limitations are non-existent in claims 1 and 11. That is, neither claims 1 nor 11 disclose identifying two or more detected events that are synchronized (i.e., have a same timestamp) and determining whether the events correlate or conflict. New paragraphs have been cited from Wolf to teach the “performing” limitation. Applicant’s arguments in regards to the “generating” limitation has been considered but is moot since they do not apply to the newly cited reference: Wiest. Therefore, the USC 102 rejection has been withdrawn.
Regarding the USC 103 rejection, applicant argues for claims 5 and 15 that Parikh's system identifies redundant records of the same adverse event from different data source which is different from "determining that the first event and the second event conflict" as recited by claims 5 and 15. Applicant cites [0039] of the specification to explain the interpretation that the claimed conflict detection involves substantive conflicts between different types of sensor data (image data versus audio data) captured at the same timestamp during a procedure, where the image data shows one thing (e.g., surgical instrument not activated) and the audio data indicates something contradictory (e.g., a burning sound). Applicant also asserts that claims 6 and 16 are allowable for their dependency on claim 5.
Examiner disagrees with the applicant’s arguments. Examiner asserts that there seems to be a BRI mismatch between the applicant and the examiner as the citations Parikh appropriately teach the claim limitations. Under BRI, "determining that the first event and the second event conflict" can broadly encompass both logical inconsistencies (non-duplicate events happening at the same time that cannot coexist) and redundancies (duplicates). A BRI interpretation treats redundant simultaneous entries as a functional conflict within data-filtering logic. It is not clear what is contextually meant by conflict in the claim. In response to applicant's arguments that Parikh fails to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e.,
where the image data shows one thing (e.g., surgical instrument not activated) and the audio data indicates something contradictory (e.g., a burning sound)) is not recited in the rejected claims 5 and 15. Although the claims are interpreted in light of the specification, limitations from the specification [0039] are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The broadest reasonable interpretation of the claim language of “merging,” would not be limited to specifically generating an ordered list based on the respective timestamps in chronological order. Claims 6 and 16 are still rejected under USC 103. Applicant’s arguments for the 7, 9-10, 17, and 19-20 have been considered but is moot since they do not apply to the newly cited reference: Wiest. Therefore, the USC 103 rejection is maintained.
Prior Art Cited but Not Relied Upon
Cabello-Collado, C., Rodriguez-Juan, J., Ortiz-Perez, D., Garcia-Rodriguez, J., Tomás, D., & Vizcaya-Moreno, M. F. (2024). Automated generation of clinical reports using sensing technologies with deep learning techniques. Sensors, 24(9), 2751.
This reference is relevant because it discloses the applicant’s invention of automated report generation leveraging sensing and deep learning technologies.
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.
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/WINSTON R FURTADO/Primary Examiner, Art Unit 3687
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