DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Claims 1-12 are allowable over the prior art. However, the claims remain rejected under 35 USC §§101 and 112(b).
Reasons For Allowance
The cited references do not disclose the receiver operating characteristic curve acquisition module is configured to obtain a receiver operating characteristic curve corresponding to any first reference group based on classification sensitivities and classification specificities corresponding to a plurality of second reference groups amongst any first reference group, and the target question semantic meaning acquisition module is configured to determine a target question semantic meaning from the candidate question semantic meaning according to a distance between a coordinate point corresponding to any second reference group amongst the plurality of second reference groups and a perfect classification coordinate point in a receiver operating characteristic curve corresponding to the target first reference group; wherein the perfect classification coordinate point is a coordinate point with abscissa of 0 and ordinate of 1 in the receiver operating characteristic curve, and the target question semantic meaning is used to help the operator to conduct a next round of question.
The cited references do not disclose wherein the entity attribute cluster and entity attribute pair determining module is configured to determine an entity attribute cluster currently corresponding to the person to be rescued and an entity attribute pair currently corresponding to the person to be rescued according to a question from the operator and an answer to the question from a help-seeker in any round of dialogue between the operator and the help-seeker; wherein the entity attribute cluster comprises a plurality of entity attribute pairs corresponding to a same core question, each entity attribute pair comprise an entity and an attribute, the entity represents a semantic meaning of a question from the operator in a historical call for help, and the attribute represents a semantic meaning of an answer to the question from the help-seeker, and the first reference group and second reference group determining module is configured to acquire a first reference group corresponding to the entity attribute cluster and a second reference group corresponding to the entity attribute pair from an initial reference group; wherein the first reference group is obtained by classifying a plurality of historical people to be rescued amongst the initial reference group according to the entity attribute cluster, and the second reference group is obtained by classifying the first reference group according to the entity attribute.
Specification
The disclosure is objected to because of the following exemplary informalities: paragraph [0020] contains the following grammatical error: “…classifying a plurality of historical respondent[s] …”. I.e., plurality of “respondent” should be plural. Applicant is respectfully reminded to review the specification/abstract/claims/drawings for all informalities, and correct them.
Appropriate correction is required.
Claim Rejections – 35 U.S.C. § 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 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 1:
At step 1: claim 1 is directed to a “system” and thus directed to a statutory category.
At step 2a prong 1: claim 7 recites limitations that are directed to an abstract idea:
“selecting data from a collection of data, the data having a particular composition; “classify” data based upon a probability and probability threshold; “determine” a meaning of “reference group” data / questions in accordance with a “received” “characteristic curve” trend metric; “selecting” the most likely meaning of the intended question based upon a distance calculation that may be performed by a human using pencil and paper. In the alternative, this last limitation may be reasonably characterized as an abstract Mathematical Concept.
At step 2a prong 2: Claim 1 recites the following additional elements: a first reference group acquisition module, a classification test module, a receiver operating characteristic curve acquisition module, a candidate question semantic meaning acquisition module and a target question semantic meaning acquisition module. Such claim language is a high-level recitation of a generic computer components (i.e., “modules”) and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. It is noted that the recited “acquiring language” merely involves a determination step, not a reception of data.
Claim 1 also recites the “receiving” of a “characteristic curve” reference data that corresponds to a grouping of data. Such a limitation represents insignificant extra-solution activity above, and when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. 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); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Viewing the additional limitations together and the claims as a whole, nothing provides integration into a practical application.
At step 2b: the conclusions for the additional elements representing mere implementation using a computer (i.e., generic computing elements) are carried over and do not provide significantly more.
Therefore, the claim taken as a whole does not change this conclusion, and the claim is ineligible.
Claims 2-6 depend upon claim 1, and do not correct the issues set forth above.
Claims 2-5 could be characterized as mental processes, wherein data may be manipulated via a pencil and paper, or alternatively as mathematical concepts. For instance, claim 2 recites classification of two types of data based upon threshold or cut-off values. Claim 3 recites setting / classifying data and connecting or graphing such data. Claim 4 recites a calculation and subsequent setting / classifying reference group data. Claim 5 recites a calculation and subsequent setting / classifying reference group data.
Claim 6 reflects two separate concepts, associated with extra solution activity and an abstract mental process / mathematical concept. The language directed to “determining” based upon received/extracted “personal characteristic information” reflects a Mental Process. The language directed to “extracting” inherently involves reception of “personal characteristic information” data, and therefore represents an element that is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. 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); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, these dependent claims are likewise rejected as encompassing subject matter ineligible under 35 USC 101.
Regarding claim 7:
At step 1: claim 7 is directed to a “system” and thus directed to a statutory category.
At step 2a prong 1: claim 7 recites limitations that are directed to an abstract idea: “determin[ing] an entity attribute cluster currently corresponding to the person to be rescued and an entity attribute pair currently corresponding to the person to be rescued according to a question … and an answer …;”, acquir[ing] (i.e., determining via a classification process) first and second reference groups via a classification process; acquiring (i.e., determining) a number/count value for members meeting a cut-off/threshold point; determining a probability value to aid in determining who is the person to be rescued. Note that it appears that the recited terminology “acquiring” reflects a particular translation of the term that actually involves a determination process.
At step 2a prong 2: Claim 7 recites the following additional elements: an entity attribute cluster and entity attribute pair determining module, a first reference group and second reference group determining module, a module for acquiring a number of members of predefined positive examples, and a positive probability determining module. Such claim language is a high-level recitation of a generic computer components (i.e., “modules”) and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. It is noted that the recited “acquiring language” merely involves a determination step, not a reception of data.
Viewing the additional limitations together and the claims as a whole, nothing provides integration into a practical application.
At step 2b: the conclusions for the additional elements representing mere implementation using a computer (i.e., generic computing elements) are carried over and do not provide significantly more.
Therefore, the claim taken as a whole does not change this conclusion, and the claim is ineligible.
Claims 8-12 depend upon claim 7, and do not correct the issues set forth above.
Claims 8-9 and 11-12 could be characterized as mental processes, wherein data is manipulated via a pencil and paper, or alternatively as mathematical concepts. And, claim 10 recites two steps of “acquiring” data (here, meaning the “receiving” of data), and two steps of making “determinations” based on the acquired data. The claim 10 “determining” steps reflect abstract mental processes, and the "acquiring / receiving …" limitations are identified as insignificant extra-solution activity above, and when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. 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); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, these dependent claims are likewise rejected as encompassing subject matter ineligible under 35 USC 101.
35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim limitations directed to a “module” (as recited in independent claims 1 and 7) have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder “module” coupled with functional language “configured to” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 1-12 have also been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
However, a review of the specification shows that the recited “module” encompasses an embodiment that does not appear to be supported by any structure (i.e., hardware elements) described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: See the discussion, below, in the rejection of claims under 35 USC 112-2nd / 112(b). See, for example, Specification paragraph [0145] stating that “the system components can also be realized only by software solutions”, in the context of Specification paragraph [0034] indicating that modules are considered as components. Therefore, the corresponding structure of the claimed elements is unclear.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
The claim language of independent system claims 1 and 7 invokes 35 USC 112(f), as set forth above. However, the disclosure is unclear as to whether or not the recited system elements are supported in the specification by corresponding structure for performing the recited function to satisfy the definiteness requirement of 35 USC 112(b) (or pre-AIA 35 USC 112-2nd paragraph. See, for example, the specification paragraph [0145] stating that “the system components can also be realized only by software solutions”, in the context of paragraph [0034] including modules as components.
Claims 2-6 and 8-12 depend upon claims 1 and 7, respectively, and do not correct the issues set forth above. Therefore, these claims are likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Relevance is provided in at least the Abstract of each cited document.
Non-Patent Literature
Demner-Fishman, Dina, et al., “Answer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering”, Proc. of the 21st International Conf. on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, Australia, July 2006, pp. 841-848.
This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. We tackle a frequently-occurring class of questions that takes the form “What is the best drug treatment for X?” Starting from an initial set of MEDLINE citations, our system first identifies the drugs under study. Abstracts are then clustered using semantic classes from the UMLS ontology. Finally, a short extractive summary is generated for each abstract to populate the clusters. Two evaluations—a manual one focused on short answers and an automatic one focused on the supporting abstracts—demonstrate that our system compares favorably to PubMed, the search system most widely used by physicians today. (page 841, Abstract).
Olson, Catherine H., et al., “Clustering of elderly patient subgroups to identify medication-related readmission risks”, International Journal of Medical Informatics, Volume 85, Issue 1, January 2016, pp. 43-52.
Introduction: High Risk Medication Regimen (HRMR) scores are weakly predictive of hospital readmissions for elderly home health care patients. HRMR is composed of three elements related to drug risks: polypharmacy (number of medications); Potentially Inappropriate Medications (PIM) known to be harmful to the elderly; and the Medication Regimen Complexity Index (MRCI) that weighs drugs by the complexity of their dosing and instructions. In this paper, we hypothesized that HRMR scores are more predictive for demographic subgroups of elderly patients. The study used Outcome and Assessment Information Set (OASIS) variables to identify subgroups of patients for whom the HRMR measures appeared more predictive for hospital readmissions. Methods: OASIS and medication data were reused from a study of 911 patients (355 males, 556 females; mean age 78.9) from 15 Medicare-certified home health care agencies that established the relationship between HRMR and hospital readmissions. Hierarchical agglomerative clustering using the Jaccard distance measure and average-link method identified patient subgroups based on the OASIS data. Receiver operating curve (ROC) analyses evaluated the predictive strength of the HRMR variables for each subgroup. Additional False Discovery Rate (FDR) analyses assessed whether the clustered relationships were chance. (page 43, Abstract).
Aftab, Rana Mohtasham, “A systematic review of data-driven & machine learning frameworks for minimizing emergency response rate”, International Journal of Natural Sciences Research, Volume 11, No. 2, October 9, 2023, pp. 52-64.
Many blackouts have occurred in recent years across the world, wreaking havoc on socioeconomic progress. As a result, it has become a crucial area for research into emergency scenarios like power outages, traffic management, and petrochemical unit dangers, as well as ways for decreasing losses caused by these events. Because the most essential item in an endangered circumstance is life, a person will discover a rapid and precise solution with little response time in an uncommon situation. Many lives have been lost in recent years as a result of ineffective emergency response. Therefore, the main goal of the research is to develop a data-driven emergency response system based on efficient machine learning techniques that is independent of human resources and will provide the necessary emergency response in a fast way. This paper offers preliminary findings from the development of the Emergency Response Assist System, which intends to increase first respond situational awareness and safety. The system collects the essential information from text format about what the caller will say, systematically produces cases, determines the type of the case, and then informs the appropriate department. It keeps track of response time since computers are significantly faster and more efficient than people. Experiments on real crash data and models using data sets show a significant reduction in resource requirements and an accurate reduction in emergency response time. (page 52, Abstract).
US Patent Application Publications
Allen 2019/0163875
Mechanisms are provided for performing entity differentiation. A cognitive medical system ingests a corpus of medical content having references to medical entities, and performs entity recognition on the medical content to identify the medical entities. Responsive to the cognitive medical system identifying a medical entity having a plurality of annotations for a same medical entity attribute, an entity differentiation component executes an ordered set of entity differentiation algorithms, corresponding to the medical entity, for differentiating medical entity attribute values. The entity differentiation component runs the ordered set of entity differentiation algorithms, in order, on the plurality of annotations for the attribute to generate a ranked list of medical entity attribute values corresponding to the annotations in the plurality of annotations. The cognitive medical system performs a cognitive operation on the medical entity based on the ranked list of medical entity attribute values. (Abstract). As an overview, a cognitive system is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to conveying and manipulating ideas, which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. A cognitive system performs one or more computer-implemented cognitive operations that approximate a human thought process as well as enable people and machines to interact in a more natural manner so as to extend and magnify human expertise and cognition. A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic, which may be provided as specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware. The logic of the cognitive system implements the cognitive operation(s), examples of which include, but are not limited to, question answering, identification of related concepts within different portions of content in a corpus, intelligent search algorithms, such as Internet web page searches, for example, medical diagnostic and treatment recommendations, and other types of recommendation generation, e.g., items of interest to a particular user, potential new contact recommendations, or the like. (para 0058).
Green 2011/0301982
An integrated medical software system with embedded transcription functionality is disclosed. The system comprises a clinical module for capturing clinical data for a patient in a first electronic document and a communication component that communicates the clinical data to a rule-based clinical decision support (CDS) system and receives at least one of an alert, warning, reminder, and recommendation back from the CDS system based on the clinical data. The CDS system is configured to compare the clinical data against a knowledge base to identify the at least one of an alert, warning, reminder, and recommendation; the clinical data is serialized into a standardized database language and placed into a first electronic clinical document defined by a clinical document exchange standard before being communicated to the CDS system; and the at least one of an alert, warning, reminder, and recommendation is provided in a second electronic clinical document defined by the clinical document exchange standard when received back from the CDS system. (Abstract).
Felemban 2016/0328530
Rank Storing: In order to attain the rank using Bloom filter the term rank is simplified to a membership to a class of urgency. In one embodiment, each class represented by a Bloom filter holds all the patients from that class, since BSNs classify patients into urgency classes from the readings of the patient's physiological data. Membership queries can then be performed on the Bloom filter to find out whether a certain patient belongs to a specific class. Rescue team focusses their attention to the most urgent class since the number of patients is enormous which means that the number of patients in the most urgent class will be large as well. In another embodiment, paramedics send a request asking for the Bloom filter which contains the most urgent cases so that they can rescue them first. In one embodiment, paramedics request Bloom filters of less urgent classes if needed. The use of Bloom filters definitely reduce traffic, instead of sending the IDs of all patients that would require a lot of space Patients IDs are hashed into a Bloom filter. Each ID is represented by k indices which are much smaller than the size of the ID which will save a lot of space and thus minimizing traffic as this Bloom filter would have to be sent to all the nodes in the network. Moreover, Bloom filters have no false negatives meaning that there is a zero probability of an urgent patient being neglected; when a membership query returns not found for a patient this patient is certainly not urgent. However, Bloom filters introduce false positives, which will result in dealing with a non-urgent case as an urgent one, but their probability can be controlled by changing the size of the filter. (para 0022). FIG. 1 shows the gradation in casualty incidents. Not all the incidents can be classified as Mass Casualty Incident (MCI). Hence, the action that is taken is commensurate to the safety and security impact that effect has created. For example, an individual incident 102 normally leads to a local call for help to medical center or hospital 112. However, when a multiple group incident 104 happens such as a car accident, a 9-1-1 call to bring Police, Fire and Ambulance 114. Similarly, community related incidents 106 end up being serviced by local police, fire or ambulance 114. On the contrary, when a natural disaster such as flood, hurricane and tornado occurs 108, disaster management kicks in 116. Disaster management includes comprehensive prevention, evacuation and recovers planning for general safety and security of the public. Natural disasters could lead to Mass Casualty some times. Typical Mass Casualty Incident (MCI) 110 happens unexpectedly due to negligence or planning. For example, terrorist attack or bridge collapse comes unannounced. This is where Emergency Medical Services (EMS) 118 kicks in full force. EMS requires the patient data as soon as they arrive, and the proposed method enables quick gathering and ranking of patient data for patient handling prioritization. (
Woodson 2014/0249850
Technologies for medical information and scheduling communication determining a patient condition of a person presently experiencing the condition, determining a timeline, indicating the timeline, receiving real-time information regarding the condition, and updating the timeline. The timeline illustrates time lapsed since an initialization of treatment tracking, a recommended treatment of the patient condition, a treatment time necessary for effective application of the treatment for the patent condition, and an average time of treatment. (Abstract). Furthermore, if a patient is en-route to a facility, as communicated by any suitable mechanism such as emergency en-route module 406 or EMS application 120, the facility may be configured to receive the en-route information from the patient and prioritize the patient. The patient may be placed in a higher priority if arriving by EMS. Also, emergency en-route module 406 may be configured to communicate with system 100 to provide arrival status, check-in status, room assignment, or other information. (para 0086). Critical condition module 1702 may implement fully or in part one or more applications described above such as server application 104, administration application 112, caregiver application 116, or EMS application 120. Furthermore, critical condition module 1702 may be implemented in any suitable fashion, such as by modules, logic, instructions, executables, libraries, functions, scripts, applications, hardware, software, firmware, input/output mechanisms, displays, views, or any suitable combination thereof. The functionality used within a given instance of critical condition module 1702 may be selectively provided based upon a user or a classification of user of critical condition module 1702. For example, the functionality of critical condition module 1702 may be selectively presented to or used by an EMS worker, technician, nurse, or physician. (para 0213).
Omoigui 2012/0191716
With Today's Web environment, the semantics of information presented are lost upon conversation of the structured data to HTML at the server, meaning that the "knowledge" is stripped from the objects before the user has an opportunity to interact with them. In addition, Today's Web is authored and "hard-coded" on the server based on how the author "believes" the information will be navigated and consumed. Users consume only information as it is presented to them. (para 0472). The interpreter then proceeds to the execution phase. In this phase, the interpreter reviews the semantic entry table and executes all the resource queries asynchronously, or in sequential fashion. Next, it processes each resource based on the resource type. For example, for file resources, it opens the property metadata for the file and displays the metadata. For HTTP resources that refer to understood types (e.g., documents), the interpreter downloads the URL, extracts it, and displays it. For Agent resources, it calls the XML Web Service for each Agent and passes the links as XML arguments, qualifying each link with the operator. In the preferred embodiment, operators for links that cross document boundaries are always AND. In other words, the interpreter will AND all links for identical resources that are not declared together because recursive queries are assumed to be filters. The interpreter issues as many calls to a component representing the resource as there are Agent resources. For each link, the interpreter resolves the link by converting it into a query suitable for processing by the resource. (para 1059).
US Patents
Shields 11,763,949
An emergency medical treatment system is provided that can be used in connection with providing prehospital medical treatment to a patient. The system includes a patient data display device programmed to receive and display data associated with the patient; an environmental assessment device configured to capture visual, aural, or other ambient environmental data associated with an emergency treatment site associated with the patient; a patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient; and a patient data processing device configured with a speech-to-text module. Rules-based or machine learning based algorithm modules can be provided for generating alerts or making treatment option recommendations in connection with the patient data collected and displayed on the patient display device. (Abstract).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner ROBERT STEVENS whose telephone number is (571) 272-4102. The examiner can normally be reached Mon - Fri 6:00 - 2:30.
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/ROBERT STEVENS/Primary Examiner, Art Unit 2164
January 6, 2026