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
This action is in reply to the application filed on 05/24/2024.
Claims 1-30 are currently pending and have been examined.
Claim Rejections – 35 § 112(b)
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 1-30 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, recites in part, “analyzing interactions between the patient and the one or more AI agents to identify one or more urgent issues”. It is unclear how interactions between the patient and the one or more AI agents to identify one or more urgent issues is being analyzed. Is there an algorithm or formula that is being used to analyze interactions between the patient and the one or more AI agents to identify one or more urgent issues? Claims 11 and 21 recite similar limitations. Claims 1, 11 and 21 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-10, 12-20 and 22-30 are rejected for at least the same reason.
Claim 5 recites "enabling the one or more AI agents to interact with the patient". It is unclear if this step is performed as it is not positively recited. Claim 15 and 25 recite similar limitations. Claims 5, 15 and 25 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All claims dependent from this claim (claims 6, 7, 16, 17, 26 and 27) are rejected for the same reasons.
Claim 7 recites "enabling review of the interaction summary by one or more healthcare professionals". It is unclear if this step is performed as it is not positively recited. Claim 17 and 27 recite similar limitations. Claims 7, 17 and 27 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired.
Claim 8 recites "enabling the AI agents to collaborate with each other to perform the one or more tasks associated with the post-acute care transition phase of the patient". It is unclear if this step is performed as it is not positively recited. Claim 18 and 28 recite similar limitations. Claims 8, 18 and 28 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired.
Claim 9 recites "enabling the one or more AI agents to collaborate with one or more healthcare professionals to perform the one or more tasks associated with the post-acute care transition phase of the patient". It is unclear if this step is performed as it is not positively recited. Claim 19 and 29 recite similar limitations. Claims 9, 19 and 29 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired.
Claim 10 recites "enabling communications with one or more of: a healthcare database, a healthcare platform, and a governmental / information database". It is unclear if this step is performed as it is not positively recited. Claim 20 and 30 recite similar limitations. Claims 10, 20 and 30 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-30: Step 2A Prong One
Claim 1 recites detecting the discharge of a patient from an acute care facility; deploying one or more AI agents to perform one or more tasks associated with a post-acute care transition phase of the patient; and analyzing interactions between the patient and the one or more AI agents to identify one or more urgent issues. Claims 11 and 21 recite similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, cover manual, human performance of the limitations, following rules or instructions, which constitutes Certain Methods of Organizing Human activity but for the recitation of generic computer components. That is, other than reciting “computing device,” “AI agents,” “computer program product,” “computer readable medium,” “processor,” “computing system,” and “memory,” nothing in the claim precludes the limitations from practically being performed by a human following rules or instructions or in the mind of a user. For example, but for the recited generic computer components, the claimed steps encompass a user detecting the discharge of a patient from an acute care facility; deploying one or more AI agents to perform one or more tasks associated with a post-acute care transition phase of the patient; and analyzing interactions between the patient and the one or more AI agents to identify one or more urgent issues. If a claim limitation, under its broadest reasonable interpretation, covers manual, human performance of the limitation but for the recitation of generic computer components, then it falls within the Certain Methods of Organizing Human Activity grouping of abstract ideas. Accordingly, this claim recites an abstract idea. Claims 11 and 21 recite similar limitations.
Claims 2-10, 12-20 and 22-30 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components, by reciting various combinations of detecting the discharge of a patient, AI agents, identifying the one or more urgent issues, summarizing interactions, enabling review of the interaction summary, enabling collaboration, and enabling communications. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-30: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with generally linking the abstract idea to a particular technological environment. For example the claims, directly or indirectly, recite “computing device,” “AI agents,” “computer program product,” “computer readable medium,” “processor,” “computing system,” and “memory,” to carry out the abstract idea as set forth above. These elements recite a generic computing system by reciting general purpose computer components to perform the functions of the claims (Applicant’s Specification, see at least Paragraph [0014], “In another implementation, a computing system includes a processor and a memory system configured to perform operations“, Paragraph [0035], “An artificial intelligence (AI) agent (e.g., AI agents 108), generally speaking, is a computer program or system designed to perceive its environment, reason about the information it receives, and take actions to achieve specific goals or tasks autonomously. AI agents (e.g., AI agents 108) may leverage various techniques and algorithms to simulate intelligent behavior, including machine learning, natural language processing, expert systems, and symbolic reasoning”, Paragraph [0082], “Examples of computing device 300 may include, but are not limited to: a personal computer, a server computer, a series of server computers, a mini computer, a mainframe computer, a smartphone, or a cloud-based computing platform”, Paragraph [0091], “the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium“, Paragraph [0092], “Any suitable computer usable or computer readable medium may be used. The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium”, Paragraph [0094], “These computer program instructions may be provided to a processor of a general-purpose computer/special purpose computer/other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks”,. Although the additional element “AI agents” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (artificial intelligence), and thus fails to add an inventive concept to the claims. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-30: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, AI agents) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Thus these elements taken individually or together do not amount to significantly more than the abstract ideas themselves.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 11, 12, 21 and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaliraman et al. (US Patent Application Publication US 2023/0130914 A1).
Claim 1:
Kaliraman discloses the following limitations as shown below:
detecting the discharge of a patient from an acute care facility (see at least Paragraph 38, The handoff type module 520, for example, may identify a shift change, an inter-unit transfer, a rounding, a service change, a break during shift, an admission or discharge);
deploying one or more AI agents to perform one or more tasks associated with a post-acute care transition phase of the patient (see at least Paragraph 57, in an embodiment, AI may be used to help improve the IUs and Handoff process/information exchanged. For example, a receiver may ask a question and the AI may learn to incorporate the question into future exchanges, etc.); and
analyzing interactions between the patient and the one or more AI agents to identify one or more urgent issues (see at least Paragraph 42, The deep learning/AI module 590 may be used by the engine/s module 504 for various tasks that may help prioritize patient information, parse and transform information, classify information, create and modify triggers and actions, and create and modify IUs. The AI module 590 may be used to perform natural language processing (NLP) of information that exists as notes or free text or as non-discrete information or as narrative notes in clinical information module 540 or patient information module 505 or information entered into the system.).
Claims 11 and 21 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 1 and, as such, are rejected for similar reasons as given above.
Claim 2:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman further discloses the following limitations:
processing at least a portion of an Admission, Discharge, and Transfer (ADT) feed (see at least Paragraph 29, FIG. 1 illustrates an example 100 of typical breaks in patient care. These are examples, and merely used to help describe the types and occurrences of breaks in care. Breaks in patient care may occur at many different places internally within an organization or externally between multiple entities. Different types of handoffs may occur. Examples of admission or discharge transfers are shown as 110a-c. A patient may be discharged from SNF to home 110c; Paragraph 38, The handoff type module 520 may identify what type of handoff it is. The handoff type may matter as to how the engine will process the handoff. The handoff type module 520, for example, may identify a shift change, an inter-unit transfer, a rounding, a service change, a break during shift, an admission or discharge).
Claims 12 and 22 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 2 and, as such, are rejected for similar reasons as given above.
Claim Rejections - 35 USC § 103
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 of this title, if the differences between the claimed invention and the prior art axe such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 5, 10, 13, 15, 20, 23, 25 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication US 2023/0130914 to Kaliraman et al. in view of US Patent Application Publication US 2021/0233424 A1 to Lemme et al.
Claim 3:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman may not specifically disclose the following limitations, but Lemme as shown does:
wherein the one or more AI agents includes one or more of the following: a transitions of care history interview and discharge review agent; a medication reconciliation agent; a medical knowledge query response agent; a community resources agent; and an appointment scheduling and confirmation agent (see at least Paragraph 28, The artificial intelligence agent analyzes the responses provided by the user, the system selects a coordinated series of predefined therapy routines and/or lifestyle recommendations, each consisting of predefined exercises and specs of each exercise (i.e. sets, repetitions, time) specifically for each routine, all designed to systematically correct posture deviations, muscular imbalances and biomechanical compensations and to relieve the user's symptoms, in addition to modifying lifestyle, habits, and behavior of the user. These therapy routines are stored in the remote database, or another database (not shown) and are accessible to the AT system; Paragraph 33, The artificial intelligence agent accordingly applies proven therapeutic exercise and modality sciences for pain-relief, weight-loss, and strength-training while implementing macro-program sequencing using a proprietary substitution system that is based on initial contraindications, and client feedback via voice communication). The autonomous divergent learning guides the user interactions to leverage knowledge of postural therapists, physical therapists, fitness trainers, and MD's in real time via the artificial intelligence agent).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman with the variety features of Lemme with the motivation of providing the benefit “… to develop, deliver and implement ongoing personalized activity, physical therapy, and/or behavior recommendations for the user” (Lemme, see at least Paragraph 1).
Claims 13 and 23 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 3 and, as such, are rejected for similar reasons as given above.
Claim 5:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman may not specifically disclose the following limitations, but Lemme as shown does:
enabling the one or more AI agents to interact with the patient (see at least Abstract, A user directed verbal interactive method and system for requesting an evaluation and obtaining a customized verbal therapy routine based on the evaluation obtained. The method and system allow users to interact with an artificial intelligence agent by answering a series of system directed questions that guides the users through evaluation and treatment of physical pain using a customized verbal interaction and delivery regimen; Paragraph 1, an automated system whereby a user interacts with and is monitored by a conversational artificial intelligence (AI) agent (in form of voice only and/or avatar) and further whereby monitored responses and behaviors are analyzed by the AI system to develop, deliver and implement ongoing personalized activity, physical therapy, and/or behavior recommendations for the user).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman with the interactivity of Lemme for at least the same reasons given for claim 4 above.
Claims 15 and 25 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 5 and, as such, are rejected for similar reasons as given above.
Claim 10:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman does not specifically disclose the following limitations, but Lemme as shown does:
enabling communications with one or more of: a healthcare database, a healthcare platform, and a governmental/information database (see at least Paragraph 28, The artificial intelligence agent analyzes the responses provided by the user, the system selects a coordinated series of predefined therapy routines and/or lifestyle recommendations, each consisting of predefined exercises and specs of each exercise (i.e. sets, repetitions, time) specifically for each routine, all designed to systematically correct posture deviations, muscular imbalances and biomechanical compensations and to relieve the user's symptoms, in addition to modifying lifestyle, habits, and behavior of the user. These therapy routines are stored in the remote database, or another database (not shown) and are accessible to the AT system; Paragraph 33, The artificial intelligence agent accordingly applies proven therapeutic exercise and modality sciences for pain-relief, weight-loss, and strength-training while implementing macro-program sequencing using a proprietary substitution system that is based on initial contraindications, and client feedback via voice communication). The autonomous divergent learning guides the user interactions to leverage knowledge of postural therapists, physical therapists, fitness trainers, and MD's in real time via the artificial intelligence agent. In addition, it analyzes a plethora of evaluation and medical conditions via libraries of connected data and sifts through such large databases and applies appropriate therapy/fitness sequencing, recommendations, and protocols, via any voice assistant system).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman with the interactivity of Lemme for at least the same reasons given for claim 4 above.
Claims 20 and 30 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 10 and, as such, are rejected for similar reasons as given above.
Claims 4, 9, 14, 19, 24 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication US 2023/0130914 to Kaliraman et al. in view of US Patent Application Publication US 2023/0245651 A1 to Wang.
Claim 4:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman may not specifically disclose the following limitations, but Wang as shown does:
in response to identifying the one or more urgent issues, escalating the one or more urgent issues to one or more healthcare professionals (see at least Paragraph 372, if the AI system detects a patient in need of urgent medical attention, the CAM can use the environment data to determine the most appropriate healthcare professional to attend to the patient based on their location and availability).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman with the escalation of Wang with the motivation to provide the benefit to “… facilitate user-centered, contextually relevant, and personalized conversational interaction” (Wang, see at least Paragraph 5).
Claims 14 and 24 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 4 and, as such, are rejected for similar reasons as given above.
Claim 9:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman further discloses the following limitations:
one or more tasks associated with the post-acute care transition phase of the patient (see at least Paragraph 29, FIG. 1 illustrates an example 100 of typical breaks in patient care. These are examples, and merely used to help describe the types and occurrences of breaks in care. Breaks in patient care may occur at many different places internally within an organization or externally between multiple entities. Different types of handoffs may occur. Examples of admission or discharge transfers are shown as 110a-c. A patient may be discharged from SNF to home 110c; Paragraph 38, The handoff type module 520 may identify what type of handoff it is. The handoff type may matter as to how the engine will process the handoff. The handoff type module 520, for example, may identify a shift change, an inter-unit transfer, a rounding, a service change, a break during shift, an admission or discharge; Paragraph 38, The handoff type module 520, for example, may identify a shift change, an inter-unit transfer, a rounding, a service change, a break during shift, an admission or discharge; Paragraph 57, in an embodiment, AI may be used to help improve the IUs and Handoff process/information exchanged. For example, a receiver may ask a question and the AI may learn to incorporate the question into future exchanges, etc.).
Kaliraman may not specifically disclose the following limitations, but Wang as shown does:
enabling the one or more AI agents to collaborate with one or more healthcare professionals to perform the one or more tasks (see at least Paragraph 372, if the AI system detects a patient in need of urgent medical attention, the CAM can use the environment data to determine the most appropriate healthcare professional to attend to the patient based on their location and availability).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman and the interactivity of Lemme with the collaboration of Wang for at least the same reasons given for claim 4 above.
Claims 19 and 29 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 9 and, as such, are rejected for similar reasons as given above.
Claims 6, 7, 16, 17, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication US 2023/0130914 to Kaliraman et al. in view of US Patent Application Publication US 2021/0233424 A1 to Lemme et al. and further in view of US Patent Application Publication US 2020/0320365 A1 to Arat et al.
Claim 6:
The combination of Kaliraman/Lemme discloses the limitations shown in the rejections above. Kaliraman may not specifically disclose the following limitations, but Arat as shown does:
summarizing interactions between the patient and the one or more AI agents to generate an interaction summary (see at least Paragraph 57, In one implementation, as shown in FIG. 2A, processor 202 may comprise a conversation analysis component 210, a conversation generation component 220, and an evaluation classification component 230. Generally speaking, conversation analysis component 210 may be configured to analyze and interpret inputs received from a user's computing device (e.g., computing device 112, 114 of FIG. 1.). … Conversation analysis component 210 may take various forms; Paragraph 73, Scoring engine 236 may be configured to generate scores for a user involved in an interactive conversational session with a digital conversational character that can be used to summarize various aspects of the user, such as the user's personality traits, technical skills, knowledge, and/or soft skills. In some implementations, scoring engines can also include various statistics related to an interactive conversational session, including a user's response time, length of sentences, and/or vocabulary diversity).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman and the interactivity of Lemme with the interaction feature of Arat with the motivation of providing the convenience of providing a system and method of providing “… a digital conversational character … configured to ask and respond to targeted questions during an interactive conversational session between a patient and the digital conversational character without the need for the doctor to participate in the interactive conversational session” (Arat, see at least Paragraph 43).
Claims 16 and 26 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 6 and, as such, are rejected for similar reasons as given above.
Claim 7:
The combination of Kaliraman/Lemme/Arat discloses the limitations shown in the rejections above. Kaliraman may not specifically disclose the following limitations, but Arat as shown does:
enabling review of the interaction summary by one or more healthcare professionals (see at least Paragraph 73, Scoring engine 236 may be configured to generate scores for a user involved in an interactive conversational session with a digital conversational character that can be used to summarize various aspects; Paragraph 210, At block 610, back-end platform 102 may then store the received conversational data in a given datastore (e.g., a datastore stored in data storage 204, or a datastore from an external data source). The stored conversational data may be used for various purposes; Paragraph 212, As another example, the stored conversational data may be used to update a set of conversational content that may be generated to define a given type of visual conversational application via the disclosed content authoring tool. In this respect, the first user (e.g., a professional) may then use the updated set of conversational content to define a given type of visual conversational application as described above; Paragraph 214, The stored conversational data may be used for various other purposes as well).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman and the interactivity of Lemme with the interaction feature of Arat for at least the same reasons given for claim 6 above.
Claims 17 and 27 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 7 and, as such, are rejected for similar reasons as given above.
Claims 8, 18 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication US 2023/0130914 to Kaliraman et al. in view of US Patent Application Publication US 2021/0233424 A1 to Lemme et al. and further in view of US Patent Application Publication US 2024/0176653 A1 to Lee.
Claim 8:
Kaliraman discloses the limitations shown in the rejections above. Kaliraman further discloses the following limitations:
tasks associated with the post-acute care transition phase of the patient (see at least Paragraph 38, The handoff type module 520, for example, may identify a shift change, an inter-unit transfer, a rounding, a service change, a break during shift, an admission or discharge; Paragraph 57, in an embodiment, AI may be used to help improve the IUs and Handoff process/information exchanged. For example, a receiver may ask a question and the AI may learn to incorporate the question into future exchanges, etc.)
Kaliraman may not specifically disclose the following limitations, but Lee as shown does:
enabling the AI agents to collaborate with each other to perform the one or more tasks (see at least Paragraph 9, Another object of the present disclosure is to make a task plan such that multiple AI agents are able to collaborate with each other; Paragraph 69, Here, the process of generating the machine instruction set at step S230 may comprise requesting collaboration from a nearby AI agent when an object, the relevance of which is greater than a preset value, is not present, among the objects in the relevance map of the AI agent generating the instruction set).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the artificial intelligence of Kaliraman and the interactivity of Lemme with the collaboration of Lee with the motivation of providing the benefit of saving time and resources because the artificial intelligence is able to “… autonomously makes the actual master plan of the task and compensates for the lacking part by making a subplan, whereby action plans are made and carried out” (Lee, see at least Paragraph 84).
Claims 18 and 28 recite substantially similar computer program product and system limitations to those of computer-implemented method claim 8 and, as such, are rejected for similar reasons as given above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Friday, 9:00am-5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686