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 .
Claim Interpretation
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an acquisition unit”, “a generation unit” and “an update unit” in claim 19.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The Supreme Court has long held that “[l]aws of nature, natural phenomena, and abstract ideas are not patentable.” Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014) (quoting Assoc. for Molecular Pathology v. Myriad Genetics, Inc., 133 S. Ct. 2107, 2116 (2013) (internal quotation marks omitted)). The “abstract ideas” category embodies the longstanding rule that an idea, by itself, is not patentable. Alice Corp., 134S. Ct. at 2355 (quoting Gottschalk v. Benson, 409 U.S. 63, 67 (1972).
In Alice, the Supreme Court sets forth an analytical “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas [or mental processes ] from those that claim patent-eligible applications of those concepts.” Id. at 2355 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296–97 (2012)). The first step in the analysis is to “determine whether the claims at issue are directed to one of those patent-ineligible concepts.” Id. If the claims are directed to a patent-ineligible concept, the second step in the analysis is to consider the elements of the claims “individually and ‘as an ordered combination’” to determine whether there are additional elements that “‘transform the nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo, 132 S. Ct. at 1298, 1297). In other words, the second step is to “search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself’”. Id. (brackets in original) (quoting Mayo, 132 S. Ct. at 1294). The prohibition against patenting an abstract idea “‘cannot be circumvented by attempting to limit the use of the formula to a particular technological environment’ or adding ‘insignificant post-solution activity.’” Bilski v. Kappos, 561 U.S. 593, 610–11 (2010) (citation omitted).
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Independent Claim 1 recites the information processing method of displaying plan information representing a future plan, correcting the future plan and updating the plan information based on a detected user reaction to the plan information. Independent claim 1 is directed to a process. A process is a statutory category of invention. Independent Claim 19 recites an information processing device that acquires information on a reaction of a user, basic information of the user and an ideal plan of the user in consultation on a future plan, generates plan information representing the future plan and further updates the plan information based on a reaction of the user. An apparatus is a Statutory category of invention. Dependent claims 2-18 are dependent on claim 1 and therefore recite its respective statutory class.
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. In applying the framework set out in Alice, examiner found Applicant’s claims 1 and 19 are directed to a patent-ineligible abstract concept of updating generated plan information representing a future plan for a user, based on a user reaction to said plan information. The steps of Applicant’s claims 1-19 are an abstract concept that would fall under the judicial exception of mental processes. Specifically, the claims recite the step of “displaying plan information, the plan information representing a future plan and being generated based on basic information on a user and an ideal plan of the user in consultation on the future plan of the user through a voice interaction.” The claim limitation recites simply the presentation of data, such as a “plan information representing a future plan” that is generated based on basic information of the user and information gathered about an ideal plan of the user gathered through a voice interaction. As such, the claim limitation can be represented by a person-to-person consultation, such as a mentor-mentee session or a patient-doctor consultation where the generation of the plan information may represent a treatment or care plan that is developed by one of the participants, and that uses information gathered from the other participant of the consultation session. Therefore, this step is directed to a mental process. Furthermore, the step of “correcting the future plan and updating the plan information in accordance with information on reaction of the user to the plan information that has been displayed” recites steps that are directed to mental processes. Under the broadest reasonable interpretation, correcting and updating the plan information based on a reaction of the user may be performed by a human such as a mentor or doctor/physician that is in consultation with a mentee or patient and may adjust a care plan based on a reaction of the mentee or patient to a presented care plan, such that the adjusted care plan may be better fitting for the mentee or patient. Therefore, the above steps are also directed to mental processes. Regarding claim 19, the claim recites the step of “an acquisition unit that acquires information on reaction of a user, basic information on the user, and an ideal plan of the user in consultation on a future plan of the user through a voice interaction.” The limitation recites language that is directed to acquiring data or information. Thus, the recited elements are directed to mental processes. Furthermore, the claim recites the limitation of “a generation unit that generates plan information representing the future plan based on the basic information and the ideal plan which have been acquired”. As noted from the analysis of independent claim 1, the recited generation of a plan information may represent a treatment or care plan that is developed by one of the participants in a consultation session between a mentor-mentee or a patient-doctor, and that uses information gathered from the other participant of the consultation session. Thus, the limitation recites a mental process. Finally, the claim recites “an update unit that corrects the future plan and updates the plan information in accordance with the information on reaction to the plan information that has been generated”. The recitation is directed to correcting and updating the plan information which may be performed by a human such as a mentor or doctor/physician that is in consultation with a mentee or patient and may adjust a care plan based on a reaction of the mentee or patient to a presented care plan, such that the adjusted care plan may be better fitting for the mentee or patient. Thus, the recited limitation is directed to a mental process. The claims recite limitations that taken in combination, recite at least a series of mental processes.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
independent Claim 1 recites “a computer” as an additional element beyond the judicial exception. However, this additional element does not amount to significantly more than the abstract idea because the additional element constitutes a generic computer environment. Alice, 134 S. Ct. at 2357. The Claims need meaningful limitations that go beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, the steps are all abstract and the Claim as a whole is abstract. “[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” CLS Bank, 2013 U.S. App. LEXIS 9493, at *29 (citing Bancorp, 687 F.3d at 1278, and Dealertrack, Inc. v. Huber, 674 F.3d 1315, 1333-34 (Fed. Cir. 2012) (finding that the claimed computer-aided clearinghouse process is a patent-ineligible abstract idea)); SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 1333 (Fed. Cir. 2010) (“In order for the addition of a machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e., through the utilization of a computer for performing calculations.”). Additionally, dependent claims 2-18 do not provide any additional elements that integrate the judicial exception into a practical application. The claims simply describe multiple form and situations of interaction that may be represented by person-to-person interaction, such as, for example, detecting a “line of sight” of the user, which may be performed by a human by simple observation. Therefore, the dependent claims simply recite a series of mental processes without significantly more.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
At step 2A, prong two, the additional elements of the “computer” were found to be a generic computer environment. At Step 2B, the re-evaluation of the generic computer environment consideration takes into account whether or not the generic computer environment is well understood, routine, and conventional in the field. See MPEP 2106.05(g). Here, the step of using a computer to perform the processing method comprising the claimed steps is recited at such a high level of generality that the claimed is simply attaching the generic computer to the steps that may otherwise be performed by a human. Therefore, this limitation remains a generic computing environment even upon reconsideration and does not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Additionally, dependent claims 2-18 do not add an inventive concept.
In conclusion, Examiner notes that none of recited steps in Applicant's claims 1-19 refer to a specific machine by reciting structural limitations of any apparatus or to any specific operations that would cause a machine to be the mechanism to perform these steps. Although the claims may be processed by a computing system, the computing system is merely a general purpose computing system. Therefore, all of the claims 1-19 are abstract.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to “an information processing program” and thus, it recites a “software per se”. As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719. Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed "device profile" comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product). Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category. See MPEP 2601.03.
Claim Rejections - 35 USC § 102
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.
Claims 1, 3-7, 9, 12, 13 and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gnanasambandam Nathan (WO 2021/150607 A1; hereinafter “Nathan”).
As per claims 1 and 20, Nathan discloses:
An information processing method performed by a computer and an information processing program causing a computer to: display plan information (Nathan; Fig. 9B, items 920 & 922; p. 0247-0249 - the cognitive agent states: “Congrats on taking the first step toward better health! Based upon your interest, I have some recommended health improvement initiatives for you to consider,” and presents the health improvement initiatives), the plan information representing a future plan and being generated based on basic information on a user and an ideal plan of the user in consultation on the future plan of the user (Nathan; Fig. 60A, item 6002; p. 0507 - The cognitive intelligence platform 102 provided a care plan 6002 that was originally generated for the patient for a medical condition of the patient. The care plan 6002 may include an action instruction pertaining to the medical condition of the user 6000, such as an instruction to read certain recommended content for the medical condition, schedule an appointment with a physician, perform a certain test for the medical condition, etc; see also p. 0511 - If the cognitive intelligence platform 102 receives the input data 6010, 6012, and/or 6014 when the care plan 6002 is presented to the user 6000 on the user device 104, and the cognitive intelligence platform 102 detects a negative emotion (e.g., angry) and/or tone (e.g., hostile), the cognitive intelligence platform 102 may modify the care plan 6002 to generate an updated care plan 6020) through a voice interaction (Nathan; Fig. 14, item 1408; p. 0258 – audio input interface; see also p. 0559 - cognitive intelligence platform 102 may process a video recording using a machine learning model that has been trained to identify patterns between images of certain facial expressions, certain body language, certain emotions (e.g., happy, angry, sad, etc.), certain tones of voice, and/or the like); and correct the future plan and update the plan information in accordance with information on reaction of the user to the plan information that has been displayed (Nathan; Fig. 9B, items 924 (reaction of the user), 928 & 930 (updated care plan); p. 0184 - The cognitive intelligence platform 102 relates actions, the sequences of subsequent actions (and reactions), desired sub-outcomes, and outcomes, in a way that is transparent and logical (e.g., explainable). The cognitive intelligence platform 102 can plot a next best action sequence and a planning basis (e.g., health care plan template, or a financial goal achievement template), also in a manner that is explainable (updating plan information); see also Fig. 60A-E, 61 & 62; p. 0507-0538 – [0507] FIG. 60A-E show examples of modifying a care plan based on a detected emotion of the patient, a detected tone of the patient, a different medical outcome entered by a physician… [0511] If the cognitive intelligence platform 102 receives the input data 6010, 6012, and/or 6014 when the care plan 6002 is presented to the user 6000 on the user device 104, and the cognitive intelligence platform 102 detects a negative emotion (e.g., angry) and/or tone (e.g., hostile), the cognitive intelligence platform 102 may modify the care plan 6002 to generate an updated care plan 6020… [0513] the cognitive intelligence platform 102 may track the detected conditions and/or tones of the users in reaction to care plans that are presented on the user device 104… if the detected emotion (e.g., happy) and/or tone (e.g., cheerful) is positive, the cognitive intelligence platform 102 may modify the care plan to generate an updated care plan 6020. The updated care plan 6020 may include a different subset of health artifacts than the care plan 6002…).
As per claim 3, Nathan discloses: The information processing method according to claim 1, wherein the information on reaction relates to an utterance content of the user (Nathan; p. 0509 - spoken words 6012 and/or the text 6010 may be processed by a machine learning model that is trained on training data that identifies patterns between the spoken words and/or text and certain emotions and/or tones (e.g., attitude of the user 6000 towards the subject presented on the user device 104). The tones may include cheerful, pessimistic, optimistic, sarcastic, hostile, and the like; see also p. 0184 - The cognitive intelligence platform 102 can utilize a critical thinking engine 108 and a natural language database 122 (e.g., a linguistics and natural language understanding system) to relate conversation material to actions).
As per claim 4, Nathan discloses: The information processing method according to claim 1, wherein the future plan relates to a life plan (Nathan; p. 0237 - In FIG. 8B, the screenshot 851 is seen in response to the user’s selection of Diabetes (element 824). Example elements displayed in screenshot 851 include: Know How YOUR Body Works (element 852); Know the Current Standards of Care (element 864); Expertise: Self-Assessment (element 866); Expertise: Self-Care/Treatment (element 868); and Managing with Lifestyle (element 870). Managing with Lifestyle (element 870) focuses and tracks actions and lifestyle actions that a user can engage in. As a user’s daily routine helps to manage diabetes, managing the user’s lifestyle is important. The cognitive agent 110 can align a user’s respective health plan based on a health assessment at enrollment. In various embodiments, the cognitive agent 110 aligns the respective health plan with an interest of the user, a goal and priority of the user, and lifestyle factors of the user — including exercise, diet and nutrition, and stress reduction).
As per claim 5, Nathan discloses: The information processing method according to claim 1, wherein the plan information is a life plan chart (Nathan; Fig. 66; p. 0616 - FIG. 66 shows an example of providing a user interface 6600 that provides dynamic charting and personalization of a care plan in real-time or near real-time…; see also Fig. 63B for example display showing patient information in the form of charts).
As per claim 6, Nathan discloses: The information processing method according to claim 1, wherein the voice interaction is created between the user and an artificial intelligence (AI) agent (Nathan; p. 0089-0090 - The cognitive intelligence platform has the ability to extract concepts, relationships, and draw conclusions from a given text posed in natural language (e.g., a passage, a sentence, a phrase, and a question) by performing conversational analysis which includes analyzing conversational context… the cognitive intelligence platform implements an intuitive conversational cognitive agent that engages in a question and answering system that is human-like in tone and response; see also p. 0134 - The critical thinking engine 108 represents a set of instructions executing within the cognitive intelligence platform 102 that execute tasks using artificial intelligence, such as recognizing and interpreting natural language (e.g., performing conversational analysis), and making decisions in a linear manner (e.g., in a manner similar to how the human left brain processes information)).
As per claim 7, Nathan discloses: The information processing method according to claim 6, wherein a computer further executes processing of causing the AI agent to ask the user a question about information lacked in the plan information, and the future plan is corrected in accordance with a response of the user and the plan information is updated in processing of the updating (Nathan; p. 0246-0250 - The cognitive agent 110 prompts the user to fill out the health assessment… The cognitive agent 110 receives the user’s response as another originated question and undergoes an initial round of analysis (and additional rounds of analysis as needed) as described above. In the example screen shot 926, the cognitive agent 110 determines additional information is needed and prompts the user for additional information; see also p. 0449).
As per claim 9, Nathan discloses:
The information processing method according to claim 7, wherein, in the processing of asking, a content of the question is changed in accordance with an attribute of the user (Nathan; p. 0199-0201 - The cognitive intelligence platform 102 inserts first set of follow-up questions in the workspace associated with the originating question. The follow up questions are based on the identified parameters (attribute of the user), which in turn are based on the specifics of the originating question (e.g., related to an identified micro theory)… After identifying the first set of follow up questions, in this example first round of analysis, the cognitive intelligence platform 102 determines which follow up question can be answered using available data and which follow-up question to present to the user).
As per claim 12, Nathan discloses: The information processing method according to claim 6, wherein a computer further executes processing of causing the AI agent to determine whether the voice interaction is in a chat phase or in a consultation phase, and, when determining that the voice interaction is in the consultation phase, gives a question or a response to the user (Nathan; p. 0245 - FIGS. 9A-9B illustrate aspects of a conversational stream, in accordance with various embodiments. In particular, FIG. 9A displays an example conversational stream between a user and the cognitive agent 110. The screen shot 902 is an example of a dialogue that unfolds between a user and the cognitive agent 110, after the user has registered with the cognitive intelligence platform 102 (chat phase). In the screen shot 902, the cognitive agent 110 begins by stating “Welcome, would you like to watch a video to help you better understand my capabilities” (element 904). The cognitive agent provides an option to watch the video (element 906). In response, the user inputs text “that’s quite impressive” (element 908). In various embodiments, the user inputs text using the input box 916, which instructs the user to “Talk to me or type your question”. (consultation phase); see also p. 0182 for general examples of how a “consultation phase“ may be conducted through a conversation between the user and the cognitive agent, via voice, to have technical discussions focused on specific topics related to health).
As per claim 13, Nathan discloses:
The information processing method according to claim 6, wherein a computer further executes processing of causing the AI agent to give a response for making a check to the user when a certainty factor of semantic analysis on an utterance content of the user is low or the plan information greatly changes (Nathan; p. 0212-0213 - In the example illustrated in FIG. 4, in response to receiving the originating questions (line 402), the cognitive intelligence platform 102 (e.g., the cognitive agent 110 in conjunction with the critical thinking engine 108) parses the originating question (semantic analysis) to determine at least one parameter: location. The cognitive intelligence platform 102 categorizes this parameter, and a corresponding dynamically formulated question in the second set of follow-up questions. Accordingly, in lines 404 and 406, the cognitive agent 110 responds by notifying the user “I can certainly check this...” and asking the dynamically formulated question “I need some additional information in order to answer this question, was this an in-home glucose test or was it done by a lab or testing service?”).
As per claim 15, Nathan discloses: The information processing method according to claim 1, wherein the displaying comprises displaying a history of updates in a timeline in response to the updating of the plan information (Nathan; p. 0616-0617 - FIG. 66 shows an example of providing a user interface 6600 that provides dynamic charting and personalization of a care plan in real-time or near real-time, in accordance with various embodiments. The user interface 6600 may be generated and provided by the cognitive intelligence platform 102 to a computing device of a medical personnel. As depicted, the medical personnel may enter natural language patient notes in a section of the user interface 6600 designated for charting for the patient. The medical personnel entered “Performed a blood glucose test for Mr. Jones”. The patient notes or patient data may be transmitted to the cognitive intelligence platform 102 and the cognitive intelligence platform 102 may perform intelligent charting in real-time or near real-time by analyzing the patient data in view of a patient graph for the patient and Diabetes and a knowledge graph for Diabetes. The cognitive intelligence platform 102 may identify a medical code for the blood glucose test (“12345”) using metadata included in a node representing the blood glucose test in the knowledge graph for Diabetes, in a lookup table, or the like. The cognitive intelligence platform 102 may also identify other medical codes for other tests that may be performed for the medical condition (Diabetes) for which the patient is visiting the medical personnel. The medical codes may be presented in conjunction together on the user interface 6600. For example, the user interface 6600 presents, in another section, “Code: 12345 - Blood Glucose Test / Other Codes for Tests for Diabetes: 9876 -A1 c test”. Based on the dynamic charting and identification of another test to perform for the patient for Diabetes, the cognitive intelligence platform 102 may update a patient graph and may modify the care plan for the patient. As depicted, in yet another section of the user interface 6600, the user interface 6600 presents “Modified Care Plan: A blood glucose test has been completed for Mr. Jones. / Perform an A1c test next.” The statement “Perform an Alc test next” represents an action instruction that is personalized for the patient based on the patient graph for Diabetes for the patient in view of the changes that result from the dynamic charting in real-time or near real-time. To that end, it should be understood that the modified care plan may be generated and presented on the user interface 6600 in real-time or near real-time; p. 0575 - The patient graph may include elements (e.g., health artifacts) and branches representing relationships between the elements. The elements may be represented as nodes in the patient graph. The elements may represent interactions and/or actions the user has had and/or performed pertaining to the condition (history of updates)).
As per claim 16, Nathan discloses: The information processing method according to claim 15, wherein a computer further executes processing of calculating a score for the ideal plan of the plan information that has been updated, and the score calculated in the timeline is displayed in the display processing (Nathan; p. 0616-0617 - FIG. 66 shows an example of providing a user interface 6600 that provides dynamic charting and personalization of a care plan in real-time or near real-time, in accordance with various embodiments. The user interface 6600 may be generated and provided by the cognitive intelligence platform 102 to a computing device of a medical personnel. As depicted, the medical personnel may enter natural language patient notes in a section of the user interface 6600 designated for charting for the patient. The medical personnel entered “Performed a blood glucose test for Mr. Jones”. The patient notes or patient data may be transmitted to the cognitive intelligence platform 102 and the cognitive intelligence platform 102 may perform intelligent charting in real-time or near real-time by analyzing the patient data in view of a patient graph for the patient and Diabetes and a knowledge graph for Diabetes. The cognitive intelligence platform 102 may identify a medical code for the blood glucose test (“12345”) using metadata included in a node representing the blood glucose test in the knowledge graph for Diabetes, in a lookup table, or the like. The cognitive intelligence platform 102 may also identify other medical codes for other tests that may be performed for the medical condition (Diabetes) for which the patient is visiting the medical personnel. The medical codes may be presented in conjunction together on the user interface 6600. For example, the user interface 6600 presents, in another section, “Code: 12345 - Blood Glucose Test / Other Codes for Tests for Diabetes: 9876 -A1 c test”. Based on the dynamic charting and identification of another test to perform for the patient for Diabetes, the cognitive intelligence platform 102 may update a patient graph and may modify the care plan for the patient. As depicted, in yet another section of the user interface 6600, the user interface 6600 presents “Modified Care Plan: A blood glucose test has been completed for Mr. Jones. / Perform an A1c test next.” The statement “Perform an Alc test next” represents an action instruction that is personalized for the patient based on the patient graph for Diabetes for the patient in view of the changes that result from the dynamic charting in real-time or near real-time. To that end, it should be understood that the modified care plan may be generated and presented on the user interface 6600 in real-time or near real-time).
As per claim 17, Nathan discloses: The information processing method according to claim 16, wherein the score is graphically displayed in the display processing (Nathan; p. 0616-0617 - FIG. 66 shows an example of providing a user interface 6600 that provides dynamic charting and personalization of a care plan in real-time or near real-time, in accordance with various embodiments. The user interface 6600 may be generated and provided by the cognitive intelligence platform 102 to a computing device of a medical personnel. As depicted, the medical personnel may enter natural language patient notes in a section of the user interface 6600 designated for charting for the patient. The medical personnel entered “Performed a blood glucose test for Mr. Jones”. The patient notes or patient data may be transmitted to the cognitive intelligence platform 102 and the cognitive intelligence platform 102 may perform intelligent charting in real-time or near real-time by analyzing the patient data in view of a patient graph for the patient and Diabetes and a knowledge graph for Diabetes. The cognitive intelligence platform 102 may identify a medical code for the blood glucose test (“12345”) using metadata included in a node representing the blood glucose test in the knowledge graph for Diabetes, in a lookup table, or the like. The cognitive intelligence platform 102 may also identify other medical codes for other tests that may be performed for the medical condition (Diabetes) for which the patient is visiting the medical personnel. The medical codes may be presented in conjunction together on the user interface 6600. For example, the user interface 6600 presents, in another section, “Code: 12345 - Blood Glucose Test / Other Codes for Tests for Diabetes: 9876 -A1 c test”. Based on the dynamic charting and identification of another test to perform for the patient for Diabetes, the cognitive intelligence platform 102 may update a patient graph and may modify the care plan for the patient. As depicted, in yet another section of the user interface 6600, the user interface 6600 presents “Modified Care Plan: A blood glucose test has been completed for Mr. Jones. / Perform an A1c test next.” The statement “Perform an Alc test next” represents an action instruction that is personalized for the patient based on the patient graph for Diabetes for the patient in view of the changes that result from the dynamic charting in real-time or near real-time. To that end, it should be understood that the modified care plan may be generated and presented on the user interface 6600 in real-time or near real-time).
As per claim 18, Nathan discloses:
The information processing method according to claim 1, wherein the voice interaction is created between the user and a person in charge (Nathan; p. 0092-0093 - Accordingly, some embodiments of the present disclosure address the issues of reviewing the EMRs, by cognifying unstructured data. Unstructured data may include patient notes entered into one or more EMRs by a physician. The patient notes may explain symptoms described by the patient or detected by the physician, vital signs, recommended treatment, risks, prior health conditions, familial health history, and the like. The patient notes may include numerous strings of characters arranged into sentences. The sentences may be organized in one or more paragraphs. The sentences may be parsed and indicia may be identified (unstructured data representing interaction between patient and doctor)).
As per claim 19, Nathan discloses: An information processing device comprising: an acquisition unit that acquires information on reaction of a user, basic information on the user, and an ideal plan of the user in consultation on a future plan of the user through a voice interaction (Nathan; Fig. 9B, items 920 & 922; p. 0247-0249 - the cognitive agent states: “Congrats on taking the first step toward better health! Based upon your interest, I have some recommended health improvement initiatives for you to consider,” and presents the health improvement initiatives; see also Fig. 60A, item 6002; p. 0507 - The cognitive intelligence platform 102 provided a care plan 6002 that was originally generated for the patient for a medical condition of the patient. The care plan 6002 may include an action instruction pertaining to the medical condition of the user 6000, such as an instruction to read certain recommended content for the medical condition, schedule an appointment with a physician, perform a certain test for the medical condition, etc; see also p. 0511 - If the cognitive intelligence platform 102 receives the input data 6010, 6012, and/or 6014 when the care plan 6002 is presented to the user 6000 on the user device 104, and the cognitive intelligence platform 102 detects a negative emotion (e.g., angry) and/or tone (e.g., hostile), the cognitive intelligence platform 102 may modify the care plan 6002 to generate an updated care plan 6020; see also Fig. 14, item 1408; p. 0258 – audio input interface; see also p. 0559 - cognitive intelligence platform 102 may process a video recording using a machine learning model that has been trained to identify patterns between images of certain facial expressions, certain body language, certain emotions (e.g., happy, angry, sad, etc.), certain tones of voice, and/or the like); a generation unit that generates plan information representing the future plan based on the basic information and the ideal plan which have been acquired (Nathan; Fig. 9B, items 920 & 922; p. 0247-0249 - the cognitive agent states: “Congrats on taking the first step toward better health! Based upon your interest, I have some recommended health improvement initiatives for you to consider,” and presents the health improvement initiatives; see also Fig. 60A, item 6002; p. 0507 - The cognitive intelligence platform 102 provided a care plan 6002 that was originally generated for the patient for a medical condition of the patient. The care plan 6002 may include an action instruction pertaining to the medical condition of the user 6000, such as an instruction to read certain recommended content for the medical condition, schedule an appointment with a physician, perform a certain test for the medical condition, etc); and an update unit that corrects the future plan and updates the plan information in accordance with the information on reaction to the plan information that has been generated(Nathan; Fig. 9B, items 924 (reaction of the user), 928 & 930 (updated care plan); p. 0184 - The cognitive intelligence platform 102 relates actions, the sequences of subsequent actions (and reactions), desired sub-outcomes, and outcomes, in a way that is transparent and logical (e.g., explainable). The cognitive intelligence platform 102 can plot a next best action sequence and a planning basis (e.g., health care plan template, or a financial goal achievement template), also in a manner that is explainable (updating plan information); see also Fig. 60A-E, 61 & 62; p. 0507-0538 – [0507] FIG. 60A-E show examples of modifying a care plan based on a detected emotion of the patient, a detected tone of the patient, a different medical outcome entered by a physician… [0511] If the cognitive intelligence platform 102 receives the input data 6010, 6012, and/or 6014 when the care plan 6002 is presented to the user 6000 on the user device 104, and the cognitive intelligence platform 102 detects a negative emotion (e.g., angry) and/or tone (e.g., hostile), the cognitive intelligence platform 102 may modify the care plan 6002 to generate an updated care plan 6020… [0513] the cognitive intelligence platform 102 may track the detected conditions and/or tones of the users in reaction to care plans that are presented on the user device 104… if the detected emotion (e.g., happy) and/or tone (e.g., cheerful) is positive, the cognitive intelligence platform 102 may modify the care plan to generate an updated care plan 6020. The updated care plan 6020 may include a different subset of health artifacts than the care plan 6002…).
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Nathan in view of Reece (US PG Pub 20210264921).
As per claim 2, Nathan discloses: The information processing method according to claim 1, upon which claim 2 depends. Although Nathan discloses the processing of video recordings to generate emotion (reaction) data by tracking facial features of the patient, such as disclosed in p. 0559, Nathan fails to disclose wherein the information on reaction relates to a line of sight of the user. Reece does teach wherein the information on reaction relates to a line of sight of the user (Reece; p. 0058 - Video processing component 346 can also extract other conversation features from the video data, such as facial expressions, body postures or gestures, eye gaze directions, etc; see also p. 0067 - the conversation features can include an embedding of the audio, video, or textual versions of the audio, tone, sound level, emotional characteristics (e.g., supportive, agreeable, combative, engaged, enthusiastic, passionate, uncertainty, etc.), effectiveness ratings, physical reactions or movements (e.g., eye gaze directions, participant postures, participant gestures, participant head positions, laughter, nodding, facial expressions, etc.)). Therefore, it would have been obvious to one of ordinary skill in the art to modify the method of Nathan to include wherein the information on reaction relates to a line of sight of the user, as taught by Reece, in order to provide conversation analysis indicators using acoustic, video, and text data of a multiparty coaching conversation that may occur via videoconference. The rich acoustic/video data generated by the videoconference can be analyzed to quantify the underlying coaching relationship, and guide further developments in the relationship. For example, the effectiveness of the coach may be identified, and further effectiveness of scores may be determined to identify areas for improvement. Additionally, the mentee may be evaluated. For example, the communication skills of the mentee can be evaluated by performing computational methods (e.g., neural network based analysis) on the acoustic/video data. Tracking these conversation analysis indicators throughout an individual conversation, and across multiple conversations, may encourage skill development in the coach and the mentee (Reece; p. 0033-0035).
As per claim 14, Nathan discloses: The information processing method according to claim 6, upon which claim 14 depends. Nathan, however, fails to disclose wherein a computer further executes processing of identifying a decision maker in accordance with a number of utterances contributing to the plan information when there is a plurality of users, and the future plan is corrected in accordance with an utterance content of the decision maker who has been identified in the processing of the updating. Reece does teach wherein a computer further executes processing of identifying a decision maker in accordance with a number of utterances contributing to the plan information when there is a plurality of users (Reece; p. 0064-0067 - …block 402 includes recording a multi-participant coaching conversation (e.g., a coaching conversation between a coach and a mentee)… The segmented utterances from block 404 can be passed to annotator 405. Annotator 405 can identify conversation features through interfaces where humans identify the conversation features, algorithms for identifying conversation features, and/or machine learning modules trained to identify conversation features… For example, the conversation features can include an embedding of the audio, video, or textual versions of the audio, tone, sound level, emotional characteristics (e.g., supportive, agreeable, combative, engaged, enthusiastic, passionate, uncertainty, etc.), effectiveness ratings, physical reactions or movements (e.g., eye gaze directions, participant postures, participant gestures, participant head positions, laughter, nodding, facial expressions, etc.); and/or identify particular significant phrases or word choices (e.g., mm-hmm, yes, yah, oh my god, huh, uhh, etc.), segment turn length, gaps or delays in speaking, sentence length, topic choice, who is choosing the topics (decision maker), shared knowledge, mistakes or self-corrections, active listening, use of humor, participant biometrics, etc.; see also p. 0161 - … turn length, conversation percentage (e.g., percent of total conversation during which a speaker was active)…), and the future plan is corrected in accordance with an utterance content of the decision maker who has been identified in the processing of the updating (Reece; p. 0186 - In response to the conversation analysis indicators, process 2100 updates the user profiles associated with the underlying conversation. In the illustrated implementation, process 2100 updates a mentee profile and a coach profile. More specifically, process 2100 includes determining an updated progress score for a mentee based on the conversation analysis indicators and determining a performance score for a coach based on the conversation analysis indicators. The progress score defines a user's progress in professional development as defined by goals set by the mentee, coach, or a third party. The performance score can signify a coach's effectiveness in increasing the progress score of her mentees).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the method of Nathan to include wherein a computer further executes processing of identifying a decision maker in accordance with a number of utterances contributing to the plan information when there is a plurality of users, and the future plan is corrected in accordance with an utterance content of the decision maker who has been identified in the processing of the updating, as taught by Reece, in order to provide conversation analysis indicators using acoustic, video, and text data of a multiparty coaching conversation that may occur via videoconference. The rich acoustic/video data generated by the videoconference can be analyzed to quantify the underlying coaching relationship, and guide further developments in the relationship. For example, the effectiveness of the coach may be identified, and further effectiveness of scores may be determined to identify areas for improvement. Additionally, the mentee may be evaluated. For example, the communication skills of the mentee can be evaluated by performing computational methods (e.g., neural network based analysis) on the acoustic/video data. Tracking these conversation analysis indicators throughout an individual conversation, and across multiple conversations, may encourage skill development in the coach and the mentee (Reece; p. 0033-0035).
Claims 8, 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Nathan in view of Bereza (US PG Pub 20180321826).
As per claim 8, Nathan discloses:
The information processing method according to claim 7, upon which claim 8 depends. Nathan, however, fails to disclose wherein, in the processing of asking, a question is asked to the user based on a region to which a line of sight of the user is directed and one or a plurality of utterance contents of the user, which are the information on reaction. Bereza does teach wherein, in the processing of asking, a question is asked to the user based on a region to which a line of sight of the user is directed and one or a plurality of utterance contents of the user, which are the information on reaction (Bereza; p. 0502-0504 - Optionally, as depicted in FIG. 22F, before performing the action from the voice command, device 2200 updates photo viewing interface 2203 to include visual indication 2240 that identifies thumbnail 2211 as the item that user 2222 was looking at when providing voice command 2236. Confirmation prompt 2242 is also displayed asking the user to confirm that the action should be carried out on the identified content. If input selecting delete button 2244 is received, the identified content is deleted and photo viewing interface 2203 is updated (e.g., updated to show that the photo associated with thumbnail 2211 has been deleted by no longer displaying thumbnail 2211, rearranging the remaining thumbnails, and displaying any additional thumbnail to fill the space created by removal of thumbnail 2211); see also p. 0511 - the electronic device prompts (2312) the user to provide clarification).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the method of Nathan to include wherein, in the processing of asking, a question is asked to the user based on a region to which a line of sight of the user is directed and one or a plurality of utterance contents of the user, which are the information on reaction, as taught by Bereza, in order to provide faster, more efficient methods and interfaces for using image data to enhance user interactions, thereby increasing the effectiveness, efficiency, and user satisfaction with such devices (Bereza; p. 0028).
As per claim 10, Nathan discloses:
The information processing method according to claim 6, upon which claim 10 depends. Nathan, however, fails to disclose wherein a computer further executes processing of causing the AI agent to give a response in accordance with a region and a content of a question when the user asks the question about the region to which a line of sight of the user for the plan information is directed.
Bereza does teach wherein a computer further executes processing of causing the AI agent to give a response in accordance with a region and a content of a question when the user asks the question about the region to which a line of sight of the user for the plan information is directed (Bereza; p. 0502-0504 - FIG. 22E depicts another example of device 2200 receiving a voice command (e.g., voice command 2236). Like voice command 2224 (FIG. 22B), voice command 2236 (“Delete it.”) specifies a requested function (“delete”) (although the example does not explicitly show the request of the function in the form of a question, one of ordinary skill in the art would find it obvious to provide a request in the form of a question, such as “can you please delete it?”) but is ambiguous about what item the function should be performed with or on. Upon receiving voice command 2236, device 2200 analyzes captured image data to determine an item at which user 2222 was looking when the voice command 2236 was received (e.g., an item on the display of device 2200 that direction 2238 indicates user 2222 was looking at). If device 2200 determines that user 2222 was looking at thumbnail 2211 based on captured image data of user 2222, device 2200 deletes the photo associated with thumbnail 2211 and updates photo viewing interface to remove thumbnail 2211).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the method of Nathan to include wherein a computer further executes processing of causing the AI agent to give a response in accordance with a region and a content of a question when the user asks the question about the region to which a line of sight of the user for the plan information is directed, as taught by Bereza, in order to provide faster, more efficient methods and interfaces for using image data to enhance user interactions, thereby increasing the effectiveness, efficiency, and user satisfaction with such devices (Bereza; p. 0028).
As per claim 11, Nathan discloses:
The information processing method according to claim 6, upon which claim 11 depends. Nathan, however, fails to disclose wherein a computer further executes processing of causing the AI agent to ask the user a question regarding correction of the future plan about the region to which a line of sight of the user for the plan information is directed, and the future plan is corrected in accordance with a response of the user and the plan information is updated in the processing of the updating. Bereza does teach wherein a computer further executes processing of causing the AI agent to ask the user a question regarding correction of the future plan about the region to which a line of sight of the user for the plan information is directed, and the future plan is corrected in accordance with a response of the user and the plan information is updated in the processing of the updating (Bereza; p. 0502-0504 - Optionally, as depicted in FIG. 22F, before performing the action from the voice command, device 2200 updates photo viewing interface 2203 to include visual indication 2240 that identifies thumbnail 2211 as the item that user 2222 was looking at when providing voice command 2236. Confirmation prompt 2242 is also displayed asking the user to confirm that the action should be carried out on the identified content. If input selecting delete button 2244 is received, the identified content is deleted and photo viewing interface 2203 is updated (e.g., updated to show that the photo associated with thumbnail 2211 has been deleted by no longer displaying thumbnail 2211, rearranging the remaining thumbnails, and displaying any additional thumbnail to fill the space created by removal of thumbnail 2211); see also p. 0511 - the electronic device prompts (2312) the user to provide clarification).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the method of Nathan to include wherein a computer further executes processing of causing the AI agent to ask the user a question regarding correction of the future plan about the region to which a line of sight of the user for the plan information is directed, and the future plan is corrected in accordance with a response of the user and the plan information is updated in the processing of the updating, as taught by Bereza, in order to provide faster, more efficient methods and interfaces for using image data to enhance user interactions, thereby increasing the effectiveness, efficiency, and user satisfaction with such devices (Bereza; p. 0028).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon includes:
Kumar (US PG Pub 20170300637) discloses mechanisms… for implementing a personalized patient care plan (PPCP) system. The PPCP system obtains personal and medical information about the patient and generates a patient registry record in a patient registry. The PPCP system generates a PPCP for the patient, comprising a sequence of goals for the patient, based on an analysis of the obtained personal and medical information. Each goal has an associated patient action to be performed by the patient. The PPCP system monitors performance of the goals to determine, for each goal, whether the associated patient action is performed by the patient. In response to determining that the patient did not perform an associated patient action, the PPCP system modifies a goal of the personalized patient care plan to replace the associated patient action with a replacement patient action that is more likely to be performed by the patient (Kumar; Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rodrigo A Chavez whose telephone number is (571)270-0139. The examiner can normally be reached Monday - Friday 9-6 ET.
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/RODRIGO A CHAVEZ/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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