DETAILED ACTION
This action is made in response to the request for continued examination filed November 14, 2025. Claims 1, 6, 9-10, 12-14, 16, and 21-22 are pending. Claims 2-5, 7, 11, 15, 17, 18 and 19-20 have been previously cancelled. Claim 8 is presently cancelled. Claims 1, 9, and 16 have been amended. Claims 1, 9, and 16 are independent claims. This action is made non-final.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 21, 2025 has been entered.
Response to Arguments
Applicant’s arguments filed January 21, 2025 have been fully considered, but are not persuasive.
Applicant argues the claims are not directed to an abstract idea (see pages 12-13 of Applicant’s Remarks). However, the examiner respectfully disagrees.
As previously stated, and again below, the claims recite a method/system of determining and transmitting medical-related recommendations, which falls under methods of organizing human activity, including managing personal behavior/relationships/interactions between people such as following rules or instructions. The limitations of “receiving patient data associated with a user profile, the user profile including at least a medical history of a patient associated with the user profile; receiving medical professional data associated with a medical professional profile, the medical professional profile including at least historical records associated with a medical professional; analyzing, the user profile; analyzing, the medical professional profile; determining, based at least in part on analyzing the user profile, a recommendation to the medical professional; determining, based at least in part on the recommendation, data to be transmitted with the recommendation”, are steps that can be achieved by managing human interactions (i.e., a series of rules or steps performed by a human or humans to output a recommendation). Similarly, the limitations of “receiving user input, the user input including feedback that indicates which supporting evidence was used to determine a recommendation is accurate; determining at least one of a word, a phrase, or an expression based at least in part on analyzing the feedback; and determining a statistical relevance of data…based at least in part on at least one of the word, the phrase, or the expression”, are a series of rules or steps performed by a human to determine relevance of a recommendation and is, therefore, a method of organizing human activity. This interpretation is not limited to those that can be performed in the human mind, but can be conducted by an individual using a generic computer. Applicant is further reminded that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5 and MPEP 2106.04(a)(2)(II)).
The identified claim elements represent a series of rules or instructions that a person, with or without the aid of a computer, would follow to determine the relevance of various recommendation in deciding whether or not to make the recommendation. Furthermore, the Examiner submits that healthcare itself inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization but that the operations cannot be performed by the human mind. However, as addressed above, methods of organizing human activity can include a person’s interaction with a computer. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to determine the source of a recommendation when deciding whether or not to forward the recommendation, the claimed invention is directed to an abstract idea.
Applicant further argues the claims are integrated into a practical application. Applicant specific state the claims recite “novel functionality for determination of data needed to cause electronic devices to perform actions”. And further states the steps of analyzing a user provide and a medical professional profile to determine a statistical relevance of data utilized for determining a recommendation and determining how likely it is for a medical professional to utilize the data in association with the recommendation (as well as transmitting the recommendation and data to a remote device) integrate the claims into a practical application. However, the examiner respectfully disagrees.
Notably, Applicant makes conclusory statements that are unsupported by factual evidence. Rather, Applicant merely recites the claim elements and makes naked assertions that the corresponding limitations integrate the claim into a practical application. As previously stated, the aforementioned steps were directed to an abstract idea as steps that can be achieved by managing human interactions (i.e., a series of rules or steps performed by a human or humans to output a recommendation with or without interaction on a computer).
Furthermore, the novelty of a claim is of no relevance when determining subject matter eligibility. “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." (See MPEP 2106.05).
Insomuch as Applicant argues the training and use of a specific machine learning model is analogous to Example 39 of the Subject Matter Eligibility examples and patent eligible, the argument is unpersuasive. As a first matter, the present claims fail to recite any type of training, much less any details as to the specific type of learning model being used. Rather, it is recited at a high levels of generality and is amounts as tool to apply the abstract idea to a generic, unspecified algorithms/techniques and output the results (similar to “apply it"). The specification does not provide any detail as to the type of machine learning techniques or how the machine learning models specifically arrive at their solution. The claim recites a result-oriented solution and lacks details as to how the computer arrives at the solution and/or represent a commonplace mathematical algorithm being applied on a general-purpose computer. (see MPEP 2106.05(f), instructions to apply an abstract idea (“apply it”) cannot provide a practical application). See also updated guidance on subject matter eligibility, including AI, July 2024.
Furthermore, contrary to Applicant’s assertions, Example 39 provides no indication of any limitation that removes the claims from an abstract idea, but merely states the claim does not recite any judicial exception. Example 39 is distinguishable from the present case as the claims recite a method of training a neural network for facial detection and includes numerous limitations including collecting digital images, applying various transformations to the digital images to create modified images, creating training data of the modified images, training the neural network, creating a second training set including the first training set and incorrectly detected images, and further training the neural network using the second training set, none of which are present in the instant case. Rather, as previously stated, and again below, the claims recite a method for determining and transmitting a medical-related recommendation. But for the noted computer elements, this claim encompasses a person following a series of rules or instructions in the manner described in the abstract idea, and thus constitutes “certain methods of organizing human activity”. Applicant is further encouraged to review the updated subject matter eligibility guidance, including AI, provided in July 2024. In the updated guidance, it is noted that the training of machine learning can reasonably fall within one of the groupings of abstract ideas and, furthermore, the use of the trained machines, when recited at high levels of generality, provide nothing more than mere instructions to implement the abstract idea on a generic computer. Accordingly, the claim recites an abstract idea.
Applicant argues the claims recite a “non-conventional and non-generic arrangement” of features that amount to an inventive concept and specific technical solution. Applicant states the claim limitations amount to significantly more and specifically states “determining that particular types of data may be less likely to be utilized by a medical professional may result in less data transmission and/or more efficient transmission of data more likely to be utilized by the medical professional”. However, the examiner respectfully disagrees. Selecting a smaller data set over another is not an improvement of the computer, technology, or technical field nor is it a specific technical solution, but rather is merely a selection of a particular data over another. Furthermore, there is no certainty that any such improvement is realized, as Applicant even acknowledges that it may result in less data transmission. As such, the mere selection of particular data over another is not considered to be a non-conventional or non-generic arrangement that would amount to significantly more.
Insomuch as Applicant argues the examiner fails to establish the claim elements constitute “well-understood, routine, and conventional activities”, the examiner respectfully disagrees. The only identified additional elements included conventional computer components that performed generic computer functions (e.g., see MPEP 2106.05(d)). Specifically, the “one or more processors”, “non-transitory computer-readable media”, and a “remote device” were all described with a high level of generality. Furthermore, Applicant’s originally filed specification (see Specification Figs. 1, 2, [0009], [0026], [0027]) described the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements.
Accordingly, for at least the above stated reasons, the previous 101 rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 6, 9, 10, 12-14, 16, 21, and 22 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.
Claims 1, 6, 21, and 22 recite a system for determining a medical-related recommendation, which is within the statutory category of a machine. Claims 9, 10, 12-14 recite a method for determining a medical-related recommendation, which is within the statutory category of a process. Claim 16 recites a system for determining a medical-related recommendation, which is within the statutory category of a machine.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1, 6, 9, 10, 12-14, 16, 21, and 22, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claims 1, 9, and 16 (claim 1 being representative)
one or more processors; and non-transitory computer-readable media storing first computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving patient data associated with a user profile, the user profile including at least a medical history of a patient associated with the user profile; receiving medical professional data associated with a medical professional profile, the medical professional profile including at least historical records associated with a medical professional; analyzing, using one or more machine learning techniques, the user profile; analyzing, using the one or more machine learning techniques, the medical professional profile; determining, based at least in part on analyzing the user profile and an output obtained from the one or more machine learning techniques, a recommendation to the medical professional, the recommendation including at least one of a potential diagnosis, a gap in medical coverage, or a medication-related recommendation; generating a graphical user interface (GUI) configured to display on a computing device; causing the GUI to be displayed via a display of the computing device; receiving user input via the display, the user input including feedback that indicates which supporting evidence was used to determine a recommendation is accurate; determining at least one of a word, a phrase, or an expression based at least in part on analyzing the feedback;
determining a statistical relevance of first data utilized for determining the recommendation based at least in part on at least one of the word, the phrase, or the expression, wherein determining the statistical relevance of the first data is based at least in part on a degree of change that the first data has on a confidence score associated with the recommendation; generating an input for one or more machine learning techniques based at least in part on the statistical relevance of data utilized for determining the recommendation; receiving a data output from the one or more machine learning techniques based at least in part on the input; determining a likelihood that the medical professional will utilize the first data in association with the recommendation, the likelihood being determined based at least in part on the statistical relevance of the first data output and the historical records associated with the medical professional and determining that the medical professional has utilized previous data that is associated with the first data, wherein the historical records associated with the medical professional include at least one previous recommendation utilized by the medical professional; ranking at least one of the potential diagnosis, the gap in medical coverage, or the recommended medication based at least in part on the likelihood that the medical professional will utilize the recommendation;
transmitting the recommendation and the first data to a remote device associated with the medical professional, wherein the recommendation is transmitted based at least in part on the ranking; determining that a first portion of the first data is more relevant than a second portion of the first data; generating second data including the second portion of the first data, the second data including content that, when displayed, includes at least an emphasized portion, the emphasized portion including at least one of highlighting the second data, bolding the second data, italicizing the second data, or underlining the second data; and causing the remote device to display the second data and the emphasized portion.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components.. For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to receive, analyze, and process data in the manner described in the abstract idea such as a medical provider analyzing patient data and generating a recommendation wherein they further emphasize relevant data for the recommendation. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“one or more processors”, “non-transitory computer-readable media”, “graphical user interface”, “computing device”, “display”, and a “remote device” —all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claim further recites the additional elements of using one or more machine learning techniques. When given the broadest reasonable interpretation in light of the nonexistent description of the machine learning models in the disclosure, machine learning techniques with the noted data amounts to a mathematical concept that creates data associations. As such, these techniques are interpreted to be subsumed within the identified abstract idea and the use of the machine learning techniques provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. The use of the machine learning techniques provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
Furthermore, the interface including a first section and a selectable portion which causes a second section to present data generally links the claimed invention to a particular technological environment or field of use. (See MPEP 2106.05(h) & MPEP 2106.05(A), generally linking the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more). Additionally, displaying the data is recognized as insignificant extra-solution activity (See MPEP 2106.05(g), recognizing mere data gathering does not integrated into a practical application).
The claims only manipulate abstract data elements as part of performing the abstract idea. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“one or more processors”, “non-transitory computer-readable media”, “graphical user interface”, “computing device”, “display”, and a “remote device””—see Specification Figs. 1, 2, [0009], [0020], [0026], [0027] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements using one or more machine learning techniques was considered to “apply it”. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. The use of the machine learning techniques represented saying “apply it” and has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. Furthermore, the prior art of record indicates that using one or more machine leaning techniques configured to access one or more databases containing data regarding patients and analyze their patient profiles is well-understood, routine, and conventional in the field (See U.S. Patent Application Publication No. 20150216413 A1 to Soyao at Para. [0231]; U.S. Patent Application Publication No. 20180043076 to Gerber at Para. [0119]). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). (See MPEP 2106.05(I)(A) indicating that well-understood, routine, and conventional activities cannot provide significantly more).
Furthermore, the interface including a first section and a selectable portion which causes a second section to present data generally links the claimed invention to a particular technological environment or field of use. (See MPEP 2106.05(h) & MPEP 2106.05(A), generally linking the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more). Additionally, displaying the data is recognized as insignificant extra-solution activity (See MPEP 2106.05(g), recognizing mere data gathering does not integrated into a practical application). Notably, the Specification [0021] indicates that a user interface having sections and selectable elements are well-understood, routine, and conventional in the field.
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. CLAIMS 6, 10, 12-14, and 21-22 merely provide information about the independent claims, and therefore only serve to further limit the abstract idea of claims 1, 9 and 16. CLAIM 6 describes the data types used to determine the recommendation. CLAIMS 10, 21 & 22 describe the types of data included in the recommendation.
The dependent claims inherit all of the limitations of the independent claims and further define the abstract idea identified for the independent claims and/or recite field of use limitations. These steps are consistent with the types of ideas found to be methods of organizing human activity. Therefore, the descriptions in 6, 10, 12-14, and 21-22 are abstract ideas and do not contain additional elements for consideration
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 9, 10, 12-14, 16, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berdia (USPPN: 2014/0136230; hereinafter Berdia) in further view of Kohli (USPPN: 2019/0156921; hereinafter Kohli).
As to claim 1, Berdia teaches A system (e.g., see Title) comprising:
one or more processors (e.g., see Figs. 1, 2, [0053]); and
non-transitory computer-readable media storing first computer-executable instructions that, when executed by the one or more processors (e.g., see Figs. 1, 2, [0051]), cause the one or more processors to perform operations comprising:
receiving patient data associated with a user profile, the user profile including at least a medical history of a patient associated with the user profile (e.g., see [0052], [0064] wherein patient data, including their medical history, is retrieved);
receiving medical professional data associated with a medical professional profile, the medical professional profile including at least historical records associated with a medical professional (e.g., see [0052], [0078] wherein doctor/care provider information, including their historical interactions, is retrieved);
analyzing, using one or more machine learning techniques, the user profile (e.g., see [0068] teaching various analytical processes and artificial intelligence methods to analyze the patient data);
analyzing, using the one or more machine learning techniques, the medical professional profile (e.g., see [0071], [0073] teaching various analytical processes and artificial intelligence methods to analyze the doctor’s preferences);
determining, based at least in part on analyzing the user profile and an output obtained from the one or more machine learning techniques, a recommendation to the medical professional, the recommendation including at least one of a potential diagnosis, a gap in medical coverage, or a medication-related recommendation (e.g., see [0015], [0068], [0069], [0073] wherein based on the patient data and machine learning output, ranked diagnoses and treatments are provided);
generating a graphical user interface (GUI) configured to display on a computing device (e.g., see Figs. 8-23 showing various user interface displays);
causing the GUI to be displayed via a display of the computing device (e.g., see Figs. 8-23 showing various user interface displays);
receiving user input via the display, the user input including feedback that indicates which supporting evidence was used to determine a recommendation is accurate (e.g., see [0071]-[0073], [0075], [0078], [0118] wherein a doctor/provider can accept or change the provided recommendation based pertinent information such as an age of the patient);
generating an input for one or more machine learning techniques based at least in part on the statistical relevance of data utilized for determining the recommendation (e.g., see [0073], [0118], [0138] wherein inputted data can be repeatedly updated/changed based on the confidence in a determined probability of the recommendation);
receiving a data output from the one or more machine learning techniques based at least in part on the input e.g., see [0073], [0118], [0138] wherein the new input is provided to machine learning models/algorithms to output recommendations);
determining a likelihood that the medical professional will utilize the first data in association with the recommendation, the likelihood being determined based at least in part on the statistical relevance of the first data output and the historical records associated with the medical professional and determining that the medical professional has utilized previous data that is associated with the first data, wherein the historical records associated with the medical professional include at least one previous recommendation utilized by the medical professional (e.g., see [0071], [0078], [0138], [0140] wherein the system learns the providers preferences and changes various weights of some factors to increase the probability of the recommendation based on the specific provider’s knowledge base);
ranking at least one of the potential diagnosis, the gap in medical coverage, or the recommended medication based at least in part on the likelihood that the medical professional will utilize the recommendation (e.g., see [0073] wherein the output of recommendations is in ranked order);
transmitting the recommendation and the first data to a remote device associated with the medical professional, wherein the recommendation is transmitted based at least in part on the ranking (e.g., see [0053], [0073], [0074] teaching transmitting the ranked recommendations through remotely accessible devices).
While Berdia teaches modifying weights on different factors based on the provider’s input so that future recommendations more closely reflect the provider’s recommendation, Berdia fails to explicitly teach determining at least one of a word, a phrase, or an expression based at least in part on analyzing the feedback; determining a statistical relevance of first data utilized for determining the recommendation based at least in part on at least one of the word, the phrase, or the expression, wherein determining the statistical relevance of the first data is based at least in part on a degree of change that the first data has on a confidence score associated with the recommendation; determining that a first portion of the first data is more relevant than a second portion of the first data;
generating second data including the second portion of the first data, the second data including content that, when displayed, includes at least an emphasized portion, the emphasized portion including at least one of highlighting the second data, bolding the second data, italicizing the second data, or underlining the second data; and causing the remote device to display the second data and the emphasized portion.
However, in the same field of endeavor of determining relevant information, Kohli teaches determining at least one of a word, a phrase, or an expression based at least in part on analyzing the feedback (e.g., see [0032], [0042]-[0044] wherein relevant terms, context, etc., can be learned from user feedback); determining a statistical relevance of first data utilized for determining the recommendation based at least in part on at least one of the word, the phrase, or the expression, wherein determining the statistical relevance of the first data is based at least in part on a degree of change that the first data has on a confidence score associated with the recommendation (e.g., see [0117], [0120], [0143]-[0147], [0156] teaching identifying relevant terms based upon the continuously changing data and it’s similarly/dissimilarity to clinical scenarios in order to generated improved interpretations/recommendations); determining that a first portion of the first data is more relevant than a second portion of the first data (e.g., see [0041], [0143], [0168] wherein concepts and/or other terminology can be identified by ranked relevancy);
generating second data including the second portion of the first data, the second data including content that, when displayed, includes at least an emphasized portion, the emphasized portion including at least one of highlighting the second data, bolding the second data, italicizing the second data, or underlining the second data (See 112 rejection above. e.g., see [0168] wherein concepts and/or other terminology can be highlighted/emphasized according to relevancy); and causing the remote device to display the second data and the emphasized portion (e.g., see [0147] wherein the emphasized text is displayed).
Accordingly, it would have been obvious to modify Berdia in view of Kohli before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification in order to improve the quality of information interpretation thereby improving patient care (e.g., see [0036] of Kohli).
As to claim 6, the rejection of claim 1 is incorporated. Berdia further teaches wherein the first data that was used to determine the recommendation includes at least one of a test result, medical history, personal information, or identifying information associated with a test results (e.g., see [0064] wherein the retrieved data includes medical history information, lab results, or other relevant information).
As to claim 9, the claim is directed to the method implemented on the system of claim 1 and is similarly rejected.
As to claim 10, the rejection of claim 9 is incorporated. Berdia further teaches wherein the recommendation includes, at least one of a potential diagnosis, a gap in medical coverage, or a medication related recommendation (e.g., see Abstract, [0011], [0014] wherein the recommendation includes diagnosis, treatment, other medical practice actions for treating, billing, and processing the patient).
As to claim 12, the rejection of claim 9 is incorporated. Berdia further teaches further comprising ranking the data based at least in part on the likelihood that the medical professional will utilize the recommendation, wherein the data is transmitted based at least in part on the ranking (e.g., see [0022], [0078], [0134] wherein the ranked output is based on the provider’s preferences).
As to claim 13, the rejection of claim 9 is incorporated. Berdia-Kohli further teaches wherein the statistical relevance of the data is based at least in part on a degree of change that the data has on a confidence score associated with the recommendation (e.g., see [0138] of Berdia wherein the data is based on a confidence in a determined probability of a diagnosis, wherein the system is continuously updated to learn the doctor’s preferences. See also [0107] of Kohli wherein the data is processed to calculate a similarity/dissimilarity to a clinical scenario and outputs based on score/ranking of relevancy).
As to claim 14, the rejection of claim 9 is incorporated. Berdia further teaches wherein determining the data includes determining that the medical professional has utilized previous data that is associated with the data (e.g., see [0052], [0073] wherein the determination is further based on the doctor’s historical interaction with the data).
As to claim 16, the claim is directed to a broader recitation of the system of claim 1 and is similarly rejected.
As to claim 21, the rejection of claim 1 is incorporated. Berdia further teaches wherein the recommendation includes a gap in medical coverage recommendation (It is noted that the claim language of the particular type of recommendation is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed system. See MPEP 2111.05. The function described in the claimed system would be performed the same regardless of whether the claimed type of recommendation existed. Therefore, Berdia, having taught providing a recommendation, including diagnosis, treatment, billing or processing the patient, Berdia teaches the claimed limitation (e.g., see also [0142] wherein insurance approvals are identified). Furthermore, it would have been obvious to substitute any type of recommendation as a simple substitution. As such, it would have been obvious before the effective date of the application to substitute the diagnosis, treatment, billing or processing the patient of the prior art with any type of recommendation because the results would have been predictable for recommending desired data. See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143).
As to claim 22, the rejection of claim 1 is incorporated. Berdia further teaches wherein the recommendation includes the medication-related recommendation (It is noted that the claim language of the particular type of recommendation is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed system. See MPEP 2111.05. The function described in the claimed system would be performed the same regardless of whether the claimed type of recommendation existed. Nonetheless, Berdia, teaches medication recommendations (e.g., see [0134]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STELLA HIGGS whose telephone number is (571)270-5891. The examiner can normally be reached Monday-Friday: 9-5PM.
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/STELLA HIGGS/Primary Examiner, Art Unit 3686