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 .
DETAILED ACTION
This action is in response to the application filed 20 October 2023. Claims 1-9 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 October 2023 is being considered by the examiner.
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.
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 limitations use 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: "a calculation process for calculating a score indicative of a degree to which a target patient is in an anomaly state, with use of a calculation model that uses target data obtained from the target patient as an input to output the score" and "a decision process for deciding a threshold on the basis of the target data with use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state" in Claims 1 and 9.
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 this/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 it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 of the instant application is provisionally rejected on the ground of non-statutory double patenting as being unpatentable over Claim 1 of co-pending Application No. 18/382,128 (reference application) in view of Barsoum, et al. (US 2014/0279754 A1, hereinafter "Barsoum").
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1 of the instant application differs with respect to Claim 1 of the reference application as indicated by the underlined limitations of the following listing:
18/382,149 (instant)
18/382,128 (reference)
1
A state determination apparatus comprising at least one processor, the at least one processor carrying out:
1
A state determination apparatus comprising at least one processor, the at least one processor carrying out:
1
a calculation process for calculating a score indicative of a degree to which a target patient is in an anomaly state, with use of a calculation model that uses target data obtained from the target patient as an input to output the score, the calculation model being generated by semi-supervised learning using a training data group obtained at a predetermined hospital;
1
a calculation process for calculating a score indicative of a degree to which a target patient is in an anomaly state, with use of a calculation model that uses target data obtained from the target patient as an input to output the score, the calculation model being generated by semi-supervised learning using a training data group obtained at a predetermined hospital in a
predetermined period;
1
a decision process for deciding a threshold on the basis of the target data with use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state, the at least one prediction model each being a model generated by supervised learning using a training data group obtained at a hospital different from the predetermined hospital; and
1
a decision process for deciding a threshold on the basis of the target data with use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state, the at least one prediction model each being a model generated by supervised learning using a training data group obtained at the predetermined hospital in another period different from the predetermined period; and
1
a determination process for determining, by comparing the score and the threshold, whether the target patient is in the anomaly state.
1
a determination process for determining, by comparing the score and the threshold, whether the target patient is in the anomaly state.
The limitations in a predetermined period and at the predetermined hospital in another period different from the predetermined period from reference application 18/382,128 are given patentable weight, but they have been interpreted not to provide further limitation to the description of the source of the training data due to the inherency of a predetermined time period for obtained data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a training data group obtained at a hospital different from the predetermined hospital (Barsoum, [0022]: "The sources of data 68 can include for example, an electronic health record (EHR) database as well any other sources of patient data" and [0023]: "The patient data in the sources of data 68 can represent information for a plurality of different categories. ... Additionally, the patient data utilized in generated a model can include International Classification of Diseases (ICD) codes ... anatomic correlations and the like. These and other codes, which may vary depending on location or care giver affiliations, thus can be utilized to represent data elements in the active problem list for a given patient encounter").
The motivation to do so would be to facilitate use of patient data that comprises representations of medical conditions varying by location (Barsoum, [0023]: "The patient data in the sources of data 68 can represent information for a plurality of different categories. ... These and other codes, which may vary depending on location or care giver affiliations, thus can be utilized to represent data elements in the active problem list for a given patient encounter. Thus, the data extractor 66 can extract data relevant to any one or more of the categories of patient data from the sources of data 68").
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1
Claim 1 recites a state determination apparatus comprising at least one processor, and thus the claimed machine falls within a statutory category of invention.
Step 2A Prong 1
The claim recites a calculation process for calculating a score indicative of a degree to which a target ... is in an anomaly state, which is a mental process. The claim recites a decision process for deciding a threshold on the basis of the target data, which is a mental process. The claim recites a determination process for determining, by comparing the score and the threshold, whether the target ... is in the anomaly state, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a target patient does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element use of a calculation model that uses target data obtained from the target ... as an input to output the score invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the calculation model being generated by semi-supervised learning using a training data group does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element a training data group obtained at a predetermined hospital does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the at least one prediction model each being a model generated by supervised learning using a training data group does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element a training data group obtained at a hospital different from the predetermined hospital does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 2
Step 1
Regarding Claim 2, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
Claim 2 recites the abstract ideas recited by parent Claim 1.
Step 2A Prong 2, Step 2B
The additional element a training data group obtained at a predetermined hospital (as recited by Claim 1), wherein the training data group obtained at the predetermined hospital includes (i) training data that are assigned labels indicative of whether the target patient is in the anomaly state and (ii) training data that are not assigned the labels does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 3
Step 1
Regarding Claim 3, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites the calculated value that is output by each of the at least one prediction model is a prediction probability obtained by predicting a probability of the target ... being in the anomaly state, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a target patient does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 4
Step 1
Regarding Claim 4, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites decides the threshold on the basis of a value obtained by assigning weights to calculated values, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state (as recited by Claim 1), the at least one prediction model that is used in the decision process comprises a plurality of prediction models invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element in the decision process, the at least one processor decides invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element values that are output by the respective plurality of prediction models invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 5
Step 1
Regarding Claim 5, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites at least one of the plurality of prediction models is generated with use of an algorithm that is different from an algorithm with use of which at least one other of the plurality of prediction models is generated, which is a mental process. The claim recites at least one of the plurality of prediction models is ... generated with use of an algorithm which is identical between the at least one of the plurality of prediction models and the at least one other of the plurality of prediction models and to which a parameter that is different from a parameter of the at least one other of the plurality of prediction models is applied, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state (as recited by Claim 1), the at least one prediction model that is used in the decision process comprises a plurality of prediction models invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 6
Step 1
Regarding Claim 5, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites at least one of the plurality of prediction models is generated with use of a training data group that is different from a training data group with use of which at least one other of the plurality of prediction models is generated, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state (as recited by Claim 1), the at least one prediction model that is used in the decision process comprises a plurality of prediction models invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 7
Step 1
Regarding Claim 7, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites outputting ... a method of responding to the target ... for optimization of an action ... , the method being determined on the basis of the result of determination, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element the target patient does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element an action of a medical professional does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element the at least one processor further carries out an output process for outputting ... a result of determination by the determination process invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 8
Step 1
Claim 8 recites a determination method, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites calculating a score indicative of a degree to which a target ... is in an anomaly state, which is a mental process. The claim recites deciding a threshold on the basis of the target data, which is a mental process. The claim recites determining, by comparing the score and the threshold, whether the target ... is in the anomaly state, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a target patient does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element use of a calculation model that uses target data obtained from the target ... as an input to output the score invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the calculation model being generated by semi-supervised learning using a training data group does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element a training data group obtained at a predetermined hospital does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the at least one prediction model each being a model generated by supervised learning using a training data group does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element a training data group obtained at a hospital different from the predetermined hospital does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element (a), (b), and (c) each being carried out by at least one processor invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 9
Step 1
Claim 9 recites a non-transitory storage medium storing therein a program for causing a computer to carry out, and thus the claimed manufacture falls within a statutory category of invention.
Step 2A Prong 1
The claim recites a calculation process for calculating a score indicative of a degree to which a target ... is in an anomaly state, which is a mental process. The claim recites a decision process for deciding a threshold on the basis of the target data, which is a mental process. The claim recites a determination process for determining, by comparing the score and the threshold, whether the target ... is in the anomaly state, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a target patient does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element use of a calculation model that uses target data obtained from the target ... as an input to output the score invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the calculation model being generated by semi-supervised learning using a training data group does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element a training data group obtained at a predetermined hospital does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element use of at least one prediction model each of which uses the target data as an input to output a calculated value related to the anomaly state invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the at least one prediction model each being a model generated by supervised learning using a training data group does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element using a training data group obtained at a hospital different from the predetermined hospital does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over He, et al. (US 2022/0172255 A1, hereinafter "He") in view of Wexler, et al. (US 2025/0062031 A1, hereinafter "Wexler") in view of Barsoum, et al. (US 2014/0279754 A1, hereinafter "Barsoum").
Regarding Claim 1, He teaches:
A state determination (He, Fig. 6, step 628: "determine eligibility of homes") apparatus comprising at least one processor (He, [0029]: "The confidence-boosted automated valuation system 300 can include ... the multi-model processor 266 ... described in relation to FIG. 2"), the at least one processor carrying out:
a calculation process for calculating a score indicative of a degree to which a target ... is in an anomaly state (He, [0018]: "Using the predicted values, the confidence-boosted automated valuation system can learn from the errors in the predicted values (e.g., how different the predicted values are from actual values of homes) made by the models. By learning from past errors, the confidence-boosted automated valuation system can predict certainties or confidences for the predicted value of a home in the form of a confidence score and/or a predicted pricing error distribution" and [0074]: "By using an error threshold, process 600 can ... reduce the number of outlier predicted values," where He's predicted confidence corresponds to the instant calculated score of degree and He's identified outlier model predictions corresponds to the instant in an anomaly state), with use of a calculation model (He, [0030]: "The predicted values of homes can be generated by one or more automated valuation models") that uses target data obtained from the target ... as an input (He, [0041]: "At act 404, process 400 accesses the home data and/or the model data of the set of homes. In particular, process 400 can access a home data store and/or model data store to obtain the home data and/or the model data, respectively, for each particular home in the identified set of homes") to output the score (He, Fig. 6, step 608, "generate model inputs" and 614, "generate confidence scored by applying trained confidence model to model inputs," where He's home-value calculation model generates the inputs for the confidence model), the calculation model being generated by semi-supervised learning (He, [0025]: "The one or more machine learning models can include supervised learning models, unsupervised learning models, semi-supervised learning models") ...;
a decision process for deciding a threshold on the basis of the target data (He, [0074]: "At act 632, process 600 accesses/obtains an error threshold. ... In various implementations, the error threshold can be adjusted such that only a top percentage of fraction (e.g., 25%, 1/2) of the subject homes get selected as confident homes or are deemed to have confident home values") with use of at least one prediction model (He, [0065]: "The predicted values of the one or more subject homes can be generated by the one or more automated valuation models described in relation to FIG. 3") each of which uses the target data as an input to output a calculated value related to the anomaly state (He, [0066]: "At act 608, process 600 generates model inputs, for a confidence model to be applied at act 610, using the accessed/obtained predicted values, the home data, and/or the model data of the one or more subject homes. In particular, process 600 can generate/create model input using the predicted value, the home data, and/or the model data for each subject home in the identified one or more subject homes"), the at least one prediction model each being a model generated by supervised learning (He, [0025]: "The one or more machine learning models can include supervised learning models, unsupervised learning models, semi-supervised learning models") ...; and
a determination process for determining, by comparing the score and the threshold, whether the target ... is in the anomaly state (He, [0038]: "the home eligibility filter 268 can filter the subject homes by whether the confidence scores or predicted errors exceed a predefined or empirically determined threshold and identify the remaining unfiltered subject homes (used herein as 'confident homes' or 'eligible homes') as those with confident predicted values (used herein as 'confident home values')" and [0074]: "By using an error threshold, process 600 can filter out subject homes with extreme predicted values and reduce the number of outlier predicted values").
He teaches a state determination apparatus carrying out a calculation process for calculating an anomaly-degree using a calculation model generated by semi-supervised learning, a decision process for deciding a threshold using a prediction model generated by supervised learning to output a calculated anomaly value, and a determination process for determining whether the target is in the anomaly state by comparing the score and the threshold.
He does not explicitly teach a score indicative of a degree to which a target patient is in an anomaly state, target data obtained from the target patient as an input, and determining ... whether the target patient is in the anomaly state.
However, Wexler teaches:
a score indicative of a degree to which (Wexler, [0030]: "the system 102 can be or include a forecasting and/or analysis engine, and/or a server, which can be collectively configured to generate predictions of a user's health metrics" and [0071]: "that there are many measures of accuracy. ... The model score can be generated and can correspond to a relative reduction in root mean square error... This measure will equal 1 if the model prediction has zero error, and will equal zero if the model prediction has the same error as the naive prediction") a target patient is in an anomaly state (Wexler, [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). ... [T]he notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake, etc.)," where Wexler's user health metrics corresponds to the instant target patient, and where Wexler's recommended remediations correspond to the instant anomaly state)
target data obtained from the target patient as an input (Wexler, Fig., 5, Step 510, "Receive health data of a user")
determining ... (Wexler, [0030]: "the system 102 can be or include a forecasting and/or analysis engine, and/or a server, which can be collectively configured to generate predictions of a user's health metrics") whether the target patient is in the anomaly state (Wexler, [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). ... [T]he notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake, etc.)," where Wexler's remediations for a health factor correspond to the instant anomaly state).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of He regarding a state determination apparatus carrying out a calculation process for calculating an anomaly-degree using a calculation model generated by semi-supervised learning, a decision process for deciding a threshold using a prediction model generated by supervised learning to output a calculated anomaly value, and a determination process for determining whether the target is in the anomaly state by comparing the score and the threshold with those of Wexler regarding a score indicative of a degree to which a target patient is in an anomaly state, target data obtained from the target patient as an input, and determining whether the target patient is in the anomaly state.
The motivation to do so would be to facilitate training of models that predict and forecast patient health more accurately according to patient health metrics (Wexler, [0106]: "if the method 500 determines that a certain health factor is particularly influential ... the notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake; etc.). The recommendation can be customized to the particular user, e.g., based on user feedback, behavioral patterns, etc." and [0107]: "the approaches described herein provide improved accuracy in predicting various health metrics (e.g., 30-day time-in-range, 30-day average blood glucose values, 30-day average blood pressure values, weight, etc.) compared to conventional techniques. ... The enhanced accuracy can assist users in monitoring and managing their health conditions, as well as in making decisions regarding lifestyle changes and/or other actions to improve their health").
The He/Wexler combination teaches a calculation model generated by semi-supervised learning using a training data group.
The He/Wexler combination does not explicitly teach using a training data group obtained at a predetermined hospital and using a training data group obtained at a hospital different from the predetermined hospital.
However, Barsoum teaches:
using a training data group obtained at a predetermined hospital (Barsoum, [0020]: "FIG. 2 illustrates one example of a self-evolving system 50 that can be used for predicting patient outcomes .... The predicted patient outcomes can include, for example, patient length of stay, ... readmission rate, ... or any other patient outcome information that may be relevant to a ... healthcare facility" and [0021]: "Each model 51-62 can be trained on a training set of existing patient data to derive the associated set of model parameters, and validated against a test set of patient data to determine an associated accuracy" and [0038]: "the model is updated according to the set of predictors and the measured value. In one implementation, the set of predictors and the measured value are incorporated into a training set of data used to retrain the model")
using a training data group obtained at a hospital different from the predetermined hospital (Barsoum, [0022]: "The sources of data 68 can include for example, an electronic health record (EHR) database as well any other sources of patient data" and [0023]: "The patient data in the sources of data 68 can represent information for a plurality of different categories. ... Additionally, the patient data utilized in generated a model can include International Classification of Diseases (ICD) codes ... anatomic correlations and the like. These and other codes, which may vary depending on location or care giver affiliations, thus can be utilized to represent data elements in the active problem list for a given patient encounter").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the He/Wexler combination regarding a calculation model generated by semi-supervised learning using a training data group with those of Barsoum regarding using a training data group obtained at a predetermined hospital.
The motivation to do so would be to facilitate use of the model that is appropriate to a given hospital facility (Barsoum, [0038]: "the set of predictors, the predicted value of the clinical parameter and the measured value can be used as part of a test set of data to update and refine the accuracy associated with the model. By consistently updating the model in response to new patient outcomes, the model can remain accurate in the face of changes in the composition of the patient population, advances in relevant technology, and other changes in treatment and care").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the He/Wexler combination regarding a prediction model generated by supervised learning using a training data group with those of Barsoum regarding using a training data group obtained at a hospital different from the predetermined hospital.
The motivation to do so would be to facilitate use of patient data that comprises representations of medical conditions varying by location (Barsoum, [0023]: "The patient data in the sources of data 68 can represent information for a plurality of different categories. ... These and other codes, which may vary depending on location or care giver affiliations, thus can be utilized to represent data elements in the active problem list for a given patient encounter. Thus, the data extractor 66 can extract data relevant to any one or more of the categories of patient data from the sources of data 68").
Regarding Claim 8, He teaches:
A determination method (He, Claim 20: "A method for performing confident processing of disparate valuations from distributed automated valuation models") comprising:
(a) calculating a score indicative of a degree to which a target ... is in an anomaly state (He, [0018]: "Using the predicted values, the confidence-boosted automated valuation system can learn from the errors in the predicted values (e.g., how different the predicted values are from actual values of homes) made by the models. By learning from past errors, the confidence-boosted automated valuation system can predict certainties or confidences for the predicted value of a home in the form of a confidence score and/or a predicted pricing error distribution" and [0074]: "By using an error threshold, process 600 can ... reduce the number of outlier predicted values," where He's predicted confidence corresponds to the instant calculated score of degree and He's identified outlier model predictions corresponds to the instant in an anomaly state), with use of a calculation model (He, [0030]: "The predicted values of homes can be generated by one or more automated valuation models") that uses target data obtained from the target ...as an input (He, [0041]: "At act 404, process 400 accesses the home data and/or the model data of the set of homes. In particular, process 400 can access a home data store and/or model data store to obtain the home data and/or the model data, respectively, for each particular home in the identified set of homes") to output the score (He, Fig. 6, step 608, "generate model inputs" and 614, "generate confidence scored by applying trained confidence model to model inputs," where He's home-value calculation model generates the inputs for the confidence model), the calculation model being generated by semi-supervised learning (He, [0025]: "The one or more machine learning models can include supervised learning models, unsupervised learning models, semi-supervised learning models") ...;
(b) deciding a threshold on the basis of the target data (He, [0074]: "At act 632, process 600 accesses/obtains an error threshold. ... In various implementations, the error threshold can be adjusted such that only a top percentage of fraction (e.g., 25%, 1/2) of the subject homes get selected as confident homes or are deemed to have confident home values") with use of at least one prediction model (He, [0065]: "The predicted values of the one or more subject homes can be generated by the one or more automated valuation models described in relation to FIG. 3") each of which uses the target data as an input to output a calculated value related to the anomaly state (He, [0066]: "At act 608, process 600 generates model inputs, for a confidence model to be applied at act 610, using the accessed/obtained predicted values, the home data, and/or the model data of the one or more subject homes. In particular, process 600 can generate/create model input using the predicted value, the home data, and/or the model data for each subject home in the identified one or more subject homes"), the at least one prediction model each being a model generated by supervised learning (He, [0025]: "The one or more machine learning models can include supervised learning models, unsupervised learning models, semi-supervised learning models") ...; and
(c) determining, by comparing the score and the threshold, whether the target ... is in the anomaly state (He, [0038]: "the home eligibility filter 268 can filter the subject homes by whether the confidence scores or predicted errors exceed a predefined or empirically determined threshold and identify the remaining unfiltered subject homes (used herein as 'confident homes' or 'eligible homes') as those with confident predicted values (used herein as 'confident home values')" and [0074]: "By using an error threshold, process 600 can filter out subject homes with extreme predicted values and reduce the number of outlier predicted values"),
(a), (b), and (c) each being carried out by at least one processor (He, [0029]: "The confidence-boosted automated valuation system 300 can include ... the multi-model processor 266 ... described in relation to FIG. 2").
He teaches a state determination apparatus carrying out a calculation process for calculating an anomaly-degree using a calculation model generated by semi-supervised learning, a decision process for deciding a threshold using a prediction model generated by supervised learning to output a calculated anomaly value, and a determination process for determining whether the target is in the anomaly state by comparing the score and the threshold.
He does not explicitly teach a score indicative of a degree to which a target patient is in an anomaly state, target data obtained from the target patient as an input, and determining ... whether the target patient is in the anomaly state.
However, Wexler teaches:
a score indicative of a degree to which (Wexler, [0030]: "the system 102 can be or include a forecasting and/or analysis engine, and/or a server, which can be collectively configured to generate predictions of a user's health metrics" and [0071]: "that there are many measures of accuracy. ... The model score can be generated and can correspond to a relative reduction in root mean square error... This measure will equal 1 if the model prediction has zero error, and will equal zero if the model prediction has the same error as the naive prediction") a target patient is in an anomaly state (Wexler, [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). ... [T]he notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake, etc.)," where Wexler's user health metrics corresponds to the instant target patient, and where Wexler's recommended remediations correspond to the instant anomaly state)
target data obtained from the target patient as an input (Wexler, Fig., 5, Step 510, "Receive health data of a user")
determining ... (Wexler, [0030]: "the system 102 can be or include a forecasting and/or analysis engine, and/or a server, which can be collectively configured to generate predictions of a user's health metrics") whether the target patient is in the anomaly state (Wexler, [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). ... [T]he notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake, etc.)," where Wexler's remediations for a health factor correspond to the instant anomaly state).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of He regarding a state determination apparatus carrying out a calculation process for calculating an anomaly-degree using a calculation model generated by semi-supervised learning, a decision process for deciding a threshold using a prediction model generated by supervised learning to output a calculated anomaly value, and a determination process for determining whether the target is in the anomaly state by comparing the score and the threshold with those of Wexler regarding a score indicative of a degree to which a target patient is in an anomaly state, target data obtained from the target patient as an input, and determining whether the target patient is in the anomaly state.
The motivation to do so would be to facilitate training of models that predict and forecast patient health more accurately according to patient health metrics (Wexler, [0106]: "if the method 500 determines that a certain health factor is particularly influential ... the notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake; etc.). The recommendation can be customized to the particular user, e.g., based on user feedback, behavioral patterns, etc." and [0107]: "the approaches described herein provide improved accuracy in predicting various health metrics (e.g., 30-day time-in-range, 30-day average blood glucose values, 30-day average blood pressure values, weight, etc.) compared to conventional techniques. ... The enhanced accuracy can assist users in monitoring and managing their health conditions, as well as in making decisions regarding lifestyle changes and/or other actions to improve their health").
The He/Wexler combination teaches a calculation model generated by semi-supervised learning using a training data group obtained in a predetermined period.
The He/Wexler combination does not explicitly teach using a training data group obtained at a predetermined hospital and using a training data group obtained at a hospital different from the predetermined hospital.
However, Barsoum teaches:
using a training data group obtained at a predetermined hospital (Barsoum, [0020]: "FIG. 2 illustrates one example of a self-evolving system 50 that can be used for predicting patient outcomes .... The predicted patient outcomes can include, for example, patient length of stay, ... readmission rate, ... or any other patient outcome information that may be relevant to a ... healthcare facility" and [0021]: "Each model 51-62 can be trained on a training set of existing patient data to derive the associated set of model parameters, and validated against a test set of patient data to determine an associated accuracy" and [0038]: "the model is updated according to the set of predictors and the measured value. In one implementation, the set of predictors and the measured value are incorporated into a training set of data used to retrain the model")
using a training data group obtained at a hospital different from the predetermined hospital (Barsoum, [0022]: "The sources of data 68 can include for example, an electronic health record (EHR) database as well any other sources of patient data" and [0023]: "The patient data in the sources of data 68 can represent information for a plurality of different categories. ... Additionally, the patient data utilized in generated a model can include International Classification of Diseases (ICD) codes ... anatomic correlations and the like. These and other codes, which may vary depending on location or care giver affiliations, thus can be utilized to represent data elements in the active problem list for a given patient encounter").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the He/Wexler combination regarding a calculation model generated by semi-supervised learning using a training data group with those of Barsoum regarding using a training data group obtained at a predetermined hospital.
The motivation to do so would be to facilitate use of the model that is appropriate to a given hospital facility (Barsoum, [0038]: "the set of predictors, the predicted value of the clinical parameter and the measured value can be used as part of a test set of data to update and refine the accuracy associated with the model. By consistently updating the model in response to new patient outcomes, the model can remain accurate in the face of changes in the composition of the patient population, advances in relevant technology, and other changes in treatment and care").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the He/Wexler/Barsoum combination regarding a prediction model generated by supervised learning using a training data group with those of Barsoum regarding using a training data group obtained at a hospital different from the predetermined hospital.
The motivation to do so would be to facilitate use of patient data that comprises representations of medical conditions varying by location (Barsoum, [0023]: "The patient data in the sources of data 68 can represent information for a plurality of different categories. ... These and other codes, which may vary depending on location or care giver affiliations, thus can be utilized to represent data elements in the active problem list for a given patient encounter. Thus, the data extractor 66 can extract data relevant to any one or more of the categories of patient data from the sources of data 68").
Regarding Claim 9, He teaches:
A non-transitory storage medium storing therein a program (He, Claim 19: "At least one non-transitory, computer-readable medium carrying instructions, which when executed by at least one data processor, performs operations" and [0021]: "computer-readable media drives 104 (e.g., at least one non-transitory computer-readable medium) that are tangible storage means ... for reading programs and data stored on a computer-readable medium") for causing a computer to carry out: the processes recited by Claim 1. Claim 8 is rejected under the same rationale as Claim 1.
Regarding Claim 2, the rejection of Claim 1 is incorporated. The He/Wexler/Barsoum combination has been shown to teach:
wherein the training data group obtained at the predetermined hospital (as recited in the rejection of Claim 1, Barsoum, [0020]: "FIG. 2 illustrates one example of a self-evolving system 50 that can be used for predicting patient outcomes .... The predicted patient outcomes can include, for example, patient length of stay, ... readmission rate, ... or any other patient outcome information that may be relevant to a ... healthcare facility") includes (i) training data that are assigned labels indicative of whether the target ... is in the anomaly state and (ii) training data that are not assigned the labels (as recited in the rejection of Claim 1, He, [0025]: "The one or more machine learning models can include supervised learning models, unsupervised learning models, semi-supervised learning models") ... the target patient is in the anomaly state (as recited in the rejection of Claim 1, Wexler, [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). ... [T]he notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake, etc.)," where Wexler's remediations for a health factor correspond to the instant anomaly state).
Regarding Claim 3, the rejection of Claim 1 is incorporated. The He/Wexler/Barsoum combination teaches:
wherein the calculated value that is output by each of the at least one prediction model is a prediction probability obtained by predicting a probability of the target ... being in the anomaly state (He, [0067]: "At act 614, process 600 generates confidence scores by applying the obtained confidence model, trained to produce confidence scores, to the obtained predicted values, home data, and/or model data of the one or more subject homes. ... The confidence model can input the model inputs and generate/output a confidence score for each of the one or more subject homes associated with the model inputs" and [0058]: "process 500 can, for each confidence bin, compute a distribution for the confidence scores of the subset of homes in the confidence bin. In some implementations, the distribution can be a frequency distribution or probability distribution, and the confidence score, which is an error value, can be the random variable described by the distribution").
The He/Wexler/Barsoum combination has been shown to teach:
the target patient being in the anomaly state (as recited in the rejection of Claim 1, Wexler, [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). ... [T]he notification can inform the user with recommended actions with respect to that health factor (e.g., decreasing blood glucose levels; increasing physical activity; improving sleep patterns; altering dietary intake, etc.)," where Wexler's user health metrics corresponds to the instant target patient, and where Wexler's recommended remediations correspond to the instant anomaly state).
Regarding Claim 4, the rejection of Claim 1 is incorporated. The He/Wexler/Barsoum combination teaches:
wherein the at least one prediction model that is used in the decision process comprises a plurality of prediction models (He, [0085]: "the n-th confidence score can be generated from an ensemble of the first and second confidence models"), and
in the decision process, the at least one processor decides the threshold on the basis of a value (He, [0074]: "The top subject homes ranked by predicted error can also have a much smaller median/average absolute percent error, standard deviation, or variance than the other subject homes or all of the one or more subject homes as a whole. By using an error threshold, process 600 can filter out subject homes with extreme predicted values and reduce the number of outlier predicted values that get offered to sellers/ buyers," which reasonably suggests that He's error threshold is decided on the basis of an aggregate confidence value, such as variance) obtained by assigning weights to calculated values that are output by the respective plurality of prediction models (He, [0085]: "the n-th confidence score can be generated from an ensemble of the first and second confidence models" and Fig. 7, 720, "synthesize values and confidences," where He's synthesize may be a weighted average, as in [0073]: "process 600 can determine the predicted error in the predicted value of a subject home to be a synthetization ( e.g., mean, weighted mean, median, or weighted median) of the computed error distribution or converted error distribution associated with the subject home").
Regarding Claim 5, the rejection of Claim 1 is incorporated. The He/Wexler/Barsoum combination teaches:
wherein the at least one prediction model that is used in the decision process comprises a plurality of prediction models (He, [0085]: "the n-th confidence score can be generated from an ensemble of the first and second confidence models"), and
at least one of the plurality of prediction models is generated with use of an algorithm that is different from an algorithm with use of which at least one other of the plurality of prediction models is generated, or generated with use of an algorithm which is identical between the at least one of the plurality of prediction models and the at least one other of the plurality of prediction models and to which a parameter that is different from a parameter of the at least one other of the plurality of prediction models is applied (He explicitly teaches the first alternative of different algorithms at [0081]: "the confidence score usually is algorithmic specific ( e.g., pertains specifically to each of the individual first, second, and n-th confidence models) and each confidence model (e.g., the first, second, and n-th models) may have different patterns of distortion due to each confidence model's intrinsic limitations" and Fig. 4, step 412, "train confidence model," and sub-step 416, "fit confidence model to model inputs and update model parameters," which reasonably suggests that He's confidence model training uses a different algorithm for fitting each model to model inputs).
Regarding Claim 6, the rejection of Claim 1 is incorporated. The He/Wexler/Barsoum combination teaches:
wherein the at least one prediction model that is used in the decision process comprises a plurality of prediction models (He, [0085]: "the n-th confidence score can be generated from an ensemble of the first and second confidence models"), and
at least one of the plurality of prediction models is generated with use of a training data group that is different from a training data group with use of which at least one other of the plurality of prediction models is generated (He, [0081]: "each confidence model (e.g., the first, second, and n-th models) may have different patterns of distortion due to each confidence model's intrinsic limitations and training data imbalance. As a result, the confidence scores generated by the confidence models (e.g., the first, second, and n-th confidence scores) may not be directly comparable and a calibration step may be needed").
Regarding Claim 7, the rejection of Claim 1 is incorporated. The He/Wexler/Barsoum combination teaches:
the at least one processor further carries out an output process for outputting (i) a result of determination by the determination process (He, Fig. 7, 724, "provide updated values or confidences," and [0085]: "the confidence model can select a most confident predicted value after classifying the most accurate predicted value amongst the first, second, and n-th predicted values and triaging based on generated probabilities for each of the predicted values from the first, second, and n-th automated valuation models. Process 700 can provide the most confident predicted value as an updated predicted value of a most confident home at act 724").
Wexler further teaches:
the at least one processor further carries out an output process for outputting ... a method of responding to the target patient for optimization of an action of a medical professional, the method being determined on the basis of the result of determination (Wexler, [0030]: "The system 102 can be configured to perform input, determination, analysis, forecasting, and/or interpretation of a user's health metrics.... The system 102 can also be configured to output ... recommendations ... to the user based on the predicted health metrics" and [0044]: "The users can be individual users (e.g., patients, healthcare professionals, etc.)" and [0106]: "The method 500 can optionally include determining a recommended action for the user to improve their health metrics, based on the prediction and/or contributing health factor(s). For example, if the method 500 determines that a certain health factor is particularly influential in causing the user to achieve or not achieve their health goal, the notification can inform the user with recommended actions with respect to that health factor," which reasonably suggests that the system can be used by a health professional to make improved recommendations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the He/Wexler/Barsoum combination regarding the at least one processor further carries out an output process for outputting a result of determination by the determination process with the further teachings of Wexler regarding the at least one processor further carries out an output process for outputting a method of responding to the target for optimization of an action of a medical professional, the method being determined on the basis of the result of determination.
The motivation to do so would be to facilitate use of automated decision support for providing recommendations to improve longer-term health goals (Wexler, [0025]: "Treatment of diseases and/or conditions such as high blood pressure, diabetes or prediabetes, etc., may prevent or mitigate detrimental effects on the person's health and/or reduce the risk of developing more serious medical conditions. ... However, it may take weeks or months before meaningful changes become evident (e.g., based on heart function data, etc.), and individuals may lose motivation if they are uncertain whether their lifestyle changes are having any meaningful impact on their health. Accordingly, there is a need for improved systems and methods that forecast future changes in the user's health metrics and/or provide explanations for changes in the health metrics to guide users in reaching their health goals" and [0045]: "the system 102 processes and uses the data provided by the user to produce automated decision support").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT N DAY whose telephone number is (703)756-1519. The examiner can normally be reached M-F 9-5.
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/R.N.D./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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