DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to communications filed on 11/24/2025.
Claims 2 and 12 have been canceled.
Claims 1, 3-11, and 13-20 are pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/24/2025 has been entered.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
As per claim 1, it appears that a comma should be inserted between “…datum” and “wherein creating…” in line 10. It appears that a comma should be inserted after “…the at least a user structure entry” in line 12. It appears that a comma should be inserted between “model” and “wherein” in line 15. It appears that “the at least a key word” in the 4th to last line should be replaced with “the at least a keyword” for consistency. The above similarly applies to claim 11.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 1, it is unclear whether “the at least one antidote” in the 16th to last line (also in the 13th to last line) is referring to the “at least one given antidote”, or is different. It is unclear whether “the at least a user input” in the 10th to last line (also the 6th to 8th to last line) is referring to the “at least a user input datum”, or is different. As such, the claim is indefinite. Independent claim 11 also recites the same limitations and therefore have the same problem. Due at least to their dependency upon claims 1 or 11, dependent claims 3-10 and 13-20 also are indefinite.
Response to Arguments
Applicant's arguments filed have been fully considered but they are not persuasive. Applicant argues that the newly amended features are not taught by the references. However, examiner respectfully disagrees. For example, Tavshikar teaches normalizing, which is another word for formatting (e.g. in column 4 lines 20-54 and column 9 line – column 10 line 4, “automated association of data in the data entry…into a given format… a feed parsing module 136, which may categorize and format data for use in corpus of [input/input datum], creation of training sets included in corpus of [input/input datum]… standardized form, including without limitation a fixed-length textual field form, a comma-separated value (CSV) form…standardized fields in associated variables, which may be interconnected via references, pointers, or inclusion in common data structures or hierarchies... feed parsing module 136 may tokenize or otherwise separate and analyze data that is in non-standard formats to sort it into such variables, data structures, and/or hierarchies of data structures”). See also rejections below.
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.
Claims 1, 3-4, 6-11, 13-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tavshikar (US 10661902 B1) in view of Prakash et al. (US 20160012194 A1) and Norman (US 20200118662 A1).
As per independent claim 1, Tavshikar teaches a system for relating user inputs to output datum labels using artificial intelligence, the system comprising:
at least a server comprising hardware (e.g. in column 3 lines 4-8, “At least a server 104 may include any computing device as described below in reference to FIG. 15, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described below in reference to FIG. 15”), the at least a server designed and configured to:
receive at least a user input datum, wherein the at least a server is further configured to receive the at least a user input datum (e.g. in column 4 lines 45-51, “in a data entry including some textual data, a person's name and/or a description of a medical condition or therapy may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms”);
create at least an unsupervised machine-learning model as a function of the at least a user input datum (e.g. in in column 4 lines 45-51, column 13 lines 23-27 and column 18 lines 15-46, “a person's name and/or a description of a medical condition or therapy… A machine learning process is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs [i.e. create ML model]… perform an unsupervised machine learning process on corpus of [datum], which may cluster data of corpus of [datum] according to detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other” and figure 11) wherein creating the at least an unsupervised machine-learning model further comprises: selecting at least a dataset as a function of the at least user input datum wherein the at least a dataset further comprises at least a datum of user input data and at least a correlated output datum (e.g. in column 4 lines 6-16 and column 4 lines 45-51, “mathematical relationship linking values belonging to the two categories… a person's name and/or a description of a medical condition or therapy may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms… detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other”); and generating the at least an unsupervised machine-learning model wherein generating the at least an unsupervised machine-learning model further comprises generating at least a clustering model to output at least a probing element containing at least a commonality label as a function of the at least user input datum and the at least a dataset (e.g. in column 4 lines 45-51, column 13 lines 23-27 and column 18 lines 15-46, “a person's name and/or a description of a medical condition or therapy… A machine learning process is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs [i.e. create ML model]… perform an unsupervised machine learning process on corpus of [datum], which may cluster data of corpus [i.e. probing element] of [datum] according to detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other [i.e. commonality label] … such relations may then be combined with supervised machine learning results”, i.e. clustering model, and figure 11); and select at least a first training set as a function of the at least a user input datum and the at least a first probing element containing the at least a commonality label (e.g. in column 4 lines 45-51, column 13 lines 23-27 and column 18 lines 15-46, “in a data entry including some textual data, a person's name and/or a description of a medical condition or therapy may be identified by reference to a list, dictionary, or other compendium of terms… cluster data of corpus [i.e. probing element] of [datum] according to detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other [i.e. commonality label] … such relations may then be combined with supervised machine learning results”, i.e. clustering model, and figure 11); and at least a label learner operating on the at least a server (e.g. in column 17 line 33-64, “machine-learning algorithms used by [label] learner 152 may include supervised machine-learning algorithms, which may, as a non-limiting example be executed using a supervised learning module 1100 executing on at least a server 104”), the at least a label learner designed and configured to: create at least a supervised machine-learning model as a function of the at least a first training set and the at least a commonality label (e.g. in column 17 line 33-64 and column 18 lines 15-46, “supervised machine-learning algorithms… include algorithms that receive a training set relating a number of inputs to a number of outputs… such relations [i.e. commonality labels] may then be combined with supervised machine learning results” and figure 11), wherein creating the at least a supervised machine-learning model further comprises: training the at least a supervised machine-learning model (e.g. in column 13 lines 23-27 and column 17 line 33-64, “A machine learning process is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs… supervised machine-learning algorithms”, i.e. create ML model) as a function of the first training set and a supervised learning algorithm, wherein the first training set comprises inputs comprising a plurality of user input datums and outputs comprising a plurality of output datums (e.g. in column 17 line 33-64 and column 18 lines 15-46, “supervised machine-learning algorithms, which may, as a non-limiting example be executed using a supervised learning module 1100 executing on at least a server… include algorithms that receive a training set relating a number of inputs to a number of outputs… such relations may then be combined with supervised machine learning results” and figure 11), wherein: the supervised learning algorithm uses elements of the plurality of user input datums and the plurality of antidotes in combination with a scoring function representing a form of relationship to be detected between the elements of the plurality of user input datums and the plurality of output datums (e.g. in column 17 line 33-64, “a supervised learning algorithm may use…inputs …outputs, and a scoring function representing a desired form of relationship to be detected between [the input datums] and [the output datums]”); and the scoring function is configured to maximize a probability that at least one given element of the plurality of user input datums is associated with at least one given output datum of the plurality of output datums (e.g. in column 17 line 33-64, “seek to maximize the probability that a given element of [the input datums] and/or combination of [the inputs] is associated with a given [output datum of the output datums]”); determine the at least an output datum using the at least a trained supervised machine-learning model, wherein the at least a user input datum is provided to the at least a trained supervised machine-learning model as an input to output the at least an output datum (e.g. in column 13 lines 15-27, “learner 148 designed and configured to determine [output] a function of the corpus of [input/input datum]”, and figure 11); at least a parsing module operating on the at least a server, wherein the parsing module is configured to: parse the at least a user input for at least a keyword, wherein the parsing module is configured to combine separately analyzed elements of the at least a user input to create the at least a keyword (e.g. in column 4 lines 20-54, “categories may be generated using correlation and/or other processing algorithms … an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person's name and/or a description of a medical condition or therapy may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data to be made applicable for two or more distinct machine-learning algorithms as described”); and wherein the parsing module is configured to normalize one or more textual data of the at least a user input by modifying the one or more textual data to match a predetermined form (e.g. in column 4 lines 20-54 and column 9 line – column 10 line 4, “automated association of data in the data entry…into a given format… a feed parsing module 136, which may categorize and format data for use in corpus of [input/input datum], creation of training sets included in corpus of [input/input datum]… standardized form, including without limitation a fixed-length textual field form, a comma-separated value (CSV) form…standardized fields in associated variables, which may be interconnected via references, pointers, or inclusion in common data structures or hierarchies... feed parsing module 136 may tokenize or otherwise separate and analyze data that is in non-standard formats to sort it into such variables, data structures, and/or hierarchies of data structures”, i.e. normalize);
select at least a dataset as a function of the at least a key word (e.g. in column 4 lines 20-54 and column 9 line – column 10 line 4, “in a data entry including some textual data…reference to a list, dictionary, or other compendium of terms… automated association of data in the data entry…into a given format… a feed parsing module 136, which may categorize and format data for use in corpus of [input/input datum], creation of training sets included in corpus of [input/input datum]”); and
a graphical user interface communicatively connected to the at least a server and configured to: display an output (e.g. in column 12 lines 10-30 and column 28 lines 37-44, “at least a server 104 may, as a non-limiting example, display… Graphical user interface may display [an output]… system 1500 may further include a video display adapter 1552 for communicating a displayable image to a display device”),
but does not specifically teach wherein the at least a user input datum/data further comprises at least a user structure entry/data, the user input datum including containing at least a tissue sample analysis comprising a report identifying hormone levels from a fluid-based sample, wherein the output datum(s) include antidote(s), and displaying an output including at least an antidote including a treatment recommendation to a user.
However, Prakash teaches at least a user input datum/data further comprising at least a user structure entry/data and output datum(s) including antidote(s) including an at least one antidote (e.g. in paragraphs 21-22, 33, 35, 54, and 70, “symptoms and other shared health status information… based upon symptoms or other diagnostic data collected over time, the system may…detect a problem requiring certain follow-up actions… personalized recommendations include… particular medications, nutritional supplements, food, or fitness schedules… analyzing and correlating user profiles, external data and general medical information… aspects of their health (heart rate, blood pressure, etc.) [i.e. user structure]… employs machine learning techniques (sometimes referred to herein as a “learning engine”) that are implemented by supervised methods such as various classifiers (e.g., nearest-neighbor, Bayesian, regression, support vector machines, decision trees, rule-based, artificial neural networks, etc.). In other embodiments, machine learning is implemented by unsupervised methods such as clustering”) and displaying an output comprising the at least an antidote including a treatment recommendation to a user (e.g. in paragraphs 32-33, 65, 67, 70, and 168, “provides an immediate response upon detecting a trend or concern based on information provided by a user… particular medications, nutritional supplements, food, or fitness schedules… based upon the specific symptoms they have already reported…possible treatments as well as recommendations for preventive or therapeutic actions (e.g., icing your knee, refraining from strenuous activity or using a particular drug or product”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tavshikar to include the teachings of Prakash because one of ordinary skill in the art would have recognized the benefit of allowing machine learning to aid the medical field,
but does not specifically teach the user input datum including containing at least a tissue sample analysis comprising a report identifying hormone levels from a fluid-based sample.
However, Norman teaches receiving at least a user input datum containing at least a tissue sample analysis comprising a report identifying hormone levels from a fluid-based sample (e.g. in paragraphs 8-9, 40-41, and 44, “To obtain data about a person's biochemical status, the person in an example embodiment can provide a biological sample (e.g., a bodily fluid sample, such as blood, saliva, serum, plasma sample, and/or urine) so that measurements of the hormones of interest can be obtained… an artificial intelligence (AI) computer system 202 that interacts with one or more patients 204, one or more doctors 206, one or more pharmacies 208, one or more biological sample assays 210, and one or more genetic sample assays 212 to compute a recommended prescription for hormone therapy treatment with respect to patient 204 based on the patient's biochemical, symptomatic, and genetic status… measurements of hormone levels”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Norman because one of ordinary skill in the art would have recognized the benefit of improving recommendations applied to hormone therapy.
As per claim 3, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a server is further configured to receive the at least a user input datum containing at least a user complaint (e.g. Prakash, in paragraph 32, “My arm hurts”).
As per claim 4, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a server is further configured to create the at least an unsupervised machine-learning model as a function of matching the at least a user structure entry to at least a dataset correlated to the at least a user structure entry (e.g. Tavshikar, in column 13 lines 23-27 and column 18 lines 15-46, “A machine learning process is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs [i.e. create ML model]… perform an unsupervised machine learning process on corpus of [datum], which may cluster data of corpus of [datum] according to detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other”; Prakash, in paragraphs 21-22, 33, 35, 54, and 70, “symptoms and other shared health status information… based upon symptoms or other diagnostic data collected over time… analyzing and correlating user profiles, external data and general medical information… aspects of their health (heart rate, blood pressure, etc.) [i.e. user structure]”).
As per claim 6, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a server is further configured to: classify the at least a user input datum to generate at least a classified user input datum containing at least a body dimension label and select at least a first training set as a function of the at least a body dimension label (e.g. Tavshikar, in column 4 lines 20-54 and column 9 line – column 10 line 4, “in a data entry including some textual data…reference to a list, dictionary, or other compendium of terms… automated association of data in the data entry…into a given format… a feed parsing module 136, which may categorize and format data for use in corpus of [input/input datum], creation of training sets included in corpus of [input/input datum]”; Prakash, in paragraphs 100 and 105, “these user input events, sometimes in combination, are classified into various different categories, including… Reports of Symptoms, Health Status, Nutrition [i.e. body dimension] and Exercise”).
As per claim 7, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a first training set further comprises a plurality of first data entries, each first data entry of the first training set including at least an element of structure data containing the at least a commonality label and at least a correlated first antidote label (e.g. Tavshikar, in column 4 lines 6-16, column 4 lines 45-51, column 13 lines 23-27 and column 18 lines 15-46, “mathematical relationship linking values belonging to the two categories… A machine learning process is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs… cluster data of corpus of [datum] according to detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other [i.e. commonality label]… such relations may then be combined with supervised machine learning results”; Prakash, in paragraphs 33 and 65, user “symptoms” correlated to antidotes (“medications, nutritional supplements”, etc.) ).
As per claim 8, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a label learner is further designed and configured to generate the at least an antidote by executing a lazy learning process as a function of the at least a first training set and the at least a user input datum (e.g. Tavshikar, in column 13 lines 15-27, “learner 148 designed and configured to determine [output] a function of the corpus of [input/input datum]… A machine learning process is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs… generate at least an output by executing a lazy learning process as a function of” training data and input, and figure 11”; Prakash, in paragraphs 32-33, Prakash, in paragraphs 33 and 65, user “symptoms” correlated to antidotes (“medications, nutritional supplements”, etc.)).
As per claim 9, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a label learner is further designed and configured to generate the at least an antidote by: generating a loss function of at least a user variable wherein the at least a user variable further comprises a treatment input and minimizing the loss function (e.g. Tavshikar, in column 14 lines 5-8 and column 17 lines 33-64, “minimize error functions… Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided”; e.g. Prakash, in paragraphs 32-33 and 65, “based upon the specific symptoms they have already reported…possible treatments as well as recommendations for preventive or therapeutic actions (e.g., icing your knee, refraining from strenuous activity or using a particular drug or product”, i.e. antidote).
As per claim 10, the rejection of claim 1 is incorporated and the combination further teaches wherein the at least a server is further configured to: receive at least a second user input datum as a function of the at least an antidote and generate at least a second antidote as a function of the at least a second user input datum (e.g. Tavshikar, e.g. in column 4 lines 45-51, column 13 lines 23-27 and column 18 lines 15-46, “produce outputs given data provided as inputs… cluster data of corpus of [datum] according to detected relationships between elements of the corpus of [datum], including without limitation correlations of elements of [input/entry datum] to each other and correlations of [output datum] to each other”; Prakash, in paragraphs 32-33, other user “symptoms” correlated to other antidotes (“medications, nutritional supplements”, etc.)).
Claims 11, 13-14, and 16-20 are the method claims corresponding to system claims 1, 3-4, and 6-10, and are rejected under the same reasons set forth.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tavshikar (US 10661902 B1) in view of Prakash et al. (US 20160012194 A1) and Norman (US 20200118662 A1) and further in view of Goodman et al. (US 20050015454 A1).
As per claim 5, the rejection of claim 1 is incorporated, but the combination does not specifically teach wherein the at least a server is further configured to select the at least a first training set by filtering at least a training set as a function of the at least a commonality label; and selecting the at least a first training set containing at least a data entry correlated to the at least a commonality label. However, Goodman teaches filtering at least a training set as a function of at least a commonality label and selecting at least a first training set containing at least a data entry correlated to the at least a commonality label (e.g. in paragraph 51, “a first filter 212 can be trained via a machine learning system using a first subset of training data…the first filter 212 comprises common terms”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Goodman because one of ordinary skill in the art would have recognized the benefit of using relevant features to affect learning by the models.
Claim 15 is the method claim corresponding to system claim 5, and is rejected under the same reasons set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Sweeney et al. (US 20130246328 A1) teaches “all preliminary keyword labels may be examined in the aggregate to identify compound keywords, which present more than one valid keyword label within a single concept label… information may be any suitable information indicative of what information the data consumer(s) may be interested in. For example, context information may comprise one or more search queries input by a data consumer… application program may send context information (e.g., a search query entered by the data consumer into the application program) to a server computer… system identifies content information [i.e. select at least a dataset] corresponding to the identified group of one or more concepts [by parsing entered] labels of the identified concepts… (e.g., …any combination of two or more concepts” (e.g. in paragraphs 11, 118, 131-132, 333, and 368).
Bhowmick et al. (US 20190244138 A1) teaches “The labeling and training module 330 can use the determined labels to train an existing server-side machine learning model 135 into an improved server-side machine learning model… process the set of proposed labels to determine the most frequently proposed label for a unit of unlabeled data … add the unit of data and the determined label to … train a third machine learning model” (in paragraphs 46 and 87-88).
Puirava (US 20190259482 A1) teaches “provide the prescription comprising the amount of the first medicine and the amount of the second medicine of the new patient as training data to train the machine learning model… The levels of symptoms associated with the two or more medicines in use may be obtained from the user device of the new patient. The user device may provide a second graphical user interface to the new patient to input the levels of symptoms caused by the two or more medicines after intake… providing the medicine data, the associated symptoms, the expert input on the medicine data…as training data to generate the machine learning model” (e.g. in paragraphs 57-58, 60-64, and 67).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.W/Examiner, Art Unit 2144 01/03/2025
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144