DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claim(s) 1 – 11 and 21 – 29 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee (Muk), publication number: US 2022/0319646 in view of Mehta, publication number: US 2026/0127245.
As per claim 1, Muk teaches a computing system comprising:
memory;
one or more processing units coupled to the memory; and
one or more computer readable storage media storing instructions that, when executed, cause the computing system to perform operations comprising:
receiving first user input to obtain a putative value for a first attribute (diagnosis and problem encountered, [0038], figure 1, 118, [0068]);
determining a first value generation method specified for the first attribute, the
first value generation method being a first member function of a computing object, the
computing data object comprising (1) a defined set of a plurality of variables, wherein the first user input specifies a first value for at least a first variable of the plurality of variables (generating assessment based on description, [0055], 126, Fig. 1);
generating at least one result value for the first value using the trained machine
learning model (presenting top suggestions, [0055], Fig 1,); and
returning the at least one result value in response to the first user input (allowing selection by clinician, Fig. 1, 128, [0055]).
Muk does not teach (2) a second value generation method, being a second member function of the computing object that is different than the first value generation method, programmed to generate a value for a variable of the plurality of variables other than the at least a first variable;
retrieving a second value for at least a second variable of the plurality of variables of the computing object;
providing the second value to a trained machine learning model specified for the
first value generation method of the computing object;
In an analogous art, Mehta teaches (2) a second value generation method, being a second member function of the computing object that is different than the first value generation method, programmed to generate a value for a variable of the plurality of variables other than the at least a first variable;
retrieving a second value for at least a second variable of the plurality of variables of the computing object;
providing the second value to a trained machine learning model specified for the
first value generation method of the computing object (machine learning model for generating suggestions for automated form entry, updating the machine learning model based on selection of a suggestion, ranking suggestions, [0055][0057]);
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Muk to include secondary input suggestions as described in Mehta’s automated system for the advantage of further improving the suggestion system.
As per claim 2, the combination teaches the operations further comprising:
training the machine learning model using values for a plurality of instances of the
computing object (Muk: Machine learning engine 102, [0026]).
As per claim 3, the combination teaches the operations further comprising:
defining a training data view, the training data view specifying variables of the plurality
of instances of the computing object to be used in the training the machine learning model, the training data view referencing relational data (Muk: vectors 110 and 112, [0032]).
As per claim 4, the combination teaches the operations further comprising:
receiving user input accepting or rejecting the at least one result value (Muk: accept or reject, [0056-0057]).
As per claim 5, the combination teaches the operations further comprising:
generating one or more confidence measures for the at least one result value (Muk: retrieve text based on score, [0055]).
As per claim 6, the combination teaches wherein one or more confidence
measures comprise an accuracy of the at least one result value (Muk: accurate text blocks, [0022][0024]).
As per claim 7, the combination teaches wherein generating at least one
result value comprises generating a plurality of result values and returning the at least one result value in response to the first use input comprises returning multiple result values of the plurality of result values (Muk: list, [0055]).
As per claim 8, the combination teaches the operations further comprising:
ranking the multiple result values (Muk: retrieved based on score, [0054-0055], Mehta: ranking, [0055]).
As per claim 9, the combination teaches the operations further
comprising:
receiving second user input, wherein the second user input comprises the second value (Mehta: form completion, [0055][0057]).
As per claim 10, the combination teaches the operations further comprising:
storing a definition of an input value retrieval scenario, the input value retrieval scenario
specifying:
an identifier of a machine learning algorithm for the trained machine learning
model; and
data to be retrieved from a plurality of instances of the computing object (Mehta: multiple machine learning models, [0055]).
As per claim 11, the combination teaches wherein the first user input is
received through a first user interface control of a graphical user interface comprising a plurality of user interface controls, the plurality of user interface controls comprising the first user interface control, the operations further comprising:
generating a data artefact associating multiple user interface controls of the plurality of user interface controls with respective methods for obtaining a putative value for a given user interface control of the plurality of user interface controls (Mehta: different machine learning models corresponding to different input fields, [0055]).
Claims 21 – 27 are rejected based on claims 1-3, 7-8 and 10-11
Clams 28 – 29 are rejected based on claims 1 - 2
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUGBENGA O IDOWU whose telephone number is (571)270-1450. The examiner can normally be reached Monday-Friday 8am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jung Kim can be reached at 5712723804. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494
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