DETAILED ACTION
This communication is responsive to the claim amendment filed on 12/19/2025.
Claims 1, 12, and 18 are independent claims.
Claims 1, 6, 8-12, and 17-18 are amended.
Claims 1-20 are pending in this application.
This Action has been made FINAL.
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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claims do not amount to significantly more than an abstract idea.
Regarding claim 1, the claim recites language of:
“obtaining a content repository comprising one or more content resources;
obtaining a first individual data for a first individual;
determining, from the first individual data, a first skillset for the first individual, the first skillset comprising a first skill and the first skill comprising a first metric comprising a first description;
determining a first value for the first metric by performing steps comprising:
extracting, with an artificial intelligence (AI) natural language processing (NLP) model, a first textual content from the first description;
extracting, with the Al NLP model, a second textual content from the individual data;
determining, with the Al NLP model, a first word-embedding representation of the first textual content in a continuous vector space;
determining, with the Al NLP model, a second word-embedding representation of the second textual content in the continuous vector space; and
computing, with the Al NLP model, a similarity between the first word-embedding representation and second word-embedding representation, the similarity forming the first value;
determining a first score for the first skill based on the first individual data and the first value;
obtaining a content map that associates each skill in the first skillset to at least one content resource of the content repository; and
recommending a first content resource from the content repository for the first individual based on the first score and the content map.”
Regarding claim 12, the claim recites language of:
“obtaining a set of skills, each skill in the set of skills comprising at least one metric;
obtaining a content resource;
obtaining metadata for the content resource;
segmenting the content resource into one or more segments based on the metadata;
mapping each segment to at least one metric comprised by the set of skills by performing steps comprising, for each segment, for each metric:
obtaining a segment textual description of the segment comprising at least one word;
determining a word-embedding representation of the segment textual description of the segment in a continuous vector space;
obtaining a metric textual description of the metric comprising at least one word;
determining a word-embedding representation of the metric textual description in the continuous vector space, and
computing a similarity score between the word-embedding representation of the segment textual description of the segment and the word-embedding representation of the metric textual description of the metric; and
updating the content repository of content resources with the segmented content resource and associated mapping.”
a/ Analysis under Step 2A, Prong I:
Regarding claim 1, the steps of “determining, …, a first skillset for the first individual, the first skillset comprising a first skill and the first skill comprising a first metric comprising a first description;” “determining a first value for the first metric by performing steps comprising: extracting, with an artificial intelligence (AI) natural language processing (NLP) model, a first textual content from the first description; extracting, …, a second textual content from the individual data; determining, …, a first word-embedding representation of the first textual content in a continuous vector space; determining, …, a second word-embedding representation of the second textual content in the continuous vector space; and “determining a first score for the first skill based on the first individual data and the first value”, as drafted, are mental processes that, under its broadest reasonable interpretation, cover performance of the limitations in the human mind, e.g., an observation, evaluation, judgment, option, etc., but for the recitation of generic computer’s component(s). Therefore, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (see MPEP 2106.04(a)(2), Part III).
In addition, the amended step of “computing a similarity score …” falls within the “mathematical concept” grouping as abstract idea because this step is related to the mathematical relationships or formula(s) (see Applicant’s specification, paras. [0061] and [0086]).
*** Similar above analysis steps are applied to the amended limitations in claim 12 and claim 18, respectfully. Furthermore, claim 18 recites plurality steps of “determining” activities that are also mental processes because of covering performance of the limitations in human mind that falls within the “Mental Processes” grouping of abstract ideas (see MPEP 2106.04(a)(2), Part III)
b/ Analysis under Step 2A, Prong II:
The remaining limitations in claims 1, 12, and 18 do not integrate the judicial exception into a practical application. For instance, the additional element, e.g., “a first computer” is known as generic computing unit/device used as tool for performing the functionality of the above indicated steps which amounts no more than mere instructions to apply the exception using the generic computing component/unit having at least a processor or a memory, see Mayo, 566 U.S. AT 84. Next, claims 1 and 18 recite the additional limitations of “obtaining…”, “obtaining…”, “recommend a first content resource…”, “receiving…”, and “recommended a second content resource…” represent an insignificant extra solution activities because these additional steps including in the claims do not specify “how” data processes/execute (see MPEP 2106.04(a)-(h)). Furthermore, the amended feature: “an artificial intelligence (AI) natural language processing (NLP) model” does not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.
c/ Analysis under Step 2B:
Furthermore, independent claims 1, 12, and 18 do not include additional elements/limitations beyond the judicial exception that, alone or in combination, are not “well-understood, routine, conventional” (see MPEP 2106.05(d)). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “a first computer” in claim 18 is being only used as tools for performing the above indicated limitations. Next, claims 1 and 18 recite the additional limitations of “obtaining…”, “obtaining…”, “recommend a first content resource…”, “receiving…”, and “recommended a second content resource…” represent an insignificant extra solution activities because these additional steps including in the claims that are well-understood, routine, conventional activity to a skill artisan in the relevant technical field of gathering and transmitting data via network, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. Furthermore, the amended feature: “an artificial intelligence (AI) natural language processing (NLP) model” does not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. Therefore, these collective functions merely and commonly provide conventional computer implementation.
For the at least above reasons, the limitations in claims 1, 12, and 18, that, as considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Claims 2-11, 13-17, and 19-20 depend on independent claims 1 and 18 and include all the limitations of claims 1, 12, and 18; and hence, claims 2-11, 13-17, and 19-20 recite the same as being the above abstract idea.
Regarding claim 2 and claim 4, the claims recite additional limitations of “wherein the first individual data comprises an application usage of the first individual” and “wherein the first skillset further comprises a second skill” which do not integrate the judicial exception into a practical application. The claim language provides further definition of the first individual data and the first skillset which do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.
In fact, the claims do not include additional limitations/elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 3, the claim recites further additional limitation of “wherein the first skill further comprises: a first weight associated with the first metric; a second metric comprising a second description and a second value; and a second weight associated with the second metric, and wherein the first score is a weighted average of the first metric and the second metric based on the first weight and second weight”, which do not integrate the judicial exception into a practical application because these additional steps of do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.
Thus, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 5, the claim recites further additional limitation of “obtaining a second individual data for a second individual; determining, from the second individual data, a second skillset for the second individual, the second skillset comprising a first skill, the first skill comprising a first metric, the first metric comprising a first description and first value; determining a first score for the first skill of the second skillset based on the second individual data and the first metric of the first skill of the second skillset; determining a similarity metric between the first skillset and the second skillset; and recommending a second content resource from the content repository for the first individual based on the similarity metric” in which the step of “determining, from the second individual data, a second skillset for the second individual, the second skillset …; determining a first score …; determining a similarity metric between the first skillset and the second skillset”, as drafted, are mental processes as performing in the human mind that falls within the “Mental Processes” grouping of abstract idea (see MPEP 2106.04 (a)(2), part III). Plus, the additional steps of “obtaining…” and “recommending…” do not integrate the judicial exception into a practical application because the steps of “obtaining…” and “recommending …” represent insignificant extra solution activities because these additional steps including in the claim as the computing functions that do not amount to significantly more than mere instructions to apply the exception using a generic computer components that are well-understood, routine, conventional activity to a skill artisan in the relevant technical field of gathering data via network, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
Thus, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 6, the claim recites further additional limitation of “wherein the AI NLP model comprises a global vectors for word representation (GloVe) algorithm configured to determine the first word embedding representation and the second word embedding representation” which are not integrate the judicial exception into a practical application because claim language provides only further definition of the AI NLP model and a global vectors (GloVe) for word representation. Also, the “AI NLP model does not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use and being only used as a tool. Thus, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 7, the claim recites further additional limitation of “wherein the first description is a first textual description comprising at least one word, and wherein the first individual data comprises text data comprising at least one word” which do not integrate the judicial exception into a practical application. The claim language provides further definition of the first description is a textual description having at least one word, and the first individual data as text data which do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. In fact, the claims do not include additional limitations/elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claims 8, 9, and 10, the claims recite further additional limitations of “the first textual content is extracted from the first textual description and the second textual content is extracted from the text data”, “wherein determining the similarity metric is performed by an artificial intelligence (AI) model”, and “wherein the AI model is a clustering model” which do not integrate the judicial exception into a practical application. The claim language provides further definition of the first AI model, the second AI model, and the similarity metric which do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.
Thus, these claims do not include additional limitations/elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 11, the claim recites further additional limitation of “determining an attendance of the first individual for a topic comprising a third content resource; determining a third skill within the first skillset that corresponds to the topic; obtaining an attendance threshold; comparing the attendance to the attendance threshold; when the attendance is less than the attendance threshold: obtaining a score threshold; obtaining a third score, the third score computed from one or more metrics of the third skill; and when the third score is less than the score threshold: recommending the third content resource to the first individual” in which the steps of “determining an attendance…”; determining a third skill…”, and “comparing the attendance to the attendance threshold”, as drafted, are mental processes as performing in the human mind that falls within the “Mental Processes” grouping of abstract idea (see MPEP 2106.04 (a)(2), part III). In addition, the additional steps of obtaining and recommending do not integrate the judicial exception into a practical application because the steps represent insignificant extra solution activities. These additional steps including in the claim as the computing functions that do not amount to significantly more than mere instructions to apply the exception using a generic computer components that are well-understood, routine, conventional activity to a skill artisan in the relevant technical field of gathering data via network, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
Therefore, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
*** Similar above analysis is applied to claim 19, respectively.
Regarding claims 13-14, the claims recite further additional limitations of “wherein the content resource is a video acquired using a camera”, “wherein the metadata is an outline of information presented in the video”, which do not integrate the judicial exception into a practical application. The claim language provides further definition of the content resource and metadata which do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. Plus, “a video” is being used as a tool. Thus, these claims do not include additional limitations/elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 15, the claim recites further additional limitation of “wherein the metadata is a maintenance report comprising a first time data and an activity description of a maintenance activity, the maintenance report acquired from a maintenance work order database, and wherein the maintenance report is assigned to the metadata of the video by a linking method, the linking method comprising: obtaining second time data for the video; and determining that the video records the maintenance activity based on, at least, the first time data and the second time data” in which the steps of “is assigned” and “determining”, as drafted, are mental processes as performing in the human mind that falls within the “Mental Processes” grouping of abstract idea (see MPEP 2106.04 (a)(2), part III).
In addition, the additional element of “a maintenance report” and step of “obtaining…” do not integrate the judicial exception into a practical application because the steps represent insignificant extra solution activity and that is well-understood, routine, conventional activity to a skill artisan in the relevant technical field of gathering data via network, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
Therefore, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 16, the claim recites further additional limitation of “wherein segmenting the content resource into one or more segments based on the metadata is performed by a first artificial intelligence model” does not integrate the judicial exception into a practical application because the step represents insignificant extra solution activity. Thus, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 17, the claim recites further additional limitation of “wherein mapping each segment to at least one metric comprised by the set of skills comprises: for each segment: obtaining a segment textual description of the segment comprising at least one word; and for each metric: obtaining a metric textual description of the metric comprising at least one word; determining, with a second artificial intelligence (AI) model, a similarity score between the segment and the metric based on the segment textual description and the metric textual description; comparing the similarity score to a similarity threshold; and mapping the segment to the metric if the similarity score is greater than the similarity threshold” in which the steps of “determining, with a second artificial intelligence (AI) model, a similarity score…” and “comparing the similarity score to a similarity threshold”, as drafted, are mental processes as performing in the human mind that falls within the “Mental Processes” grouping of abstract idea (see MPEP 2106.04 (a)(2), part III). In addition, the additional steps of “obtaining a segment textual description…”, “obtaining a metric textual description…”, and “mapping the segment…” do not integrate the judicial exception into a practical application because the steps represent insignificant extra solution activities. These additional steps including in the claim as the computing functions that do not amount to significantly more than mere instructions to apply the exception using a generic computer components that are well-understood, routine, conventional activity to a skill artisan in the relevant technical field of gathering data via network, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
Therefore, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Regarding claim 20, the claim recites further additional limitation of “a meeting room, a first camera installed in the meeting room, a field facility, wherein process data for the field facility is stored in a field facility database, a second camera installed in the field facility, and a second computer configured to: receive an input creating an empty repository of content resources, receive a content resource from the data acquisition system, receive, from the data acquisition system, metadata for the content resource, segment the content resource into one or more segments based on the metadata, receive a set of skills, each skill in the set of skills comprising at least one metric, map each segment to at least one metric comprised by the set of skills, and update the content repository of content resources with the segmented content resource and associated mapping; wherein mapping each segment to at least one metric comprised by the set of skills, comprises: for each segment: obtaining a segment textual description of the segment comprising at least one word; and for each metric: obtaining a metric textual description of the metric comprising at least one word; determining, with an artificial intelligence (AI) model, a similarity score between the segment and the metric based on the segment textual description and the metric textual description; making a second determination whether the similarity score is greater than a similarity threshold; and based on the second determination that the similarity score is determined greater than the similarity threshold, mapping the segment to the metric” in which the steps of “determining” the similarity score, and “mapping” the segment to the metric, as drafted, are mental processes as performing in the human mind that falls within the “Mental Processes” grouping of abstract idea (see MPEP 2106.04 (a)(2), part III).
In addition, the additional elements of a meeting room, a first camera installed in the meeting room, a field facility, wherein process data for the field facility is stored in a field facility database, a second camera installed in the field facility, a similarity score, a similarity threshold, and a second computer are being used as stools. The additional steps of “receiving” an input, a content resource, a set of skill, etc., and “obtaining” a metric textual description, a segment textual description, etc., do not integrate the judicial exception into a practical application because the steps represent insignificant extra solution activities. These additional steps including in the claim as the computing functions that do not amount to significantly more than mere instructions to apply the exception using a generic computer components that are well-understood, routine, conventional activity to a skill artisan in the relevant technical field of gathering data via network, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
Therefore, the claim does not include additional limitation/element that is sufficient to amount to significantly more than the judicial exception because the additional limitations/elements, when consider both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
For at least above reasons, claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significant more.
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, and 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson et al., US Pub. No. 2015/0172395 A1 (hereinafter as “Jackson”) in view of ORBACH et al., US Pub. No. 2022/0382982 A1 (hereinafter as “Orbach”), and further in view of Brown et al., US Pub. No. 2023/0317258 A1 (hereinafter as “Brown”), AND Xiong et al., US Pub. No. 2018/0260746 A1 (hereinafter as “Xiong”).
Regarding claim 1, Jackson teaches a method, comprising:
obtaining a content repository comprising one or more content resources (see Figs. 2-3 as shown the obtained content database/repository having content resources; and Fig. 3; and par. [0019] e.g., “Database 134 may store any type of data accessible by server(s) 132 including, for example, various content items registered as resources associated with users 102, 104 and 106”);
obtaining a first individual data for a first individual (e.g., the individual profile of user(s) stored in database, see pars. [0034-35]);
determining, from the first individual data, a first skillset for the first individual (see Fig. 1, see Skills at elements 102a, 104a, 106a of the user(s)=individual(s); and par. [0045] e.g., “a physical possession owned by the first user or information related to a profession (e.g., doctor), professional service, or set of skills associated with the first user in the social networking service”), the first skillset comprising a first skill (pars. [0024] “certain skill-sets”, [0025] “information related to a set of skills 102a associated with the user. For example, the set of skills 102a may be related to the user's profession. In an example, the information registered by user 102 may include profile information identifying the user as a medical doctor…”, and [0045] e.g., “set of skills associated with the first user/individual).
Jackson does not explicitly teach: “the first skill comprising a first metric comprising a first description;” “determining a first value for the first metric by performing steps comprising: extracting, with an artificial intelligence (AI) natural language processing (NLP) model, a first textual content from the first description; extracting, with the Al NLP model, a second textual content from the individual data; determining, with the Al NLP model, a first word-embedding representation of the first textual content in a continuous vector space; determining, with the Al NLP model, a second word-embedding representation of the second textual content in the continuous vector space; and computing, with the Al NLP model, a similarity between the first word-embedding representation and second word-embedding representation, the similarity forming the first value”, and “determining a first score for the first skill based on the first individual data and the first value; obtaining a content map that associates each skill in the first skillset to at least one content resource of the content repository; recommending a first content resource from the content repository for the first individual based on the first score and the content map.”
In the same field of endeavor (i.e., data processing), Orbach teaches the amended limitations: determining a first value for the first metric by performing steps comprising:
extracting, with an artificial intelligence (AI) natural language processing (NLP) model, a first textual content from the first description (see Fig. 3A, element 210; Abstract, e.g., “extracting one or more phrases from the document”, wherein the one or phrases are interpreted as textual content, and the document implies the description as broadest reasonable interpretation (see MPEP 2111); and par. [0069] e.g., “As known in the art of Artificial Intelligence (AI)-based Natural Language Processing (NLP)”);
extracting, with the Al NLP model, a second textual content from the individual data (similar to the first textual content algorithm is applied to a second textual content, respectively, see Abstract, pars. [0069-71] including “receive a word in a human language, and produce therefrom an embedding vector representation 220A of the received word” which is interpreted as the individual data);
determining, with the Al NLP model, a first word-embedding representation of the first textual content in a continuous vector space (see in par. [0069] e.g., “a vector, commonly referred to as an “embedding vector” in a vector space commonly referred to as an “embedding vector space. Using this embedding vector space representation of words may enable NLP systems… The benefit of this continuous embedding vector representation is that NLP systems may map words that are similar in meaning to similar regions of the embedding vector space (e.g., to be represented by a similar embedding vector). For example, an embedding vector representation of the word “cat” may be similar (according to some predefined metric) to an embedding vector representation of the word “feline” than to an embedding vector representation of the word “piano”.”; and further in pars. [0070-71] teaches word-embedding representation);
determining, with the Al NLP model, a second word-embedding representation of the second textual content in the continuous vector space (see in Fig. 3A, element 240A; and similar to the first word-embedding representation algorithm is applied to a second word-embedding representation, respectively, see in pars. [0069-71] as disclosed above) and
computing, with the Al NLP model, a similarity between the first word-embedding representation and second word-embedding representation, the similarity forming the first value (par. [0069] “… an embedding vector representation of the word “cat” may be similar (according to some predefined metric) to an embedding vector representation of the word “feline” than to an embedding vector representation of the word “piano””; and further in pars. [0075] “weight calculation module 230 may be a calculator of TF-IDF score, and weight 230A may be a TF-IDF score value…”, and [0076-77] via word weight calculation technique for the phrase “something similar”; and par. [0080-91], and [0094] via “relevant score”=value of similarity).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson and Orbach because the teachings of Orbach would have provided Jackson with the above amended limitations for allowing a skilled artisan in motivation to use the AI NLP model to extract the textual content with the word-embedding representation in the continuous vector space for performing the value (Orbach: pars. [0069-70]]).
Jackson and Orbach do not explicitly teach: “the first skill comprising a first metric comprising a first description;” and “determining a first score for the first skill based on the first individual data and the first value; obtaining a content map that associates each skill in the first skillset to at least one content resource of the content repository; recommending a first content resource from the content repository for the first individual based on the first score and the content map.”
In the same field of endeavor (i.e., data processing), Brown teaches: “the first skill comprising a first metric comprising a first description” (see par. [0125] “As this data is used for training skill (or task) models, which will be used to determine the surgeon's skill score, the data may be annotated to indicate whether the data was generated by an “expert” or “nonexpert” surgeon. As will be discussed herein, the training data may contain asymmetries, as when there are many more nonexpert than expert data values. Consequently, a resampling method, such as Synthetic Minority Oversampling Technique (SMOTE) (e.g., using the Imblearn™ library function imblearn.over_sampling.SMOTE), may be applied to the raw training data at block 705c or to the generated metrics at block 705d”; and par. [0126] further discloses “…select specific types of metrics for each model to use when assessing their corresponding skill (or task)… descriptions…”); and
“determining a first score for the first skill based on the first individual data and the first value” (again in par. [0125] e.g., “determining the surgeon’s skill score” and “he training data may contain asymmetries, as when there are many more nonexpert than expert data values…”; and par. [0126] “score results from skill models may also be used to infer score results for tasks). One will appreciate that as both task and skill models operate upon collections of OPI data to produce a score, descriptions herein for OPI-selection, training, and application with respect to skill models apply likewise to task models (even though only skill models may be discussed for clarity)”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, and Brown because the teachings of Brown would have provided Jackson and Orbach with the above indicated limitations for allowing a skilled artisan in motivation as modifying the teachings of cited references to include the description and values in the skill metric for performing the skill score of the expert/non-expert efficiently (Brown: pars. [0125-128, and 135]).
Jackson, Orbach, and Brown do not explicitly teach: “obtaining a content map that associates each skill in the first skillset to at least one content resource of the content repository; recommending a first content resource from the content repository for the first individual based on the first score and the content map.”
In the same field of endeavor (i.e., data processing), Xiong teaches: “obtaining a content map that associates each skill in the first skillset to at least one content resource of the content repository” (see par. [0016] e.g., “Based on an expertise-estimation model, the job scheduler may recommend one or more appropriate resources to handle the job by matching the job type to resources with the appropriate background, for example, skills, experience, training, ability, availability, etc.”); and “recommending a first content resource from the content repository for the first individual based on the first score and the content map” (see again par. [0016] teaches mapping/matching the resource to job type with skills, experience; and par. [0049] teaches the first recommended resource according to the content resource map and the weight from the weight generator).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, and Xiong because the teachings of Xiong would have provided Jackson, Orbach and Brown with the above indicated limitations for allowing a skilled artisan in motivation as modifying the teachings of cited references to perform the content resource recommendation based on the content map/match and the score/weight (Xiong: pars. [00015-16 and 49-52]).
Regarding claim 3, Brown teaches: wherein the first skill further comprises:
a first weight associated with the first metric (par. [0125] e.g., “metrics at block 705d” and “determining the surgeon’s skill score”; and par. [0126] “score results from skill models may also be used to infer score results for tasks). One will appreciate that as both task and skill models operate upon collections of OPI data to produce a score, descriptions herein for OPI-selection, training, and application with respect to skill models apply likewise to task models (even though only skill models may be discussed for clarity)”);
a second metric comprising a second description and a second value (par. [0125] e.g., “the generated metrics at block 705d”; and par. [0126] further discloses “…select specific types of metrics for each model to use when assessing their corresponding skill (or task)….”, “score”, “values associated with a skill” and “descriptions)); and
a second weight associated with the second metric, and wherein the first score is a weighted average of the first metric and the second metric based on the first weight and second weight (par. [0125] e.g., “metrics at block 705d” and “determining the surgeon’s skill score”; and par. [0126] “score results from skill models may also be used to infer score results for tasks). One will appreciate that as both task and skill models operate upon collections of OPI data to produce a score, descriptions herein for OPI-selection, training, and application with respect to skill models apply likewise to task models (even though only skill models may be discussed for clarity)”).
***Examiner’s note: claim 3 recites limitations, which are similar to the limitations/steps in claim 1, but are applied to a second metric comprising a second description and a second value rather than a first metric comprising a first description and first value, etc.; thus, the claim is rejected by implementing same quotations in the same analysis of above claim 1, respectively.
Regarding claim 4, Brown teaches: wherein the first skillset further comprises a second skill ((see Fig. 1, see Skills at elements 102a, 104a, 106a of the user(s)=individual(s); pars. [0024] “certain skill-sets”, [0025] “information related to a set of skills 102a associated with the user. For example, the set of skills 102a may be related to the user's profession. In an example, the information registered by user 102 may include profile information identifying the user as a medical doctor…”, and [0045] e.g., “set of skills associated with the first user/individual).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson, Orbach, Brown, and Xiong, and further in view of APPS et al., US Pub. No. 2020/0234205 A1 (hereinafter as “APPS”).
Regarding claim 2, the claim is rejected by the same reasons set forth above to claim 1. However, Jackson, Orbach, Brown, and Xiong do not teach: “wherein the first individual data comprises an application usage of the first individual.”
In the same field of endeavor (i.e., data processing), APPS teaches: “wherein the first individual data comprises an application usage of the first individual” (par. [0052] e.g., “usage of the application 108” and “the individual data”; and in par. [0055] e.g., “a user creating profiles in the application 108…”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, Xiong, and APPS because the teachings of APPS would have provided Jackson, Orbach, Brown, and Xiong with an application usage of the first individual for allowing a skilled artisan in motivation as modifying the teachings of cited references to perform the user/individual profile in the specific organization(s) (APPS: Figs. 3-4; and pars. [0015-16 and 49-52]).
Claims 5, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson, Orbach, Brown, and Xiong, and further in view of Dow et al., US Pub. No. 2013/0041876 (hereinafter as “Dow”).
Regarding claim 5, the claim is rejected by the same reasons set forth above to claim 1. Furthermore, Jackson, Orbach, and Brown, in combination, teach:
obtaining a second individual data for a second individual (Jackson: pars. [0034-35] e.g., the individual profile of user/individual);
determining, from the second individual data, a second skillset for the second individual (Jackson: see Fig. 1, see Skills at elements 102a, 104a, 106a of the user(s)=individual(s); and par. [0045] e.g., “a physical possession owned by the first user or information related to a profession (e.g., doctor), professional service, or set of skills associated with the first user in the social networking service”), the second skillset comprising a first skill (Jackson: see pars. [0024] “certain skill-sets”, [0025] “information related to a set of skills 102a associated with the user. For example, the set of skills 102a may be related to the user's profession. In an example, the information registered by user 102 may include profile information identifying the user as a medical doctor…”, and [0045] e.g., “set of skills” associated with the first user/individual), the first skill comprising a first metric, the first metric comprising a first description and first value (Brown: see par. [0125] “As this data is used for training skill (or task) models, which will be used to determine the surgeon's skill score, the data may be annotated to indicate whether the data was generated by an “expert” or “nonexpert” surgeon. As will be discussed herein, the training data may contain asymmetries, as when there are many more nonexpert than expert data values…, the raw training data at block 705c or to the generated metrics at block 705d”; and par. [0126] further discloses “…select specific types of metrics for each model to use when assessing their corresponding skill (or task)….”, “score”, “values associated with a skill” and “descriptions);
determining a first score for the first skill of the second skillset based on the second individual data and the first metric of the first skill of the second skillset (Brown: see par. [0125] e.g., “determining the surgeon’s skill score”; and par. [0126] “score results from skill models may also be used to infer score results for tasks). One will appreciate that as both task and skill models operate upon collections of OPI data to produce a score, descriptions herein for OPI-selection, training, and application with respect to skill models apply likewise to task models (even though only skill models may be discussed for clarity)”).
However, Jackson, Orbach, Brown, and Xiong do not teach the particular claim feature: “determining a similarity metric”
In the same field of endeavor (i.e., data processing), Dow teaches: “determining a similarity metric” (par. [0023] e.g., “Similar links can be recommended to users that have shared a link, wherein various similarity metrics can be utilized to determine similarity between users and links as discussed further below. Various techniques can be utilized with respect to recommending similar links such as user-based top-N (N is a positive integer) recommendation and link-based top-N recommendation”; and par. [0031] e.g., “computing a similarity metric”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, Xiong, and Dow because the teachings of Dow would have provided Jackson, Orbach, Brown, and Xiong with the determined similarity metric for allowing a skilled artisan in motivation as modifying the teachings of cited references to perform grouping technique based on the similarity metric to form top-N recommendation of links and/or user/individual (Dow: Figs. 3, 5-6; and pars. [0023, 31]). Therefore, in combination, Jackson, Brown, Xiong, and Dow teach: “determining a similarity metric between the first skillset and the second skillset” (Dow: see par. [0023] e.g., “various similarity metrics can be utilized to determine similarity between users and links as discussed further below. Various techniques can be utilized with respect to recommending similar links such as user-based top-N (N is a positive integer) recommendation and link-based top-N recommendation”; and par. [0031] e.g., “computing a similarity metric”; and Jackson: see pars. [0024] “certain skill-sets”, [0025] “information related to a set of skills 102a associated with the user. For example, the set of skills 102a may be related to the user's profession”); and “recommending a second content resource from the content repository for the first individual based on the similarity metric” (Xiong: see again par. [0016] teaches mapping/matching the resource to job type with skills, experience; and par. [0049] teaches the first recommended resource according to the content resource map and the weight from the weight generator; and Dow: see par. [0023] e.g., “various similarity metrics can be utilized to determine similarity between users and links as discussed further below. Various techniques can be utilized with respect to recommending similar links such as user-based top-N (N is a positive integer) recommendation and link-based top-N recommendation”; and par. [0031] e.g., “computing a similarity metric”).
Regarding claim 9, Orbach and Dow, in combination, teach: “wherein determining the similarity metric is performed by an artificial intelligence (AI) model” (Orbach: see par. [0069] via AI NLP model and similar in meaning to similar regions and similar embedding vector for similar embedding word(s), and par. [0094] via relevant score; and Dow: par. [0023] e.g., “Similar links can be recommended to users that have shared a link, wherein various similarity metrics can be utilized to determine similarity between users and links as discussed further below. Various techniques can be utilized with respect to recommending similar links such as user-based top-N (N is a positive integer) recommendation and link-based top-N recommendation”; and par. [0031] e.g., “computing a similarity metric”; and par. [0055] “include or consist of artificial intelligence, machine learning, or knowledge or rule-based components, sub-components, processes, means, methodologies, or mechanisms (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, classifiers . . . )…”).
Regarding claim 10, Orbach and Dow, in combination, teach: “wherein the AI model is a clustering model” (Orbach: see Fig. 4 as shown the merging/clustering model; and Dow: see again par. [0055] “artificial intelligence…”; and pars. [0035] and [0037] disclose “clustered” and “clusters”/ “clustering” technique/model).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson, Orbach, Brown, and Xiong, and further in view of Dadkhahnikoo et al., US Pub. No. 2019/0311790 A1(hereinafter as “Dadkhahnikoo”).
Regarding claim 6, the claim is rejected by the same reasons set forth above to claim 1. However, Jackson, Orbach, Brown, and Xiong do not teach: “a global vectors for word representation (GloVe) algorithm”.
In the same field of endeavor (i.e., data processing), Dadkhahnikoo teaches: “a global vectors for word representation (GloVe) algorithm” (see par. [0031] “…word2vec or GloVe”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, Xiong, and Dadkhahnikoo because the teachings of Dadkhahnikoo would have provided Jackson, Orbach, Brown, and Xiong with the GloVec algorithm for allowing a skilled artisan in motivation to perform embedding word vector(s) in the AI system (Dadkhahnikoo: Figs. 1, 6-7; and pars. [0030-32])
Regarding claim 7, the claim is rejected by the same reasons set forth above to claims 1 and 6. Furthermore, Orbach teaches: “wherein the first description is a first textual description comprising at least one word, and wherein the first individual data comprises text data comprising at least one word” (see Figs. 3A-4, Fig. 8, element S1010 via “extracting” algorithm/technique; and par. [0066] via “Phrase extraction module 210”).
Regarding claim 8, the claim is rejected by the same reasons set forth above to claims 1 and 6-7. Furthermore, Orbach, and Xiong, in combination, teach the artificial intelligence (AI) model (Orbach: par. [0069] via AI NLP; and Xiong: par. [0019] “the system 100 uses a combination of Artificial Intelligence (AI) and machine learning techniques to perform a variety of operations associated with job allocation…”, par. [0023] “frequency values for all word pairs”, par. [0025] “parameter values… high intensity values… low intensity values”), and the amended limitation: “the first the textual content is extracted from the first textual description and the second textual content is extracted from the text data” (Orbach: Figs. 3A-4, Fig. 8, element S1010 via “extracting” algorithm/technique for the text document=textual description and textual content from the text data, see in par. [0066-68]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson, Orbach, Brown, and Xiong, and further in view of Lane, US Pub. No. 2024/0354675 A1 (hereinafter as “Lane”) and Kumar et al., US Pub. No. 2023/0231729 A1 (hereinafter as “Kumar”).
Regarding claim 11, the claim is rejected by the same reasons set forth above to claim 1. However, Jackson, Orbach, Brown, and Xiong do not explicitly teach: “determining an attendance of the first individual for a topic comprising a third content resource; determining a third skill within the first skillset that corresponds to the topic; obtaining an attendance threshold; comparing the attendance to the attendance threshold; when the attendance is less than the attendance threshold: obtaining a score threshold; obtaining a third score, the third score computed from one or more metrics of the third skill; and
when the third score is less than the score threshold: recommending the third content resource to the first individual.”
In the same field of endeavor (i.e., data processing”), Lane teaches:
determining an attendance of the first individual for a topic comprising a third content resource (par. [0099] “a location where the individual can live in order to fulfill the requirements of the position, whether the position can be remote or if on-site attendance is required, and/or various other performance related information. Further, the name of the user that modifies the information associated with the individual/resource is also listed”; and par. [0104] “ a resource/individual is assigned, using a team name textbox 547, can indicate whether the resource is a subject matter expert, via a checkbox 549, and/or can input the areas of expertise or other notes, via the notes textbox 551, for each resource/individual…” wherein the subject matter expert is implemented to the topic);
determining a third skill within the first skillset that corresponds to the topic (par. [0103] “determine what skills, …”, pars. [0104] teaches topic as a subject matter expert, and [0112] ““accountant” is a role within an organization, and the organization may indicate that all roles (e.g., accountants) have certain skillsets. Additionally or alternatively, within the organization there may be a need for a cost accountant position, which may have different skill requirements than a forensic accountant. Thus, the organization can define the skill requirements and assess the skills of resources within the organization that fulfill certain needs for certain roles and/or certain positions.”; and par. [0132]);
obtaining an attendance threshold (pars. [0102] “In order for a “demand” entry to be certified/completed, a user may be required to provide a threshold number of skills associated with each role…”, [0103] and [0134] e.g., “a plurality of threshold requirements”); and
obtaining a third score, the third score computed from one or more metrics of the third skill (par. [0108] “display the resources/individuals within the enterprise, and may score the resource/individuals based on their skill level relative to enterprise standards or desired skill levels based on the role and/or position. For instance, the user interface display 701 may provide a user with micro-level insights into whether a resource's/individual's skills are above or below manager expectations”, pars. [0110, 111-113] skills in skillset/skill sets, [0117-118] teaches one or more metric including similarity and learning metrics as broadest reasonable interpretation. See MPEP 2111).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, Xiong, and Lane because the teachings of Lane would have provided Jackson, Orbach, Brown, and Xiong with the above indicated limitations for allowing a skilled artisan in motivation as modifying the teachings of cited references to facilitate collecting information from users (e.g., managers, supervisors, executives, etc.) that assess the skill(s) needed and/or other demand (i.e., the skills/competencies, proficiencies, timing of the need, etc.) needed by the enterprise (Lane: Figs. 5-7; and pars. [0099, 103, 110-113 and 117-118]).
However, Jackson, Orbach, Brown, Xiong, and Lane do not explicitly teach: “comparing the attendance to the attendance threshold; when the attendance is less than the attendance threshold: obtaining a score threshold; when the third score is less than the score threshold: recommending the third content resource to the first individual.”
In the same field of endeavor (i.e., data processing), Kumar teaches:
comparing the attendance to the attendance threshold (see Abstract: “a degree of importance of attending the communications session”, and par. [0036] e.g., “compare the attendance score to a predetermined threshold”);
when the attendance is less than the attendance threshold (par. [0048] “if the attendance score is below the threshold”): obtaining a score threshold (pars. [0046] “obtain an attendance score”, and [0048] “generates a response based on the attendance score”); when the third score is less than the score threshold (par. [0048] “if the attendance score is below the threshold”): recommending the third content resource to the first individual (see Figs. 4-5 as shown the score threshold and displaying the content resource to the user/individual).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, Xiong, Lane, and Kumar because the teachings of Kumar would have provided Jackson, Orbach, Brown, Xiong, and Lane with the above indicated limitations for allowing a skilled artisan in motivation as modifying the teachings of cited references to perform the score threshold and recommending/displaying the content resource to user/individual/attendee training/meeting (Kumar: Figs. 4-6; Abstract, and pars. [0046-48]).
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson et al., US Pub. No. 2015/0172395 A1 (hereinafter as “Jackson”) in view of Elchik et al., US Pub. No. 2018/0061256 A1 (hereinafter as “Elchik”), and further in view of ORBACH et al, US Pub. No. 2022/0382982 A1 (hereinafter as “Orbach”).
Regarding claim 12, the claim recites similar amended limitations in claim 1; hence, the claim is rejected by the same rational on basis of claim 1. Furthermore, Jackson teaches: a method for generating a content repository of content resources (Figs. 2-3 as shown the obtained content database/repository having content resources; and Fig. 3; and par. [0019] e.g., “Database 134 may store any type of data accessible by server(s) 132 including, for example, various content items registered as resources associated with users 102, 104 and 106”), comprising:
obtaining a set of skills, each skill in the set of skills comprising at least one metric (pars. [0024] “certain skill-sets”, [0025] “information related to a set of skills 102a associated with the user. For example, the set of skills 102a may be related to the user's profession. In an example, the information registered by user 102 may include profile information identifying the user as a medical doctor…”, and [0045] e.g., “set of skills associated with the first user/individual);
obtaining a content resource (Figs. 2-3 as shown the obtained content database/repository having content resources; and Fig. 3; and par. [0019] e.g., “Database 134 may store any type of data accessible by server(s) 132 including, for example, various content items registered as resources associated with users 102, 104 and 106”).
In the same field of endeavor (i.e., data processing), Elchik teaches:
obtaining metadata for the content resource (Fig. 3 shown as content resource; Fig. 5; and par. [0058] “selection of the template having metadata that matches the most of the audience member's attributes.”; and par. [0076] “select a digital media file from a data set of candidate digital media files 501. Each digital media file in the dataset may be associated with metadata descriptive of the programming file and its content, such as a category (sports, political news, music, etc.), a named entity (e.g., sports team, performer, newsworthy individual), and/or other descriptive material….”; and par. [0086])
segmenting the content resource into one or more segments based on the metadata (par. [0060] “Each digital media clip may be a segment of the video and/or audio track of the digital media file from which the sentences and/or key words that are used in the exercise were extracted…”; and pars. [0058, 76, and 86] teach the “metadata”, and pars. [0069-70]);
mapping each segment to at least one metric comprised by the set of skills (see Figs. 3-4; par. [0028] “content of a digital video, to drive learning, the system may lead to improved efficacy in acquisition and improved proficiency in performance in the skills”; pars. [0060] via “segment”, and [0086] via “match” algorithm, and “a similarity metric”. Since the claim does not require any particular “metric”; hence, the lessons and topics for user learning skills of Elchik should be matched as broadest reasonable interpretation. See MPEP 2111); and
updating the content repository of content resources with the segmented content resource and associated mapping (par. [0095] e.g., update the user’s profile store in database, see Fig. 6, in particular at element 606).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson and Elchik because the teachings of Elchik would have provided Jackson with the above indicated limitations for allowing a skilled artisan in motivation as modifying the teachings of cited references to perform updating the users’ profile(s) in content repository of the content source of future used purpose (Elchik: Figs.5-7; Abstract, and pars. [0007-8, 86, and 95]).
Furthermore, in the same field of endeavor (i.e., data processing), Orbach teaches:
obtaining a segment textual description of the segment comprising at least one word (see Fig. 3A, element 210, Fig. 3B; Abstract, e.g., “extracting one or more phrases from the document”, which teaches textual description as broadest reasonable interpretation (see MPEP 2111); and par. [0067] teaches the segment/fragment of the textual description having at least one word);
determining a word-embedding representation of the segment textual description of the segment in a continuous vector space (see in pars. [0066], and [0067] explained above to segment textual description; and par. [0069] e.g., “a vector, commonly referred to as an “embedding vector” in a vector space commonly referred to as an “embedding vector space. Using this embedding vector space representation of words may enable NLP systems… The benefit of this continuous embedding vector representation is that NLP systems may map words that are similar in meaning to similar regions of the embedding vector space (e.g., to be represented by a similar embedding vector). For example, an embedding vector representation of the word “cat” may be similar (according to some predefined metric) to an embedding vector representation of the word “feline” than to an embedding vector representation of the word “piano”.”; and further in pars. [0070-71] teaches word-embedding representation);
obtaining a metric textual description of the metric comprising at least one word (again in Fig. 3B, Fig. 8; and pars. [0066-69]);
determining a word-embedding representation of the metric textual description in the continuous vector space (see in Fig. 3A, element 240A; Figs. 3B and 8; and pars. [0069-71] as disclosed above) and
computing a similarity score between the word-embedding representation of the segment textual description of the segment and the word-embedding representation of the metric textual description of the metric (par. [0069] “… an embedding vector representation of the word “cat” may be similar (according to some predefined metric) to an embedding vector representation of the word “feline” than to an embedding vector representation of the word “piano””; and further in pars. [0075] “weight calculation module 230 may be a calculator of TF-IDF score, and weight 230A may be a TF-IDF score value…”, and [0076-77] via word weight calculation technique for the phrase “something similar”; and par. [0080-91], and [0094] via such the “relevant score”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Elchik and Orbach because the teachings of Orbach would have provided Jackson and Elchik with the above amended limitations for allowing a skilled artisan in motivation to perform the textual description with the word-embedding representation in the continuous vector space based on the calculated similarity score (Orbach: pars. [0069-70]).
Regarding claim 13, Elchik teaches: “wherein the content resource is a video acquired using a camera” (par. [0099] “Data also maybe received from a video capturing device 825 (i.e., camera)”).
Regarding claim 14, Elchik teaches: “wherein the metadata is an outline of information presented in the video” (see Figs. 3-4 as shown the outline of information presented in the displayed video; and par. [0076])
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson, Orbach, and Elchik, and further in view of FOROUGHI et al., US Pub. No. 2022/0130272 A1 (hereinafter as “Foroughi”).
Regarding claim 15, the claim is rejected by the same reasons set forth above to claims 12 and 13. However, Jackson, Orbach, and Elchik do not explicitly teach: “wherein the metadata is a maintenance report comprising a first time data and an activity description of a maintenance activity, the maintenance report acquired from a maintenance work order database, and wherein the maintenance report is assigned to the metadata of the video by a linking method, the linking method comprising: obtaining second time data for the video; and determining that the video records the maintenance activity based on, at least, the first time data and the second time data.”
In the same field of endeavor (i.e., data processing), Foroughi teaches:
wherein the metadata is a maintenance report (see pars. [0113-114] e.g., “metadata” and “… report on the training progress of all users and generate exports such as reports and perform data analysis…”) comprising a first time data and an activity description of a maintenance activity (pars. [0005 and 7], and further in pars. [0090] “Other data which can also assist in the recommendation of additional training can include manager reviews of user work, time to complete a task, and training behavior such as number of times s and how a microlearning module was accessed”, and [0092]; and further in par [0104] “Training tasks can also be time-bound, such as required to be completed by a certain date, required for refreshing every certain period of time …”), the maintenance report acquired from a maintenance work order database (pars. [0092, 0096] teach the maintenance works/tasks order on the work task list storing in task database, see Fig. 1), and
wherein the maintenance report is assigned to the metadata of the video by a linking method (see Figs. 7 and 11 shown as the video linking, pars. [0020] teaches “video” and [0113-114] teach “report”; and further in par. [0087] e.g., “Microlearning can be delivered in a variety of electronic forms in training modules that contain engaging content so that users learn from materials designed to engage learners and maximize knowledge retention. Some formats that training modules can take include but are not limited to one or more of text, slideshow, video, audio, photographs, virtual and augmented reality. Microlearning can also include games, mock exercises similar to real-world tasks, puzzles, mini-tasks or quizzes, and other interactive media. The training modules can also provide one or more links to external resources, recorded or archived material, live coaching, or demonstrations…”), the linking method comprising: obtaining second time data for the video (see Figs. 7 and 11; and par. [0087 and 106]); and determining that the video records the maintenance activity based on, at least, the first time data and the second time data (see pars. [0108-109] and [0113-114] and Figs. 7, 11 and 16 video records the activity based on times/sessions) .
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Elchik, Orbach, and Foroughi because the teachings of Foroughi would have provided Jackson, Elchik, and Orbach with the above indicated limitations for allowing a skilled artisan in motivation as modifying the teachings of cited references to facilitate the report in maintaining and tracking the workers’ activity in certain times/sessions via microlearning content modules (Foroughi: Figs.1, 7, 11-12, and 16; Abstract, and pars. [0005-7, 20, 65, 87], [0108-109] and [0113-114]).
Regarding claim 16, the claim is rejected by the same reasons set forth above to claim 12. Furthermore, Jackson, Orbach, Elchik, and Foroughi teach: “wherein segmenting the content resource into one or more segments based on the metadata is performed by a first artificial intelligence model” (Orbach: par. [0067] and [0069] via such AI NLP; and Forough: pars. [0007] teaches “software projects are segmented into individuals tasks to be done, and each segment or set of segments is assembled or addressed by a different developer or team of developers” wherein the “software projects are interpreted as the content resource, and [0065] “microtraining or microlearning segments”; further in pars. [0120] e.g., “using artificial intelligence (AI)” and par. [0123] “selection of training modules can be identified using search, tags, artificial intelligence based on previous identification of relevant tasks, or a combination thereof. Further, text and audio in the training modules can be used as metadata tags to bring forward training modules relevant to a particular task or ticket…”).
Regarding claim 17, the claim is rejected by the same reasons set forth above to claim 12. Furthermore, Orbach, and Elchik teach: wherein mapping each segment to at least one metric comprised by the set of skills comprises:
for each segment: obtaining a segment textual description of the segment comprising at least one word (Orbach: see pars. [0067-68]; and Elchik: see pars. [0043-48] via “text segments” algorithm; par. [0060] “Each digital media clip may be a segment of the video and/or audio track of the digital media file from which the sentences and/or key words that are used in the exercise were extracted…”);
for each metric: obtaining a metric textual description of the metric comprising at least one word (Orbach: Figs. 3B and 8, pars. [0067-69]; and Elchik: see Figs. 3-4 as shown the metric textual description; and par. [0084] teaches the metric in such “text analysis techniques 602, such as classification/categorization to extract topics such as “sports” or “politics” or more refined topics such as “World Series” or “Democratic primary.” The methods used for automated topic categorization may be based on the presence of keywords and key phrases”; and par. [0086] via “match” algorithm, and “a similarity metric”. ***Examiner’s note: Since the claim does not require any particular “metric”; hence, the lessons and/or topics of Elchik should be matched as broadest reasonable interpretation. See MPEP 2111);
determining, with a second artificial intelligence (AI) model, a similarity score between the segment and the metric based on the segment textual description and the metric textual description (Orbach: see in Figs. 3A and 8, and par. [0069-74] and [0094] via such the “relevant score”; and Elchik: pars. [0062-63] teach “screening score” and the “point scores” using any suitable algorithm or trained model, wherein the “trained model” is interpreted as the AI model; and wherein the “parameters” are interpreted as the textual description of the media file equivalent to segment and metric as broadest reasonable interpretation. See MPEP 2111) ;
comparing the similarity score to a similarity threshold (Elchik: see par. [0062] e.g., “if a screening score generated based on an analysis of one or more screening parameters exceeds a threshold”); and
mapping the segment to the metric if the similarity score is greater than the similarity threshold (Elchik: see pars. [0060] “text segments”, and [0062] e.g., “if a screening score generated based on an analysis of one or more screening parameters exceeds a threshold”, and further in pars. [0084 and 86] as implementing the metric(s) technique).
*** Examiner’s notes:
Independent claim 18 is a system claim. In contrast, the claim recites the limitations which are similar to limitations in claim 1 and claim 5. Therefore, the claim is also rejected on the same analysis and cited quotations of above claims 1 and 5, in combination respectively.
Claim 19 depends to claim 18. In contrast, the claim recites the limitations which are similar to limitations in claim 11. Therefore, the claim is also rejected on the same analysis and cited quotations of above claim 11, respectively.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson, Orbach, Brown, Xiong, Elchik, and Foroughi, and further in view of Briffa et al., US Pub. No. 2021/0082064 A1 (hereinafter as “Briffa”).
Regarding claim 20, the claim is rejected by the same reasons set forth above to claim 18. Furthermore, Jackson, Orbach, Brown, Xiong, Elchik and Foroughi, in combination, teach:
receive an input creating an empty repository of content resources (Jackson: Figs. 2-3 as shown the obtained content database/repository having content resources; and par. [0019] e.g., “Database 134 may store any type of data accessible by server(s) 132 including, for example, various content items registered as resources associated with users 102, 104 and 106),
receive a content resource from the data acquisition system (Jackson: again in Figs. 2-3, and par. [0019]; Xiong: see Figs. 1-2 and pars. [0025-27]; and Foroughi: see Fig. 3, and par. [0087] “provide one or more links to external resources”, [0093] and [0106] e.g., “Micro learning modules can comprise various types of media and interactive resources to engage the user and provide interactive opportunities for the user to learn the material”),
receive, from the data acquisition system, metadata for the content resource (Jackson: Figs. 2-3 as shown the obtained content database/repository having content resources; and Fig. 3; and par. [0019] e.g., “Database 134 may store any type of data accessible by server(s) 132 including, for example, various content items registered as resources associated with users 102, 104 and 106”; Xiong: see at Fig. 2 via Resources data/metadata; Orbach: Fig. 7 at element 21B - Metadata; and Elchik: Fig. 3 shown as content resource; Fig. 5; and par. [0058] “selection of the template having metadata that matches the most of the audience member's attributes.”; and par. [0076] “select a digital media file from a data set of candidate digital media files 501. Each digital media file in the dataset may be associated with metadata descriptive of the programming file and its content, ...”; and par. [0086]),
segment the content resource into one or more segments based on the metadata (Orbach: see par. [0067] via such the fragment; and Elchik: par. [0060] “Each digital media clip may be a segment of the video and/or audio track of the digital media file from which the sentences and/or key words that are used in the exercise were extracted…”; and pars. [0058, 76, and 86] teach the “metadata”, and pars. [0069-70]),
receive a set of skills, each skill in the set of skills comprising at least one metric (Jackson: see pars. [0024] “certain skill-sets”, [0025] “information related to a set of skills 102a associated with the user. For example, the set of skills 102a may be related to the user's profession. In an example, the information registered by user 102 may include profile information identifying the user as a medical doctor…”, and [0045] e.g., “set of skills” associated with the first user/individual; Brown: see pars. [0125-126] teach receiving skill/task from skill/task models for scoring purpose; and Elchik: [0086] e.g., “a similarity metric”),
map each segment to at least one metric comprised by the set of skills (Elchik: see Figs. 3-4; par. [0028] “content of a digital video, to drive learning, the system may lead to improved efficacy in acquisition and improved proficiency in performance in the skills”; and pars. [0060] via “segment”, and [0086] via “match” algorithm, and “a similarity metric”. Since the claim does not require any particular “metric”; hence, the lessons and topics for user learning skills of Elchik should be matched as broadest reasonable interpretation. See MPEP 2111), and
update the content repository of content resources with the segmented content resource and associated mapping (Elchik: see par. [0095] e.g., update the user’s profile store in database, see Fig. 6, in particular at element 606),
for each segment: obtaining a segment textual description of the segment comprising at least one word (Elchik: see pars. [0043-48] via “text segments” algorithm; par. [0060] “Each digital media clip may be a segment of the video and/or audio track of the digital media file from which the sentences and/or key words that are used in the exercise were extracted…”); and
for each metric: obtaining a metric textual description of the metric comprising at least one word (Orbach: see Fig. 8 and pars. [0066-69]; and Elchik: see Figs. 3-4 as shown the metric textual description; and par. [0084] teaches the metric in such “text analysis techniques 602, such as classification/categorization to extract topics such as “sports” or “politics” or more refined topics such as “World Series” or “Democratic primary.” The methods used for automated topic categorization may be based on the presence of keywords and key phrases”; and par. [0086] via “match” algorithm, and “a similarity metric”. ***Examiner’s note: Since the claim does not require any particular “metric”; hence, the lessons and/or topics of Elchik should be matched as broadest reasonable interpretation. See MPEP 2111);
determining, with an artificial intelligence (AI) model, a similarity score between the segment and the metric based on the segment textual description and the metric textual description (Orbach: see Fig. 3A and 8, and pars. [0069-74] and [0094] via “relevant score”; and Elchik: pars. [0062-63] teach “screening score” and the “point scores” using any suitable algorithm or trained model, wherein the “trained model” is interpreted as the AI model; and wherein the “parameters” are interpreted as the textual description of the media file equivalent to segment and metric as broadest reasonable interpretation. See MPEP 2111);
making a second determination whether the similarity score is greater than a similarity threshold (Elchik: see par. [0062] e.g., “if a screening score generated based on an analysis of one or more screening parameters exceeds a threshold”); and
based on the second determination that the similarity score is determined greater than the similarity threshold, mapping the segment to the metric (Elchik: see pars. [0060] “text segments”, and [0062] e.g., “if a screening score generated based on an analysis of one or more screening parameters exceeds a threshold”, and further in pars. [0084 and 86] as implementing the metric(s) technique).
Jackson, Brown, Xiong, Elchik, and Foroughi are analogous because they are directed to the same field of invention as data processing and manipulating including the obtaining/receiving content resource, metadata, and skills, segmenting and mapping the content resource, textual description, determining score, etc.
Before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art having the teachings of Jackson, Orbach, Brown, Xiong, Elchik, and Foroughi to combine the teachings of cited references as modified to include the above indicated limitations. Thus, one of ordinary skill in the art would have been motivated to make this change in order to provide data manipulations in updating the content repository of the content resources for individual accessing future.
Therefore, it would have been obvious to combine Jackson, Brown, Xiong, Elchik, and Foroughi to obtain the instant claim.
Examiner respectfully submits that Jackson teaches computing system having at least one database as resource database (see Figs. 1-3), Brown teaches using machine learning for facilitating performance of skills based on skills scores (see Figs. 2A-2F, 6 and 11), Xiong teaches hardware platform (see Fig. 4), Elchik teaches the generating digital lessons system associated with video content resource which were/are captured by camera (see Figs. 1-5, and par. [0099]), and Foroughi teaches the just-in-time training system and method having at least streaming video online works/tasks training via microlearning, microtraining models (see Figs. 1-8, 13, and 16; and par. [0087]).
However, Jackson, Orbach, Brown, Xiong, Elchik, and Foroughi do not explicitly teaches: “a meeting room, a first camera installed in the meeting room, a field facility, wherein process data for the field facility is stored in a field facility database, a second camera installed in the field facility.”
In the same field of endeavor (i.e., data processing), Briffa teaches the subject matter generally relates to the field of facility management. In particular, Briffa teaches “systems and methods for monitoring and managing a plurality of facilities, such as buildings and equipment, in real-time.” (see Abstract, and par. [0001]). Therefore, Briffa teaches:
“a meeting room” (see par. [0078] e.g., “a range of facilities/services of the buildings 104A-104N including meeting and conference rooms, …”),
“a first camera installed in the meeting room” (par. [0048] “high quality 360-degree cameras, are configured to be installed at strategic locations in different areas within or around the respective facility…”),
“a field facility, wherein process data for the field facility is stored in a field facility database” (see par. [0001] e.g., “the field of facility management” implies to the field facility; and par. [0053] “he storage module of the centralised facility management system 102 may be a central repository of building (or facility) documentation, information, building plans, asset locations, procedures and rules. Authorised residents, investors, lawyers, conveyancers and real-estate agents, stakeholders etc. can access the important documents in one central database, i.e. the storage module…”),
“a second camera installed in the field facility” (par. [0048] “high quality 360-degree cameras, are configured to be installed at strategic locations in different areas within or around the respective facility…”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of Jackson, Orbach, Brown, Xiong, Elchik, Foroughi, and Briffa because the teachings of Briffa would have provided J Jackson, Orbach, Brown, Xiong, Elchik, and Foroughi with the above indicated limitations for allowing a skilled artisan in motivation as combining or modifying the teachings of cited references to field facility in assisting with monitoring people in the meeting, conference rooms efficiently (Briffa: Abstract, pars. [0001, 48, 53, and 78]).
Response to Arguments
Referring to claim rejections under 35 U.S.C. 101 as being abstract idea, (see Remarks, pages 11-18) have been fully considered, but are not persuasive.
a/ Per Applicant’s Remarks (pages 13-15): Examiner respectfully submits that according new update 2019 PEG (under step 2A, prong I), examiner respectfully submits that independent claims 1, 12, and 18 are directed to abstract idea as under analysis Step 2A (Prong I) because the claim recites steps of “extracting” and “determining”, as drafted, are mental processes that, under its broadest reasonable interpretation, cover performance of the limitations of concepts that are performed in mind (e.g., an observation, evaluation, judgment, option, etc.) but for the recitation of generic computer’s component(s). As results, the indicated steps in claims 1, 12, and 18 fall within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a)(2), Part III. Plus, the amended step of “computing a similarity/score” falls within the “Mathematical Concepts”.
b/ Per Applicant’s Remarks (pages 16-18): Examiner respectfully submits that the remaining limitations in claims 1, 12, and 18 do not integrate the judicial exception into a practical application. Please see the above set forth rejections for details under analysis of Steps 2A (Prong II) and Step 2B.
For at least above reasons, the rejections are maintained.
Referring to claim rejections under 35 U.S.C. 103, Applicant’s arguments with respective claims 1, 12, and 18 (see Remarks in particular page 19 to the amended limitations, e.g., “using an AI NLP model to perform the text extraction from limitations (i) and the word embedding operations from limitation (ii)”) have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant's amendment to the claims. Applicant's newly amended features are taught implicitly, expressly, or impliedly by the prior art of record.
Any other claims argued merely because of a dependency on a previouslyargued claim(s) in the arguments presented to the examiner, 12/19/2025, are moot in view of the examiner's interpretation of the claims and art and are still considered rejected based on their respective rejections from at least a prior Office action (part(s) of recited above).
Prior Arts
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)); Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jessica N. Le whose telephone number is (571)270-1009. The examiner can normally be reached M-F 9:30 am - 5:30 pm (EST).
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/Jessica N Le/ /MD I UDDIN/ Primary Examiner, Art Unit 2169 Examiner, Art Unit 2169