DETAILED ACTION
Response to Amendment
Claims 1, 6, 7, 18 and 19 are currently pending.
In the Amendment filed 27 February 2026, claims 1, 6, 9, 10 and 18 are amended and claims 2-5, 8, 11-17 and 20 are cancelled. This action is 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3 December 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 6, 7, 18 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106.
Claim 1 recites training a first artificial intelligence mechanism using historical user descriptions and known metadata for specific training content; training a second artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content; training a third artificial intelligence mechanism using historical user descriptions of known audio and known metadata for specific training content; generating a metadata database for a plurality of content, including: for each corresponding content of the plurality of content: generating first metadata for the corresponding content by employing the first artificial intelligence mechanism on user descriptions of the corresponding content; generating second metadata for the corresponding content by employing the second artificial intelligence mechanism on each video scene of a video portion of the corresponding content; generating third metadata for the corresponding content by employing the third artificial intelligence mechanism on an audio portion of the corresponding content; mapping the first metadata, the second metadata, and the third metadata to the corresponding content; and storing the first metadata, the second metadata, and the third metadata in the metadata database and storing the mapping of the first metadata, the second metadata, and the third metadata to the corresponding content; receiving input from a user; employing a fourth artificial intelligence mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms; searching the metadata database for metadata matching the threshold number of terms from plurality of searchable terms, including: in response to matching the threshold number of terms plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata terms in the metadata database; and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that no metadata match is found between the plurality of searchable terms and metadata in the metadata database; in response to identifying the metadata match, identifying target content from the plurality of content mapped to the matched metadata; and providing the target content to the user.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The limitations of generating a metadata database for a plurality of content, including: for each corresponding content of the plurality of content: generating first metadata for the corresponding content on user descriptions of the corresponding content; generating second metadata for the corresponding content on each video scene of a video portion of the corresponding content; generating third metadata for the corresponding content on an audio portion of the corresponding content; mapping the first metadata, the second metadata, and the third metadata to the corresponding content; and storing the first metadata, the second metadata, and the third metadata in the metadata database and storing the mapping of the first metadata, the second metadata, and the third metadata to the corresponding content; generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms; searching the metadata database for metadata matching the threshold number of terms from plurality of searchable terms, including: in response to matching the threshold number of terms plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata terms in the metadata database; and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that no metadata match is found between the plurality of searchable terms and metadata in the metadata database; in response to identifying the metadata match, identifying target content from the plurality of content mapped to the matched metadata, as drafted, are processes that, under their broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by employing a first artificial intelligence mechanism,” “by employing a second artificial intelligence mechanism,” “employing a third artificial intelligence mechanism” and “employing a fourth artificial intelligence mechanism,” nothing in the claim limitations precludes the steps from practically being performed in the mind. For example, the limitations encompass a person generating a metadata database in the form of a table containing metadata generated based on descriptions of corresponding content and the content itself. The person can then expand a query by coming up with synonyms. Next, the person can then search the table for a match using a threshold number of matching terms by comparing metadata in the table to the query terms. The threshold can dynamically be selected by the person. If claim limitations under their broadest reasonable interpretation, covers performance of a limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
This judicial exception is not integrated into a practical application. The claim recites the additional elements of a first artificial intelligence mechanism, a second artificial intelligence mechanism, a third artificial intelligence mechanism and a fourth artificial intelligence mechanism. The artificial intelligence mechanisms are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of each artificial intelligence mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The claim also recites the additional limitations of receiving input from a user and providing the target content to the user. Each of these limitations are merely adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since they are obtaining and outputting information. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). Accordingly, these additional limitations and elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional elements of a first artificial intelligence mechanism, a second artificial intelligence mechanism, a third artificial intelligence mechanism and a fourth artificial intelligence mechanism. The artificial intelligence mechanisms are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of each artificial intelligence mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The claim also recites the additional limitations of receiving input from a user and providing the target content to the user. Each of these limitations are merely adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since they are obtaining and outputting information. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The limitation is directed to IESA of obtaining and outputting information, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, electronically scanning or extracting data from a physical document, a web browser’s back and forward button functionality, recording a customer’s order, shuffling and dealing a standard deck of cards, restricting public access to media by requiring a consumer to view an advertisement, presenting offers and gathering statistics, determining an estimated outcome and setting a price, arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II and the Berkheimer Memo. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not patent eligible.
Claim 10 recites train a plurality of artificial intelligent mechanisms, including: train a first artificial intelligence mechanism using historical user descriptions and known metadata for specific training content; train a second artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content; train a third artificial intelligence mechanism using historical user descriptions of known audio and known metadata for specific training content; generate a metadata database by employing the plurality of artificial intelligence mechanisms for each corresponding content to generate metadata, including: employ the first artificial intelligence mechanism on user descriptions of the corresponding content to generate first metadata for the corresponding content; employ the second artificial intelligence mechanism on each video scene of a video portion of the corresponding content to generate second metadata for the corresponding content; employ the third artificial intelligence mechanism on an audio portion of the corresponding content to generate third metadata for the corresponding content; map the first metadata, the second metadata, and the third metadata to the corresponding content; receive input from a user; employ a fourth artificial intelligence mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; access the metadata database to identify metadata that matches the plurality of searchable terms, including: dynamically set a threshold number of terms based on a number of searchable terms in the plurality of searchable terms; search the metadata database for metadata matching the threshold number of terms from plurality of searchable terms; in response to matching the threshold number of terms plurality of searchable terms to metadata in the metadata database, determine that a metadata match is found between the plurality of searchable terms and the matched metadata terms in the metadata database; in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determine that no metadata match is found between the plurality of searchable terms and metadata in the metadata database; and in response to identifying the metadata match, identifying target content from the plurality of content mapped to the matched metadata; and provide the target content to the user.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The limitations of generate a metadata database by employing the plurality of artificial intelligence mechanisms for each corresponding content to generate metadata, including: employ on user descriptions of the corresponding content to generate first metadata for the corresponding content; employ on each video scene of a video portion of the corresponding content to generate second metadata for the corresponding content; employ on an audio portion of the corresponding content to generate third metadata for the corresponding content; map the first metadata, the second metadata, and the third metadata to the corresponding content; employ on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; access the metadata database to identify metadata that matches the plurality of searchable terms, including: dynamically set a threshold number of terms based on a number of searchable terms in the plurality of searchable terms; search the metadata database for metadata matching the threshold number of terms from plurality of searchable terms; in response to matching the threshold number of terms plurality of searchable terms to metadata in the metadata database, determine that a metadata match is found between the plurality of searchable terms and the matched metadata terms in the metadata database; in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determine that no metadata match is found between the plurality of searchable terms and metadata in the metadata database; and in response to identifying the metadata match, identifying target content from the plurality of content mapped to the matched metadata, as drafted, are processes that, under their broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “at least one memory,” “a processor system,” “by employing a first artificial intelligence mechanism,” “by employing a second artificial intelligence mechanism,” “employing a third artificial intelligence mechanism” and “employing a fourth artificial intelligence mechanism,” nothing in the claim limitations precludes the steps from practically being performed in the mind. For example, the limitations encompass a person generating a metadata database in the form of a table containing metadata generated based on descriptions of corresponding content and the content itself. The person can then expand a query by coming up with synonyms. Next, the person can then search the table for a match using a threshold number of matching terms by comparing metadata in the table to the query terms. The threshold can dynamically be selected by the person. If claim limitations under their broadest reasonable interpretation, covers performance of a limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
This judicial exception is not integrated into a practical application. The claim recites the additional elements of a memory, a processor system, a first artificial intelligence mechanism, a second artificial intelligence mechanism, a third artificial intelligence mechanism and a fourth artificial intelligence mechanism. The artificial intelligence mechanisms, memory and processor system are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of each artificial intelligence mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The claim also recites the additional limitations of receiving input from a user and providing the target content to the user. Each of these limitations are merely adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since they are obtaining and outputting information. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). Accordingly, these additional limitations and elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional elements of a memory, a processor, a first artificial intelligence mechanism, a second artificial intelligence mechanism, a third artificial intelligence mechanism and a fourth artificial intelligence mechanism. The artificial intelligence mechanisms, processor system and memory are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of each artificial intelligence mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The claim also recites the additional limitations of receiving input from a user and providing the target content to the user. Each of these limitations are merely adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since they are obtaining and outputting information. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The limitation is directed to IESA of obtaining and outputting information, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, electronically scanning or extracting data from a physical document, a web browser’s back and forward button functionality, recording a customer’s order, shuffling and dealing a standard deck of cards, restricting public access to media by requiring a consumer to view an advertisement, presenting offers and gathering statistics, determining an estimated outcome and setting a price, arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II and the Berkheimer Memo. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not patent eligible.
Claim 18 recites training a first artificial intelligence mechanism using historical user descriptions and known metadata for specific training content; training a second artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content; training a third artificial intelligence mechanism using historical user descriptions of known audio and known metadata for specific training content; generating a metadata database for a plurality of content, including: for each corresponding content of the plurality of content: generating first metadata for the corresponding content by employing the first artificial intelligence mechanism on user descriptions of the corresponding content; generating second metadata for the corresponding content by employing the second artificial intelligence mechanism on each video scene of a video portion of the corresponding content; generating third metadata for the corresponding content by employing the third artificial intelligence mechanism on an audio portion of the corresponding content; mapping the first metadata, the second metadata, and the third metadata to the corresponding content; and storing the first metadata, the second metadata, and the third metadata in the metadata database and storing the mapping of the first metadata, the second metadata, and the third metadata to the corresponding content; receiving input from a user; employing a fourth artificial intelligence mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms; searching the metadata database for metadata matching the threshold number of terms from plurality of searchable terms, including: in response to matching the threshold number of terms plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata terms in the metadata database; and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that no metadata match is found between the plurality of searchable terms and metadata in the metadata database; in response to identifying the metadata match, identifying target content from the plurality of content mapped to the matched metadata; and providing the target content to the user.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The limitations of generating a metadata database for a plurality of content, including: for each corresponding content of the plurality of content: generating first metadata for the corresponding content on user descriptions of the corresponding content; generating second metadata for the corresponding content on each video scene of a video portion of the corresponding content; generating third metadata for the corresponding content on an audio portion of the corresponding content; mapping the first metadata, the second metadata, and the third metadata to the corresponding content; and storing the first metadata, the second metadata, and the third metadata in the metadata database and storing the mapping of the first metadata, the second metadata, and the third metadata to the corresponding content; generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms; searching the metadata database for metadata matching the threshold number of terms from plurality of searchable terms, including: in response to matching the threshold number of terms plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata terms in the metadata database; and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that no metadata match is found between the plurality of searchable terms and metadata in the metadata database; in response to identifying the metadata match, identifying target content from the plurality of content mapped to the matched metadata, as drafted, are processes that, under their broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by employing a first artificial intelligence mechanism,” “by employing a second artificial intelligence mechanism,” “employing a third artificial intelligence mechanism” and “employing a fourth artificial intelligence mechanism,” nothing in the claim limitations precludes the steps from practically being performed in the mind. For example, the limitations encompass a person generating a metadata database in the form of a table containing metadata generated based on descriptions of corresponding content and the content itself. The person can then expand a query by coming up with synonyms. Next, the person can then search the table for a match using a threshold number of matching terms by comparing metadata in the table to the query terms. The threshold can dynamically be selected by the person. If claim limitations under their broadest reasonable interpretation, covers performance of a limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
This judicial exception is not integrated into a practical application. The claim recites the additional elements of a medium, instructions, a processor, a first artificial intelligence mechanism, a second artificial intelligence mechanism, a third artificial intelligence mechanism and a fourth artificial intelligence mechanism. The artificial intelligence mechanisms, medium, instructions and processor are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of each artificial intelligence mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The claim also recites the additional limitations of receiving input from a user and providing the target content to the user. Each of these limitations are merely adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since they are obtaining and outputting information. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). Accordingly, these additional limitations and elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional elements of a medium, instructions, a processor, a first artificial intelligence mechanism, a second artificial intelligence mechanism, a third artificial intelligence mechanism and a fourth artificial intelligence mechanism. The artificial intelligence mechanisms, medium, instructions and processor are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of each artificial intelligence mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The claim also recites the additional limitations of receiving input from a user and providing the target content to the user. Each of these limitations are merely adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since they are obtaining and outputting information. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The limitation is directed to IESA of obtaining and outputting information, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, electronically scanning or extracting data from a physical document, a web browser’s back and forward button functionality, recording a customer’s order, shuffling and dealing a standard deck of cards, restricting public access to media by requiring a consumer to view an advertisement, presenting offers and gathering statistics, determining an estimated outcome and setting a price, arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II and the Berkheimer Memo. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not patent eligible.
Dependent claims 6, 7 and 19 are directed to the abstract idea of the independent claims. The limitations of each independent claim are also directed to “Mental Processes.” Therefore, there are no additional elements to integrate the abstract idea into a practical application by imposing any meaningful limits on practicing the abstract idea or to provide an inventive concept. The claims are not patent eligible.
Dependent claim 9 is directed to the abstract idea of the independent claims. The additional elements of each of the dependent claims are merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(g)). Therefore, the additional elements do not integrate the abstract idea into a practical application by imposing any meaningful limits on practicing the abstract idea or provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2011/0238754 to Dasilva et al (hereafter Dasilva) in view of US PGPub 2022/0132179 to Bennett-James et al (hereafter Bennett) in view of US PGPub 2014/0074866 to Shah et al (hereafter Shah) in view of US PGPub 2021/0103615 to Jindal et al (hereafter Jindal) in view of US PGPub 2024/0095275 to Tambi et al (hereafter Tambi) in view of US PGPub 2025/0200983 to Li et al (hereafter Li).
Referring to claim 1, Dasilva discloses a method, comprising:
generating a metadata database for a plurality of content (see [0041]-[0043] – The front end interface receives video files uploaded from the content provider and delivers the video files to the upload server. The upload server processes the video files and stores the video files in the video database. The video database stores metadata of the video files.), including:
for each corresponding content of the plurality of content:
generating first metadata [any of a title; description; tag information; and administrative rights of a video file] for the corresponding content (see [0043] – The video database stores metadata of the video files. For example, the video database stores one or more of: a title; description; tag information; and administrative rights of a video file.);
generating second metadata [any of a title; description; tag information; and administrative rights of a video file that was not chosen as the first metadata] for the corresponding content (see [0043]);
generating third metadata [any of a title; description; tag information; and administrative rights of a video file that was not chosen as the first or second metadata] for the corresponding content (see [0043]);
mapping the first metadata, the second metadata and the third metadata to the corresponding content (see [0042]; [0043] – The video database is construed as providing the mapping between different categories of metadata and the video ID.); and
storing the first metadata, the second metadata and the third metadata in the metadata database and storing the mapping between the first metadata, the second metadata and the third metadata mapped to the corresponding content (see [0042]; [0043] – The video database is construed as storing the mapping between different categories of metadata and the video ID.);
receiving input from a user [search queries received by the front end interface from users] (see [0044], lines 1-6);
searching the metadata database for metadata matching the input (see [0044], lines 6-15 – The video search module uses the search criteria to query the metadata of video files stored in the video database and returns the search results to the user via the front end interface. For example, if a user provides a keyword search query to the video search module via the front end interface, the video search module identifies videos stored in the video database matching the keyword.);
in response to identifying a metadata match, identifying target content from the plurality of content mapped to matched metadata (see [0044], lines 6-15 – The video search module uses the search criteria to query the metadata of video files stored in the video database and returns the search results to the user via the front end interface. For example, if a user provides a keyword search query to the video search module via the front end interface, the video search module identifies videos stored in the video database matching the keyword.); and
providing the target content to the user (see [0044], lines 13-15 – returns search results to the user via the front end interface).
While Dasilva teaches the generation of first metadata, second metadata and third metadata, Dasilva fails to explicitly teach the use of artificial intelligence to generate the metadata. Bennett teaches the use of machine learning to generate metadata for content, including the further limitations of
training a first artificial intelligence mechanism [machine learning model] using known metadata for specific training content (see [0028]; [0035]; [0115] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions.);
training a second artificial intelligence mechanism [machine learning model] using known metadata for specific training content (see [0028]; [0035]; [0115] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions.);
training a third artificial intelligence mechanism [machine learning model] using historical user descriptions of known audio and known metadata for specific training content (see [0028]; [0035]; [0092] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions. Using data from existing audio descriptions the model can be trained);
for each corresponding content of the plurality of content (see [0034]):
generating first metadata for the corresponding content by employing an a first artificial intelligence mechanism (see [0035]; [0036]);
generating second metadata for the corresponding content by employing an the second artificial intelligence mechanism on the corresponding content to generate a metadata description of each scene (see [0035]; [0036] - in one illustrative example using a video as an example of an item of media content, the machine learning system 104 can use the one or more machine learning models to recognize one or more characters in a scene of the video, detect various objects in the scene, determine which actions the character(s) and/or object(s) are performing in the scene, determine an emotion of the character(s), determine a path or trajectory of the character(s) and/or object(s) in the scene, and determine a sentiment of the scene (e.g., positive, negative, etc.).);
generating third metadata for the corresponding content by employing the artificial intelligence mechanism on an audio portion of the corresponding content (see [0057] and [0062] - The process 300 (and the processes 400-600 of FIG. 4-FIG. 6) is described as generating an audio description output.);
mapping the first metadata, the second metadata, and the third metadata to the corresponding content (see [0005], lines 1-3 - Techniques and systems are described herein for annotating media content using metadata generated using one or more machine learning models.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use a plurality of artificial intelligence mechanisms, each trained separately to generate metadata as taught by Bennett to generate the metadata of Dasilva. One would have been motivated to do so in order to provide a system which can quickly and efficiently annotate media with metadata (Bennett: see [0004]; [0005]).
While the combination of Dasilva and Bennett (hereafter Dasilva/Bennett) teaches generating first metadata for the corresponding content by employing an a first artificial intelligence mechanism and that the metadata can be descriptions, Dasilva/Bennett fails to explicitly teach the further limitation of employing an a first artificial intelligence mechanism on user descriptions of the corresponding content. Shah teaches the creation of metadata for a video file, including the further limitation of
generating first metadata for the corresponding content by employing an artificial intelligence mechanism [machine learning] on user descriptions of the corresponding content (see [0020]; [0026]; [0036]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to apply the machine learning algorithm of Dasilva/Bennett to user descriptions of content as taught by Shah. One would have been motivated to do so since this is merely a specific function being performed on a specific type of data and Dasilva teaches description as a type of metadata (Dasilva: see [0043]) and Bennett teaches that the machine learning model can be trained to perform any function (Bennett: see [0035]).
While the combination of Dasilva/Bennett and Shah (hereafter Dasilva/Bennett/Shah) teaches searching for content with a user input, Dasilva/Bennett/Shah fails to explicitly teach the further limitations of employing a third artificial intelligence mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; searching the metadata database for metadata matching the plurality of searchable terms, including: selecting a threshold number of terms based on the plurality of searchable terms; and identifying a metadata match where the threshold number of terms in the plurality of searchable terms match metadata terms in the metadata database. Jindal teaches a multimedia content search using content tags, including the further limitations of
receiving input [search query] from a user (see [0060], lines 1-6 – The process 200 involves receiving a search query that includes a keyword set. The image processing system can receive, during a session with a client device, the search query from the client device via the data network.);
employing a mechanism [image processing system] on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input (see [0018]; [0060] – The image processing system can identify a keyword set from the search query. In some embodiments, the keyword set includes one or more keywords provided by the client device 106. In additional or alternative embodiments, the keyword set includes variants of one or more keywords provided by the client device 106 (e.g., synonyms of a user-provided keyword, a root word of a user-provided keyword, etc.));
dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms (see [0064] – The keyword search engine communicates with the multimedia database to determine whether content tags associated with a particular video file includes a sufficient number of content tags that match a keyword set in a search query received at block 202.);
searching the metadata database [multimedia database] for metadata [content tags/file metadata] matching the threshold number of terms from plurality of searchable terms (see [0064]), including:
in response to matching the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata in the metadata database (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202.); and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that metadata match is found between the plurality of searchable terms and metadata in the metadata database (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202. This is construed as if not then the video file is not retrieved.);
identifying a metadata match where the threshold number of terms in the plurality of searchable terms [keyword set] match metadata terms [content tags/file metadata] in the metadata database [multimedia database] (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the searching process of Jindal to identify the metadata match of Dasilva/Bennett/Shah. One would have been motivated to do so in order to efficiently search for and locate desirable multimedia content (Jindal: see [0002]).
While the combination of Dasilva/Bennett/Shah and Jindal (hereafter Dasilva/Bennett/Shah/Jindal) teaches employing a mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input, Dasilva/Bennett/Shah/Jindal fails to explicitly teach that the mechanism is a fourth artificial intelligence mechanism. Tambi teaches query processing, including the further limitations of
employing a fourth artificial intelligence mechanism [artificial neural network (ANN)] on the input [original query] to generate a plurality of searchable terms [expanded query], wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input (see [0041] – Query processing apparatus 200 includes a computer implemented artificial neural network (ANN) for augmenting an original query with an additional phrase to obtain an expanded query.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use an ANN to expand the queries of Dasilva/Bennett/Shah/Jindal as taught by Tambi. One would have been motivated to do so to provide a process that can automatically generate alternative or augmented queries based on an original query from a user (Tambi: see [0002]).
While Dasilva/Bennett/Shah/Jindal/Tambi teaches training of a plurality of artificial intelligence mechanisms, Dasilva/Bennett/Shah/Jindal/Tambi fails to explicitly teach training a first artificial intelligence mechanism using historical user descriptions and known metadata for specific training content and training a second artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content. Li teaches training an artificial intelligence model, including the further limitations of
training an artificial intelligence mechanism using historical user descriptions and known metadata for specific training content (see [0075] - In the method for training an autonomous driving model provided in the embodiment of the present disclosure, an initial video frame collected by a target vehicle and/or scenario description metadata of the initial video frame are used to determine a scenario context of the initial video frame; a vehicle movement instruction of the target vehicle is determined using the initial video frame and/or trajectory data of the target vehicle corresponding to the initial video frame; and an initial model is trained using the initial video frame and a corresponding control text to obtain the video prediction model, where the control text includes the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.); and
training an artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content (see [0075] - In the method for training an autonomous driving model provided in the embodiment of the present disclosure, an initial video frame collected by a target vehicle and/or scenario description metadata of the initial video frame are used to determine a scenario context of the initial video frame; a vehicle movement instruction of the target vehicle is determined using the initial video frame and/or trajectory data of the target vehicle corresponding to the initial video frame; and an initial model is trained using the initial video frame and a corresponding control text to obtain the video prediction model, where the control text includes the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the training content of Li to train the first and second mechanism of Dasilva/Bennett/Shah/Jindal/Tambi. One would have been motivated to do so in order to provide necessary content required to train a model (Bennett: see [0035]; Li: see [0077]).
Referring to claim 6, the combination of Dasilva/Bennett/Shah/Jindal/Tambi and Li (hereafter Dasilva/Bennett/Shah/Jindal/Tambi/Li) teaches the method of claim 1, further comprising: for each corresponding content of the plurality of content: generating fourth metadata for the corresponding content from details of the corresponding content (Shah: see [0022]; [0029]; Dasilva: see [0043]); and storing the fourth metadata in the metadata database and mapped to the corresponding content (Shah: see [0020]; [0032]; [0035]; [0054]; Dasilva: see [0043]).
Referring to claim 7, Dasilva/Bennett/Shah/Jindal/Tambi/Li teaches the method of claim 1, wherein the details of the corresponding content includes one or more of: title, character name [speaker identities], cast name, length, genre [topics being discussed], or release date (Shah: see [0029]; Dasilva: see [0035] - title).
Referring to claim 9, Dasilva/Bennett/Shah/Jindal/Tambi teaches the method of claim 1, wherein generating the first metadata for the corresponding content comprises: obtaining user-generated descriptions on the corresponding content from a plurality of users (Shah: see [0026]; [0036]); and employing an artificial intelligence mechanism on the user-generated descriptions to generate the first metadata for the corresponding content (Shah: see [0020]; [0021]; [0026]).
Referring to claim 10, Dasilva discloses a computing system, comprising:
at least one memory configured to store computer instructions (see [0025]); and
a processor system configured to execute the computer instructions (see [0028]) to:
generate a metadata database for each corresponding content of a plurality of content to generate metadata (see [0041]-[0043] – The front end interface receives video files uploaded from the content provider and delivers the video files to the upload server. The upload server processes the video files and stores the video files in the video database. The video database stores metadata of the video files.), including:
employ on user descriptions of the corresponding content to generate first metadata [any of a title; description; tag information; and administrative rights of a video file] for the corresponding content (see [0043] – The video database stores metadata of the video files. For example, the video database stores one or more of: a title; description; tag information; and administrative rights of a video file.);
generate second metadata for the corresponding content [any of a title; description; tag information; and administrative rights of a video file that was not chosen as the first metadata] for the corresponding content (see [0043]);
generate third metadata for the corresponding content [any of a title; description; tag information; and administrative rights of a video file that was not chosen as the first or second metadata] for the corresponding content (see [0043]);
map the first metadata, the second metadata and the third metadata to the corresponding content (see [0042]; [0043] – The video database is construed as providing the mapping between different categories of metadata and the video ID.); and
receive input from a user [search queries received by the front end interface from users] (see [0044], lines 1-6);
access the metadata database to identify metadata that matches the plurality of searchable terms, including:
search the metadata database for metadata matching the plurality of searchable terms (see [0044], lines 6-15 – The video search module uses the search criteria to query the metadata of video files stored in the video database and returns the search results to the user via the front end interface. For example, if a user provides a keyword search query to the video search module via the front end interface, the video search module identifies videos stored in the video database matching the keyword.);
in response to identifying a metadata match, identifying target content from the plurality of content mapped to matched metadata (see [0044], lines 6-15 – The video search module uses the search criteria to query the metadata of video files stored in the video database and returns the search results to the user via the front end interface. For example, if a user provides a keyword search query to the video search module via the front end interface, the video search module identifies videos stored in the video database matching the keyword.); and
providing the target content to the user (see [0044], lines 13-15 – returns search results to the user via the front end interface).
While Dasilva teaches the generation of first metadata, second metadata and third metadata, Dasilva fails to explicitly teach the use of artificial intelligence to generate the metadata. Bennett teaches the use of machine learning to generate metadata for content, including the further limitations of
train a plurality of artificial intelligence mechanisms, including:
train a first artificial intelligence mechanism [machine learning model] using known metadata for specific training content (see [0028]; [0035]; [0115] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions.);
train a second artificial intelligence mechanism [machine learning model] using known metadata for specific training content (see [0028]; [0035]; [0115] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions.);
train a third artificial intelligence mechanism [machine learning model] using historical user descriptions of known audio and known metadata for specific training content (see [0028]; [0035]; [0092] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions. Using data from existing audio descriptions the model can be trained);
for each corresponding content of the plurality of content (see [0034]):
generating first metadata for the corresponding content by employing an a first artificial intelligence mechanism (see [0035]; [0036]);
generating second metadata for the corresponding content by employing an the second artificial intelligence mechanism on the corresponding content to generate a metadata description of each scene (see [0035]; [0036] - in one illustrative example using a video as an example of an item of media content, the machine learning system 104 can use the one or more machine learning models to recognize one or more characters in a scene of the video, detect various objects in the scene, determine which actions the character(s) and/or object(s) are performing in the scene, determine an emotion of the character(s), determine a path or trajectory of the character(s) and/or object(s) in the scene, and determine a sentiment of the scene (e.g., positive, negative, etc.).);
generating third metadata for the corresponding content by employing the artificial intelligence mechanism on an audio portion of the corresponding content (see [0057] and [0062] - The process 300 (and the processes 400-600 of FIG. 4-FIG. 6) is described as generating an audio description output.);
mapping the first metadata, the second metadata, and the third metadata to the corresponding content (see [0005], lines 1-3 - Techniques and systems are described herein for annotating media content using metadata generated using one or more machine learning models.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use a plurality of artificial intelligence mechanisms, each trained separately to generate metadata as taught by Bennett to generate the metadata of Dasilva. One would have been motivated to do so in order to provide a system which can quickly and efficiently annotate media with metadata (Bennett: see [0004]; [0005]).
While the combination of Dasilva and Bennett (hereafter Dasilva/Bennett) teaches generating first metadata for the corresponding content by employing an a first artificial intelligence mechanism and that the metadata can be descriptions, Dasilva/Bennett fails to explicitly teach the further limitation of employing an a first artificial intelligence mechanism on user descriptions of the corresponding content. Shah teaches the creation of metadata for a video file, including the further limitation of
generating first metadata for the corresponding content by employing an artificial intelligence mechanism [machine learning] on user descriptions of the corresponding content (see [0020]; [0026]; [0036]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to apply the machine learning algorithm of Dasilva/Bennett to user descriptions of content as taught by Shah. One would have been motivated to do so since this is merely a specific function being performed on a specific type of data and Dasilva teaches description as a type of metadata (Dasilva: see [0043]) and Bennett teaches that the machine learning model can be trained to perform any function (Bennett: see [0035]).
While the combination of Dasilva/Bennett and Shah (hereafter Dasilva/Bennett/Shah) teaches searching for content with a user input, Dasilva/Bennett/Shah fails to explicitly teach the further limitations of employing a third artificial intelligence mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; searching the metadata database for metadata matching the plurality of searchable terms, including: selecting a threshold number of terms based on the plurality of searchable terms; and identifying a metadata match where the threshold number of terms in the plurality of searchable terms match metadata terms in the metadata database. Jindal teaches a multimedia content search using content tags, including the further limitations of
receiving input [search query] from a user (see [0060], lines 1-6 – The process 200 involves receiving a search query that includes a keyword set. The image processing system can receive, during a session with a client device, the search query from the client device via the data network.);
employing a mechanism [image processing system] on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input (see [0018]; [0060] – The image processing system can identify a keyword set from the search query. In some embodiments, the keyword set includes one or more keywords provided by the client device 106. In additional or alternative embodiments, the keyword set includes variants of one or more keywords provided by the client device 106 (e.g., synonyms of a user-provided keyword, a root word of a user-provided keyword, etc.));
dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms (see [0064] – The keyword search engine communicates with the multimedia database to determine whether content tags associated with a particular video file includes a sufficient number of content tags that match a keyword set in a search query received at block 202.);
searching the metadata database [multimedia database] for metadata [content tags/file metadata] matching the threshold number of terms from plurality of searchable terms (see [0064]), including:
in response to matching the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata in the metadata database (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202.); and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that metadata match is found between the plurality of searchable terms and metadata in the metadata database (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202. This is construed as if not then the video file is not retrieved.);
identifying a metadata match where the threshold number of terms in the plurality of searchable terms [keyword set] match metadata terms [content tags/file metadata] in the metadata database [multimedia database] (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the searching process of Jindal to identify the metadata match of Dasilva/Bennett/Shah. One would have been motivated to do so in order to efficiently search for and locate desirable multimedia content (Jindal: see [0002]).
While the combination of Dasilva/Bennett/Shah and Jindal (hereafter Dasilva/Bennett/Shah/Jindal) teaches employing a mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input, Dasilva/Bennett/Shah/Jindal fails to explicitly teach that the mechanism is a fourth artificial intelligence mechanism. Tambi teaches query processing, including the further limitations of
employing a fourth artificial intelligence mechanism [artificial neural network (ANN)] on the input [original query] to generate a plurality of searchable terms [expanded query], wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input (see [0041] – Query processing apparatus 200 includes a computer implemented artificial neural network (ANN) for augmenting an original query with an additional phrase to obtain an expanded query.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use an ANN to expand the queries of Dasilva/Bennett/Shah/Jindal as taught by Tambi. One would have been motivated to do so to provide a process that can automatically generate alternative or augmented queries based on an original query from a user (Tambi: see [0002]).
While Dasilva/Bennett/Shah/Jindal/Tambi teaches training of a plurality of artificial intelligence mechanisms, Dasilva/Bennett/Shah/Jindal/Tambi fails to explicitly teach training a first artificial intelligence mechanism using historical user descriptions and known metadata for specific training content and training a second artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content. Li teaches training an artificial intelligence model, including the further limitations of
training an artificial intelligence mechanism using historical user descriptions and known metadata for specific training content (see [0075] - In the method for training an autonomous driving model provided in the embodiment of the present disclosure, an initial video frame collected by a target vehicle and/or scenario description metadata of the initial video frame are used to determine a scenario context of the initial video frame; a vehicle movement instruction of the target vehicle is determined using the initial video frame and/or trajectory data of the target vehicle corresponding to the initial video frame; and an initial model is trained using the initial video frame and a corresponding control text to obtain the video prediction model, where the control text includes the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.); and
training an artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content (see [0075] - In the method for training an autonomous driving model provided in the embodiment of the present disclosure, an initial video frame collected by a target vehicle and/or scenario description metadata of the initial video frame are used to determine a scenario context of the initial video frame; a vehicle movement instruction of the target vehicle is determined using the initial video frame and/or trajectory data of the target vehicle corresponding to the initial video frame; and an initial model is trained using the initial video frame and a corresponding control text to obtain the video prediction model, where the control text includes the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the training content of Li to train the first and second mechanism of Dasilva/Bennett/Shah/Jindal/Tambi. One would have been motivated to do so in order to provide necessary content required to train a model (Bennett: see [0035]; Li: see [0077]).
Referring to claim 18, Dasilva discloses a non-transitory computer-readable medium storing computer instructions that, when executed by at least one processor (see [0025]), cause the at least one processor to perform actions, the actions comprising:
generating a metadata database for a plurality of content (see [0041]-[0043] – The front end interface receives video files uploaded from the content provider and delivers the video files to the upload server. The upload server processes the video files and stores the video files in the video database. The video database stores metadata of the video files.), including:
for each corresponding content of the plurality of content:
generating first metadata [any of a title; description; tag information; and administrative rights of a video file] for the corresponding content (see [0043] – The video database stores metadata of the video files. For example, the video database stores one or more of: a title; description; tag information; and administrative rights of a video file.);
generating second metadata [any of a title; description; tag information; and administrative rights of a video file that was not chosen as the first metadata] for the corresponding content (see [0043]);
generating third metadata [any of a title; description; tag information; and administrative rights of a video file that was not chosen as the first or second metadata] for the corresponding content (see [0043]);
mapping the first metadata, the second metadata and the third metadata to the corresponding content (see [0042]; [0043] – The video database is construed as providing the mapping between different categories of metadata and the video ID.); and
storing the first metadata, the second metadata and the third metadata in the metadata database and storing the mapping between the first metadata, the second metadata and the third metadata mapped to the corresponding content (see [0042]; [0043] – The video database is construed as storing the mapping between different categories of metadata and the video ID.);
receiving input from a user [search queries received by the front end interface from users] (see [0044], lines 1-6);
searching the metadata database for metadata matching the input (see [0044], lines 6-15 – The video search module uses the search criteria to query the metadata of video files stored in the video database and returns the search results to the user via the front end interface. For example, if a user provides a keyword search query to the video search module via the front end interface, the video search module identifies videos stored in the video database matching the keyword.);
in response to identifying a metadata match, identifying target content from the plurality of content mapped to matched metadata (see [0044], lines 6-15 – The video search module uses the search criteria to query the metadata of video files stored in the video database and returns the search results to the user via the front end interface. For example, if a user provides a keyword search query to the video search module via the front end interface, the video search module identifies videos stored in the video database matching the keyword.); and
providing the target content to the user (see [0044], lines 13-15 – returns search results to the user via the front end interface).
While Dasilva teaches the generation of first metadata, second metadata and third metadata, Dasilva fails to explicitly teach the use of artificial intelligence to generate the metadata. Bennett teaches the use of machine learning to generate metadata for content, including the further limitations of
training a first artificial intelligence mechanism [machine learning model] using known metadata for specific training content (see [0028]; [0035]; [0115] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions.);
training a second artificial intelligence mechanism [machine learning model] using known metadata for specific training content (see [0028]; [0035]; [0115] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions.);
training a third artificial intelligence mechanism [machine learning model] using historical user descriptions of known audio and known metadata for specific training content (see [0028]; [0035]; [0092] – The systems and techniques can use one or more machine learning models (e.g., by implementing a combination of multiple machine learning models) to generate the description of the media content. Each machine learning model of the machine learning system can be trained to perform one or more functions. Using data from existing audio descriptions the model can be trained);
for each corresponding content of the plurality of content (see [0034]):
generating first metadata for the corresponding content by employing an a first artificial intelligence mechanism (see [0035]; [0036]);
generating second metadata for the corresponding content by employing an the second artificial intelligence mechanism on the corresponding content to generate a metadata description of each scene (see [0035]; [0036] - in one illustrative example using a video as an example of an item of media content, the machine learning system 104 can use the one or more machine learning models to recognize one or more characters in a scene of the video, detect various objects in the scene, determine which actions the character(s) and/or object(s) are performing in the scene, determine an emotion of the character(s), determine a path or trajectory of the character(s) and/or object(s) in the scene, and determine a sentiment of the scene (e.g., positive, negative, etc.).);
generating third metadata for the corresponding content by employing the artificial intelligence mechanism on an audio portion of the corresponding content (see [0057] and [0062] - The process 300 (and the processes 400-600 of FIG. 4-FIG. 6) is described as generating an audio description output.);
mapping the first metadata, the second metadata, and the third metadata to the corresponding content (see [0005], lines 1-3 - Techniques and systems are described herein for annotating media content using metadata generated using one or more machine learning models.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use a plurality of artificial intelligence mechanisms, each trained separately to generate metadata as taught by Bennett to generate the metadata of Dasilva. One would have been motivated to do so in order to provide a system which can quickly and efficiently annotate media with metadata (Bennett: see [0004]; [0005]).
While the combination of Dasilva and Bennett (hereafter Dasilva/Bennett) teaches generating first metadata for the corresponding content by employing an a first artificial intelligence mechanism and that the metadata can be descriptions, Dasilva/Bennett fails to explicitly teach the further limitation of employing an a first artificial intelligence mechanism on user descriptions of the corresponding content. Shah teaches the creation of metadata for a video file, including the further limitation of
generating first metadata for the corresponding content by employing an artificial intelligence mechanism [machine learning] on user descriptions of the corresponding content (see [0020]; [0026]; [0036]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to apply the machine learning algorithm of Dasilva/Bennett to user descriptions of content as taught by Shah. One would have been motivated to do so since this is merely a specific function being performed on a specific type of data and Dasilva teaches description as a type of metadata (Dasilva: see [0043]) and Bennett teaches that the machine learning model can be trained to perform any function (Bennett: see [0035]).
While the combination of Dasilva/Bennett and Shah (hereafter Dasilva/Bennett/Shah) teaches searching for content with a user input, Dasilva/Bennett/Shah fails to explicitly teach the further limitations of employing a third artificial intelligence mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input; searching the metadata database for metadata matching the plurality of searchable terms, including: selecting a threshold number of terms based on the plurality of searchable terms; and identifying a metadata match where the threshold number of terms in the plurality of searchable terms match metadata terms in the metadata database. Jindal teaches a multimedia content search using content tags, including the further limitations of
receiving input [search query] from a user (see [0060], lines 1-6 – The process 200 involves receiving a search query that includes a keyword set. The image processing system can receive, during a session with a client device, the search query from the client device via the data network.);
employing a mechanism [image processing system] on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input (see [0018]; [0060] – The image processing system can identify a keyword set from the search query. In some embodiments, the keyword set includes one or more keywords provided by the client device 106. In additional or alternative embodiments, the keyword set includes variants of one or more keywords provided by the client device 106 (e.g., synonyms of a user-provided keyword, a root word of a user-provided keyword, etc.));
dynamically setting a threshold number of terms based on a number of searchable terms in the plurality of searchable terms (see [0064] – The keyword search engine communicates with the multimedia database to determine whether content tags associated with a particular video file includes a sufficient number of content tags that match a keyword set in a search query received at block 202.);
searching the metadata database [multimedia database] for metadata [content tags/file metadata] matching the threshold number of terms from plurality of searchable terms (see [0064]), including:
in response to matching the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that a metadata match is found between the plurality of searchable terms and the matched metadata in the metadata database (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202.); and
in response to failing to match the threshold number of terms from plurality of searchable terms to metadata in the metadata database, determining that metadata match is found between the plurality of searchable terms and metadata in the metadata database (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202. This is construed as if not then the video file is not retrieved.);
identifying a metadata match where the threshold number of terms in the plurality of searchable terms [keyword set] match metadata terms [content tags/file metadata] in the metadata database [multimedia database] (see [0064] – The keyword search engine 110 can use the video file metadata to determine whether a number of matches between the keyword set and the video file’s content exceeds a threshold. If so, the keyword search engine 110 selects the video file as a search result for the search query received at block 202.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the searching process of Jindal to identify the metadata match of Dasilva/Bennett/Shah. One would have been motivated to do so in order to efficiently search for and locate desirable multimedia content (Jindal: see [0002]).
While the combination of Dasilva/Bennett/Shah and Jindal (hereafter Dasilva/Bennett/Shah/Jindal) teaches employing a mechanism on the input to generate a plurality of searchable terms, wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input, Dasilva/Bennett/Shah/Jindal fails to explicitly teach that the mechanism is a fourth artificial intelligence mechanism. Tambi teaches query processing, including the further limitations of
employing a fourth artificial intelligence mechanism [artificial neural network (ANN)] on the input [original query] to generate a plurality of searchable terms [expanded query], wherein the plurality of searchable terms includes at least one term derived from the input without being included in the input (see [0041] – Query processing apparatus 200 includes a computer implemented artificial neural network (ANN) for augmenting an original query with an additional phrase to obtain an expanded query.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use an ANN to expand the queries of Dasilva/Bennett/Shah/Jindal as taught by Tambi. One would have been motivated to do so to provide a process that can automatically generate alternative or augmented queries based on an original query from a user (Tambi: see [0002]).
While Dasilva/Bennett/Shah/Jindal/Tambi teaches training of a plurality of artificial intelligence mechanisms, Dasilva/Bennett/Shah/Jindal/Tambi fails to explicitly teach training a first artificial intelligence mechanism using historical user descriptions and known metadata for specific training content and training a second artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content. Li teaches training an artificial intelligence model, including the further limitations of
training an artificial intelligence mechanism using historical user descriptions and known metadata for specific training content (see [0075] - In the method for training an autonomous driving model provided in the embodiment of the present disclosure, an initial video frame collected by a target vehicle and/or scenario description metadata of the initial video frame are used to determine a scenario context of the initial video frame; a vehicle movement instruction of the target vehicle is determined using the initial video frame and/or trajectory data of the target vehicle corresponding to the initial video frame; and an initial model is trained using the initial video frame and a corresponding control text to obtain the video prediction model, where the control text includes the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.); and
training an artificial intelligence mechanism using historical user descriptions of known scenes and known metadata for specific training content (see [0075] - In the method for training an autonomous driving model provided in the embodiment of the present disclosure, an initial video frame collected by a target vehicle and/or scenario description metadata of the initial video frame are used to determine a scenario context of the initial video frame; a vehicle movement instruction of the target vehicle is determined using the initial video frame and/or trajectory data of the target vehicle corresponding to the initial video frame; and an initial model is trained using the initial video frame and a corresponding control text to obtain the video prediction model, where the control text includes the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the training content of Li to train the first and second mechanism of Dasilva/Bennett/Shah/Jindal/Tambi. One would have been motivated to do so in order to provide necessary content required to train a model (Bennett: see [0035]; Li: see [0077]).
Referring to claim 19, Shah/Ishikawa/Jindal/Tambi and Zhang teaches the non-transitory computer-readable medium of claim 18, wherein the details of the corresponding content includes one or more of: title, character name [speaker identities], cast name, length, genre [topics being discussed], or release date (Shah: see [0029]).
Response to Arguments
Applicant's arguments filed with regards to the 101 rejection have been fully considered but they are not persuasive.
The Examiner respectfully disagrees that the claims do not contain abstract ideas. With regards the Memorandum by the Deputy Commissioner, the Memo has not changed the manner in which the claims are examined. The memo is directed to the requirement that at least one limitation of the claim must recite an abstract idea. Limitations that are not directed to a “Mental Processes” should not be considered under metal processes. These additional limitations that are not directed to abstract concepts are then considered in steps 2A, prong 2 and step 2B. As depicted above in the 101 rejection, the claims do recite processes that can be performed in the human mind. The Applicant argues that the human mind is not multiple artificial intelligence mechanisms that are trained to generate different kinds of metadata. The Examiner has not stated that the multiple artificial intelligence mechanisms that are trained to generate different kinds of metadata can be performed by the human mind. The artificial intelligence mechanisms are recited at a high-level of generality (i.e., as a generic AI model performing the generic functions of generating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The separate training of the artificial intelligence mechanisms are also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The application does not claim improvement of the machine learning technique itself but instead claims the application of the machine learning technique to specific contexts. The memo states When evaluating these two considerations, examiners may consider the following:
Whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome.
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology.
The AI is merely being used to as a tool to perform a mental process.
With regards to Applicant’s arguments concerning the Desjardins Memo, the Examiner respectfully disagrees that the condition of the current application are similar to Desjardins. MPEP 2106(d)III states the following:
In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).
The Desjardins case includes a specific statement of the improvement to the machine learning model and the manner in which the improvement is achieved in the Specification and the claims explicitly recite that improvement. The current application merely recites the training and use of artificial intelligence. Even with reciting the content type being used to train the each artificial intelligence mechanism, the training of the mechanisms is also recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component since this is an inherent requirement of artificial intelligence mechanisms (see MPEP 2106.05(f)). The artificial intelligence itself has not been improved as it was in Desjardins.
With regards to Applicant’s arguments on page 9 of the Remarks concerning MPEP 2106.04(d) and 2106.05(a), the Examiner has considered the claims as a whole and the Examiner has not dismissed additional elements as mere generic computer components without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or a computer system. As stated above, the claims merely recite the usage of the technology of artificial intelligence and not an improvement to the technology.
Therefore, the claims as a whole are directed to a judicial exception and do not provide an improvement.
Therefore, the 101 rejections have been maintained.
With regards to the prior art rejections, new references have been utilized to teach the newly added limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPub 2018/0213299 to Park et al – tagging a video file with metadata
US PGPub 2023/0101319 to Ramachandivan et al – querying based on metadata
US PGPub 2021/0042393 to Ishikawa et al - wherein searching the metadata database for metadata matching the input comprises: identifying matched metadata in response to the matched metadata meeting a threshold similarity value relative to the input (Ishikawa: see [0042] – above a threshold similarity value).
US PGPub 2023/0005075 to Li et al – teaches dynamically determining a threshold
US PGPub 2014/0074851 to Zhang et al - teaches searching, including the further limitation identify matched metadata where a threshold number of terms in the plurality of searchable terms match metadata terms, wherein the threshold number of terms dynamically changes based on the plurality of searchable terms (see [0019] – In some embodiments, the index data table is filtered based on the filter conditions to acquire one or more corresponding pieces of index information when keywords in the index data table and the search terms are the same and the threshold scores of the keywords are greater than or equal to the dynamic threshold scores of the search terms. A dynamic threshold score refers to different queries using different threshold scores for filtering. In other words, a threshold score is dynamically assigned to a query instead of the same threshold score being assigned to all queries for filtering.).
US PGPub 2015/0347590 to Kamotsky teaches using matching constraints designed for each search that identifies how many unmatched words are allowed.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY LOVEL WILSON whose telephone number is (571)272-2750. The examiner can normally be reached 8-4:30.
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/KIMBERLY L WILSON/Primary Examiner, Art Unit 2167
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