DETAILED ACTION
Status of Claims
Claim(s) 1-20 are pending and are examined herein.
Claim(s) 1-20 are rejected under 35 U.S.C. §§§ 101, 102, and 103.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 1-10 recite a method (representing a process); and
Claims 11-20 recite a non-transitory storage medium (representing an article of manufacture);
Therefore, each set of the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Claim(s)
1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, and hence is not patent-eligible subject matter.
Regarding Claim 1,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
determining, from a database of entities including the target entity and the one or more context entities, a probability set for the target entity including one or more conditional probabilities for each of the one or more context entities, wherein the database includes statistics of interactions between pairs of entities in the relevance query; (An abstract idea of a mental process and/or mathematical concepts. Examiner’s note: the “determining” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind with or without physical aid (e.g., pen and paper. The process of determining a probability set is determined by performing statistical analysis on historical data (i.e., entity interaction data) to generate a list of conditional probabilities between a target entity (e.g., person or application) and context entities (e.g., time, location etc.). This step covers concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).)
determine a relevance of the target entity for the query context; (An abstract idea of a mental process. Examiner’s note: the “determining” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). This step involves determining/predicting a relevance score which is the likelihood score that target entity is relevant based on the probability set. This is an evaluation and judgment process that could be performed mentally without a computer. See MPEP § 2106.04(a)(2)(III).)
taking an action based on the determined relevance. (An abstract idea of a mental process. Examiner’s note: the “taking” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. This involves an evaluation and decision-making process that can be performed in the human mind. For example, a person determined the relevance score can rank/select entity and/or suggest/recommend an entity. This is a mental process. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 2A Prong 2: Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application.
Additional Elements Analysis:
The claim recite the additional element such as:
“in response to a relevance query including a target entity and query context comprising one or more context entities: ... from a database of entities including the target entity and the one or more context entities...” (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). The recitation of “in response to a relevance query” merely defines a received request to perform an abstract idea using information collected from interaction database. The database itself is used for storing interaction statistics, which merely represents a generic computer component. This limitation merely represents a generic computer function (i.e., data gathering in conjunction with the abstract idea).)
“applying a machine learning model” to the probability set to determine a relevance of the target entity for the query context; (This amounts to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). In other words, the claim invokes computer and/or other machinery in its ordinary capacity merely as a tool to perform an abstract idea.)
Step 2B: Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept).
Additional Elements Analysis:
As outlined above, the claimed additional elements merely represents generic computer component (i.e., conventional models) configured to perform the abstract ideas. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. Additionally, the step of receiving a query and collecting interaction statistics represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 1 does not recite patent-eligible subject matter.
Regarding Claim 2,
Step 2A Prong 1: Claim 2, which incorporates the rejection of claim 1, recites further limitation such as:
the probability set is determined based on a current database, wherein the entities in training database and entities in the current database are different. (That is part of the abstract idea recited in claim 1. This limitation describes that the probability set is determined using the current entities and current interaction data, which is part of the abstract idea of determining the probability set based on current/available interaction data in the repository. This is an abstract idea of a mental process and/or mathematical concept.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the machine learning model is trained with a training database, (This amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). This limitation represents high-level training a model using the pre-collected training data, which amounts to no more than invoking computer or other machinery in its ordinary capacity as a tool to perform an existing process. The additional elements do not integrate the abstract idea into a practical application; they merely perform routine data gathering and generic model training. The claim does not introduce any technical improvement, and therefore, does not integrate the abstract idea into a practical application.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. The high-level recitation of using training data to train the machine learning model amounts to generic and conventional computer component performing an existing process. This does not amount to an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 2 is ineligible.
Regarding Claim 3,
Step 2A Prong 1: Claim 3, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the machine learning model is trained with a training database, and at least one of the target entity and the context entities are not in the training database. (This amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The claim mainly suggesting that the training database (historical data) being used to train the model. This represents high-level training a model using the previously collected data, which amounts to no more than invoking computer or other machinery in its ordinary capacity as a tool to perform an existing process.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See MPEP § 2106.05(d).
Therefore, claim 3 is ineligible.
Regarding Claim 4,
Step 2A Prong 1: Claim 4, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the machine learning model was trained with a training database, and the method further includes: updating the training database with new statistics of interactions; and wherein the probability set is determined from the updated training database. (This amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The claim merely recite high-level machine learning training process to apply/implement the abstract idea on a computer. Thus invoking computer or other machinery in its ordinary capacity as a tool to apply the abstract idea does not add meaningful limit that would integrate the judicial exception. See MPEP § 2106.04(d).)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See MPEP § 2106.05(d).
Therefore, claim 4 is ineligible.
Regarding Claim 5,
Step 2A Prong 1: Claim 5, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the one or more conditional probabilities for a first context entity includes at least a first conditional probability of the target entity given the first context entity and a second conditional probability of the first context entity given the target entity. (That is part of the abstract idea recited in claim 1. This limitation merely specifies the conditional probabilities are determined in two ways to determine the relationship between the target entity and the context entity. This is part of the mental and/or mathematical concept that can be practically performed in the human mind with the aid of pen and paper. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 5 is ineligible.
Regarding Claim 6,
Step 2A Prong 1: Claim 6, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the database is a personal knowledge graph of a user, and the target entity and the context entities are aspects of a device associated with the user. (This amounts to no more generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). The claim merely describes the technological environment where the database such as a user knowledge graph are derived from device interaction. However, simply linking the judicial exception to a field of use or a particular technological environment does not imposes a meaningful limit on the judicial exception and hence does not integrate the judicial exception into a practical application.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element amounts to generally linking the abstract idea into a technological environment. This cannot provide an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 6 is ineligible.
Regarding Claim 7,
Step 2A Prong 1: Claim 7, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the aspects of the device include one or more of: a time period of a day the device is used, a day of a week the device is used, a location the device is used, an application used on the device, a motion of the device, or a Wi-Fi state of the device. (This amounts to no more generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). The claim merely defines the environment (i.e., context data of the user device) where the user interaction happens. This does not imposes any meaningful limit on the judicial exception and hence does not integrate the judicial exception into a practical application.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element amounts to generally linking the judicial exception to a particular technological environment. This cannot provide an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 7 is ineligible.
Regarding Claim 8,
Step 2A Prong 1: Claim 8, which incorporates the rejection of claim 1, recites further limitation such as:
determining a relevance ranking amongst entities in the target class of entities in the database based on the determined relevance of the target entity; and providing a response to the relevance query based on the determined relevance ranking. (This limitation forms part of the abstract idea of claim 1. The claim merely involves comparing the relevance of multiple entities, ranking them based on the relevancy score, and returning the results based on the ranking. These steps cover concepts that fall within the mental processes including evaluation and decision-making process that can be practically performed in the human mind and/or with physical aid (e.g., pen and paper). Accordingly, the limitation represents an evaluation and estimation step that would fall within the abstract idea processes.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 8 is ineligible.
Regarding Claim 9,
Step 2A Prong 1: Claim 9, which incorporates the rejection of claim 8, recites further limitation such as:
taking an action based on a most relevant entity in the relevance ranking; (That is part of the abstract idea recited in claim 8. This limitation merely defines the use of the highest ranking (most relevant) entity to perform a decision or operation. This falls within the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
presenting, in a user interface, a most relevant subset of entities in the relevance ranking. (The limitation amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed step represents displaying certain results of the evaluation and comparison analysis, which constitute a generic data outputting step in conjunction with the abstract idea. The recited “user interface” amounts to using generic computer component to display the results of the abstract analysis (i.e., ranking). Accordingly, the additional element does not integrate the abstract idea into a practical application.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information using GUI represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. This does not amount to an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 9 is ineligible.
Regarding Claim 10,
Step 2A Prong 1: Claim 10, which incorporates the rejection of claim 8, recites further limitation such as:
the target class of entities includes software application entities previously installed on a device; the relevance query requests a ranking of predicted relevance amongst the target class; (That is part of the abstract idea recited in claim 8. This limitation merely suggest to rank the software applications previously installed on a device according how relevant they are to the context. In other words, the claim involves determining a relevance score for each app and ranking them from most relevant to least relevant. These operations encompass the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (This limitation amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed step represents displaying certain results of the evaluation and comparison analysis, which constitute a generic data outputting step in conjunction with the abstract idea. The recited “a user interface of the device” amounts to using generic computer component to display the results of the abstract analysis (i.e., ranking). Accordingly, the additional element does not integrate the abstract idea into a practical application.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information using GUI represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. This does not amount to an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 10 is ineligible.
Regarding Claim 11,
Step 2A Prong 1: Claim 11, which incorporates the rejection of claim 8, recites further limitation such as:
the target class of entities includes geographic location entities previously referenced by a user of a device; the relevance query requests a ranking of predicted relevance amongst the target class; (That is part of the abstract idea recited in claim 8. This limitation merely suggest to rank the locations based on historical interaction data and current context to identify the most relevant location. In other words, the claim involves determining a relevance score for each location based on interaction statistics and ranking them from most relevant to least relevant. These operations encompass the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (This limitation amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed step represents displaying certain results of the evaluation and comparison analysis, which constitute a generic data outputting step in conjunction with the abstract idea. The recited “a user interface of the device” amounts to using generic computer component to display the results of the abstract analysis (i.e., ranking). Accordingly, the additional element does not integrate the abstract idea into a practical application.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information using GUI represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. This does not amount to an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 11 is ineligible.
Regarding Claim 12,
Step 2A Prong 1: Claim 12, which incorporates the rejection of claim 8, recites further limitation such as:
the target class of entities includes person entities previously referenced by a user of a device; the relevance query requests a ranking of predicted relevance amongst the target class; (That is part of the abstract idea recited in claim 8. This limitation merely suggest to rank a group of people based on historical interaction data and current context to identify the most relevant person. In other words, the claim involves ranking previously referenced people using historical information and current context to determine the most relevant individual. These operations encompass the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (This limitation amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed step represents displaying certain results of the evaluation and comparison analysis, which constitute a generic data outputting step in conjunction with the abstract idea. The recited “a user interface of the device” amounts to using generic computer component to display the results of the abstract analysis (i.e., ranking). Accordingly, the additional element does not integrate the abstract idea into a practical application.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information using GUI represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. This does not amount to an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 12 is ineligible.
Regarding Claim 13,
Step 2A Prong 1: Claim 13, which incorporates the rejection of claim 8, recites further limitation such as:
entities in the database include attributes; the relevance query includes a query attribute; the target class of entities includes a subset of person entities having an attribute that matches the query attribute; the relevance query requests a ranking of predicted relevance amongst the target class; (That is part of the abstract idea recited in claim 8. The claim merely defines the attributes as a filtering or selection to identify a subset of entities. This involves using attributes to select a subset of people, and then ranking those people by the determined relevance score. These operations encompass the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (This limitation amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed step represents displaying certain results of the evaluation and comparison analysis, which constitute a generic data outputting step in conjunction with the abstract idea. The recited “a user interface of the device” amounts to using generic computer component to display the results of the abstract analysis (i.e., ranking). Accordingly, the additional element does not integrate the abstract idea into a practical application.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information using GUI represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. This does not amount to an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 13 is ineligible.
Regarding Claim 14,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 14. The only difference is that claim 1 is drawn to a method, and claim 14 is drawn to a system. The recitation of “A system, comprising: a processor; and a memory storing instructions, that when executed by the processor...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instructions to apply the abstract idea using computer components. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f).
Therefore, claim 14 is ineligible.
Regarding Claim 15,
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 2, as described above, is equally applicable to claim 15.
Therefore, claim 15 is ineligible.
Regarding Claim 16,
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 16.
Therefore, claim 16 is ineligible.
Regarding Claim 17,
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Claim 18,
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Claim 19,
The claim recites similar limitations as corresponding claim 6. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 6, as described above, is equally applicable to claim 19.
Therefore, claim 19 is ineligible.
Regarding Claim 17,
The claim recites similar limitations as corresponding claim 7. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 7, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Claim 18,
The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 8, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Claim 19,
The claim recites similar limitations as corresponding claim 9. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 9, as described above, is equally applicable to claim 19.
Therefore, claim 19 is ineligible.
Regarding Claim 20,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 20. The only difference is that claim 1 is drawn to a method, and claim 20 is drawn to a non-transitory computer readable memory. The recitation of “A non-transitory computer readable memory storing instructions that, when executed by a processor, cause the processor...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instructions to apply the abstract idea using computer components. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f).
Therefore, claim 20 is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 8-9, 11-12, 14-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pueyo et al., (Pub. No.: US 20120030152 A1).
Regarding Claim 1,
Pueyo discloses the following:
A method, comprising: in response to a relevance query including a target entity and query context comprising one or more context entities: (Pueyo, [0014]-[0015] “Some example methods, apparatuses, and articles of manufacture are disclosed herein that may be implemented, partially, dominantly, or substantially, to rank entities that occur in a faceted relationship or facets using user-click feedback... faceted relationships may be represented, for example, via one or more entity-facet pairs associated with or extracted from the vocabulary of one or more information corpora, such as, for example, one or more extraction corpora. As used herein, “entity,” “query entity,” or the plural form of such terms may be used interchangeably and may refer to one or more lexical objects descriptive or representative of a query that may be defined... “Facet” or “entity facet,” as the terms used herein, may refer to one or more lexical objects representative of one or more concepts, aspects, properties, attributes, or characteristics of an entity that may be defined, for example, via a directed relationship between an entity e and an entity facet f, such as, for example, in a faceted relationship or relation (e, f).” [0021] “... a search engine or other like information management system to determine how to respond to a search query or perform other information processing functions. More specifically, as illustrated in example implementations described herein, one or more entities or faceted relationships (e.g., entity-facet pairs, etc.) may be extracted or obtained, for example, from one or more extraction corpora so as to create a dictionary or pool of facets for an entity of interest.” [0024]-[0027] “Results of such ranking may be implemented, in whole or in part, for use with a search engine or other like information management systems, for example, responsive to search queries... In the context of a search, a query may be submitted via an interface, such as a graphical user interface (GUI), for example, by entering certain words or phrases to be queried, and a search engine may return a search results...”) [Examiner’s Note: a search query is submitted to rank or score entities based on relevance. The query entity reads on the target entity and the related entity or entity facets reads on the context entities.]
determining, from a database of entities including the target entity and the one or more context entities, a probability set for the target entity including one or more conditional probabilities for each of the one or more context entities, wherein the database includes statistics of interactions between pairs of entities in the relevance query; (Pueyo, [0021]-[0022] “In an implementation, ranking corpora may comprise, for example, one or more query logs reflecting user behavior information collected or archived over a certain period of time, as one possible example... In an implementation, co-occurrence statistics with respect to faceted relationships of interest extracted or derived from ranking corpora may be analyzed, and a number of metrics or measures used for ranking entity facets may be computed. Such metrics or measures may comprise, for example, one or more statistical features based, at least in part, on one or more variants of conditional probabilities with respect to entities or pairs of entities occurring or co-occurring within the vocabulary of one or more information corpora, such as, for example, one or more ranking corpora... a conditional user probability may be computed or estimated, at least in part, as a statistical probability of a particular entity co-occurring together ... with a related facet with respect to a particular user.” [0033] “For example, in one particular implementation, first corpus 118 may be used to extract entities and faceted relations of interest (e.g., extraction corpora), and query logs 120 may be used to rank such relations (e.g., ranking corpora) utilizing one or more statistical features extracted or otherwise derived from query logs 120.” [0039]-[0040] “one or more statistical features capturing relevance between facets and related entities of interest may be extracted or otherwise derived from one or more information corpora, such as, for example, one or more ranking corpora... certain statistical information reflecting relevancy of extracted faceted relationships for a given query may be collected, for example, from one or more ranking corpora and may be analyzed in some manner.” [0063]-[0065] “statistical feature may comprise, for example, a non-symmetric feature represented by a single-user-prone variant of a conditional probability, such as a conditional user probability, that may be computed as: ... where |e| denotes a number of users that used entity e in an event, and |e ∩ f| denotes a number of users that used both an entity facet pair (e, f) in an event... It should be appreciated that various statistical features or metrics capturing relevance between facets and an entity of interest may also be computed or considered.) [Examiner’s Note: Pueyo computes a set of conditional probabilities between the query entity and the related facet, obtained from a corpus/database of co-occurrence statistics. The statistical features computed for entity-facet pairs, derived from co-occurrence statistics in the ranking corpora/query logs reads on the determined “probability set.”]
applying a machine learning model to the probability set to determine a relevance of the target entity for the query context; (Pueyo, [0023]-[0024] “one or more learner functions (e.g., employing one or more machine learning techniques) may be trained and used to establish one or more machine-learned functions. More specifically, as illustrated in example implementations described herein, such one or more machine-learned functions may comprise, for example, a ranking function established based, at least in part, on one or more inputs or applications of user-click feedback in conjunction with one or more statistical features extracted or derived from one or more ranking corpora... a ranking function may be trained, for example, to predict or estimate an actual CTR on a facet based, at least in part, on user-click feedback information in conjunction with one or more statistical features by employing a stochastic gradient boosted decision trees (GBDT) learner, as will be described below.” [0039] “one or more statistical features may be used, at least in part, to train or establish a machine-learned ranking function determining a ranking order of facets by predicting or estimating an actual CTR on a facet for a given entity representative of a query, as will be seen.” [0067]-[0075] “Here, a ranking function may comprise, for example, a machine-learned function trained or established to predict an actual CTR on a facet given an entity of interest, ..., training or establishing a machine-learned function that may be utilized, in whole or in part, to determine a ranking order of facets for a given query by predicting or estimating an actual CTR on a facet. As described below, such a machine-learned function may comprise a ranking function trained or established based, at least in part, on one or more inputs or applications of user-click feedback in conjunction with one or more statistical features extracted or derived from one or more ranking corpora.”) [Examiner’s Note: Pueyo explicitly applies a machine learning function (model) to predict relevance score (CTR) based on the statistical features derived from conditional probabilities.] and
taking an action based on the determined relevance. (Pueyo, [0027]-[0028] “Following the above discussion, in processing a query, a search engine may place documents that are deemed to be more likely to be relevant or useful in a higher position or slot on a returned search results page... in one particular implementation, ranked facets may be integrated or incorporated, for example, into a search results page so as to possibly enhance user experience in the context of a faceted image search by providing an ergonomic or interactive user environment. For example, a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships...” [0085] “With regard to operation 406, one or more digital signals representing a listing of ranked facets may be transmitted to a user or client device via a communication interface. In one implementation, ranked facets may be integrated or incorporated, for example, into a search results page so as to possibly enhance user experience in the context of a faceted image search by providing an ergonomic or interactive user environment, as discussed above.” Further see [0074].).
Regarding Claim 2, Pueyo teaches the elements of claim 1 as outlined above, and further teaches:
wherein the machine learning model is trained with a training database, and the probability set is determined based on a current database, wherein the entities in training database and entities in the current database are different. (Pueyo, [0021] “In an implementation, ranking corpora may comprise, for example, one or more query logs reflecting user behavior information collected or archived over a certain period of time, as one possible example.” [0035] “In certain implementations, it may be advantageous to utilize one or more real-time indexing techniques or processes, for example, to keep search index 126 sufficiently or continually updated with a real-time content (e.g., facets, etc.). As such, IIS 102 may be operatively enabled to subscribe to or otherwise be integrated with one or more information corpora via a “live” or direct feed, for example. As a way of illustration, IIS 102 may be enabled to subscribe to a direct photostream feed, for example, from Flickr® photo annotation corpus, thus, providing more current or fresh facets associated with Flickr® database so as to facilitate or support ranking mechanisms based, at least in part, on occurrence or co-occurrence statistics with respect to faceted relationships within the database.” [0052] “Also, if an interest score (e.g., a ranking score of co-occurring pairs in a particular event, etc.) needs to be computed, a week Id may be added to each line, for example, and a common model or operation (e.g., utilizing a conditional probability of an entity-facet pair, etc.) may be recomputed over a desired number of weeks (e.g., last ten, twelve, fifteen weeks, etc.).” Further see [0049] and [0067].) [Examiner’s Note: Pueyo describes the training using previously determined conditional probabilities and collected statistics. Pueyo also describes the live/real-time feeds and describes the computation of statistical features from the current/live ranking corpus that continually updated. Thus, the entities currently in the database are not necessarily the same entities that were present in the database used to train the machine learning model.]
Regarding Claim 3, Pueyo teaches the elements of claim 1 as outlined above, and further teaches:
wherein the machine learning model is trained with a training database, and at least one of the target entity and the context entities are not in the training database. (Pueyo, [0017] “An information corpus may comprise, for example, a relatively open or fluid vocabulary, meaning that the content of an information corpus may change over time... one or more information corpora may, although not necessarily, be subdivided into one or more extraction corpora and one or more ranking corpora... certain example implementations may utilize more than one information corpus, and such corpora may be separate or overlapping, for example, or one corpus may be a subset of another.” [0033] “For example, in one particular implementation, first corpus 118 may be used to extract entities and faceted relations of interest (e.g., extraction corpora), and query logs 120 may be used to rank such relations (e.g., ranking corpora) utilizing one or more statistical features extracted or otherwise derived from query logs 120.” [0039] “Statistical features may, for example, be used to facilitate or support, in whole or in part, one or more techniques, operations, or processes associated with ranking entity facets using user-click feedback, as was also indicated. For example, one or more statistical features may be used, at least in part, to train or establish a machine-learned ranking function determining a ranking order of facets by predicting or estimating an actual CTR on a facet for a given entity representative of a query,” [0074] “Trees in stochastic GBDT may be trained on a randomly selected subset of a training data or information and may be less prone to over-fitting.”)
Regarding Claim 4, Pueyo teaches the elements of claim 1 as outlined above, and further teaches:
updating the training database with new statistics of interactions; and wherein the probability set is determined from the updated training database. (Pueyo, [0017] “An information corpus may comprise, for example, a relatively open or fluid vocabulary, meaning that the content of an information corpus may change over time. Optionally or alternatively, a vocabulary of an information corpus may be relatively static, for example, meaning that the vocabulary may remain relatively unchanged over time.” [0021] “To facilitate or support facet ranking, one or more statistical features capturing relevance between facets and a given entity may be extracted or otherwise derived, for example, from one or more ranking corpora, as previously mentioned. In an implementation, ranking corpora may comprise, for example, one or more query logs reflecting user behavior information collected or archived over a certain period of time, as one possible example. As used herein, “query log” may refer to one or more information databases or repositories generated during one or more information searches (e.g., by search engine users, etc.), which may comprise, for example, a sequence of search actions, queries or search terms, documents viewed, documents clicked on, a resource identifier of a clicked result and a result position, user identifier (ID), session ID, event ID, time stamp, etc. In addition, various user-generated content or knowledge databases, such as, for example, one or more user-annotated image or photo sharing databases may also be accessed to extract or otherwise derive one or more statistical features capturing relevancy information with respect to faceted relationships of interest, as will also be seen.” [0035] “In certain implementations, it may be advantageous to utilize one or more real-time indexing techniques or processes, for example, to keep search index 126 sufficiently or continually updated with a real-time content (e.g., facets, etc.).” [0052] “Also, if an interest score (e.g., a ranking score of co-occurring pairs in a particular event, etc.) needs to be computed, a week Id may be added to each line, for example, and a common model or operation (e.g., utilizing a conditional probability of an entity-facet pair, etc.) may be recomputed over a desired number of weeks (e.g., last ten, twelve, fifteen weeks, etc.).” Further see [0049] and [0077].) [Examiner’s Note: Pueyo explicitly describes that the ranking corpus (i.e., training database) is continually updated with new statistical information from query logs, and that the conditional probability features are recomputed overtime from the updated corpus..]
Regarding Claim 5, Pueyo teaches the elements of claim 1 as outlined above, and further teaches:
wherein the one or more conditional probabilities for a first context entity includes at least a first conditional probability of the target entity given the first context entity and a second conditional probability of the first context entity given the target entity. (Pueyo, [0063]-[0065] “a statistical feature may comprise, for example, a non-symmetric feature represented by a single-user-prone variant of a conditional probability, such as a conditional user probability, that may be computed as:
P
f
e
=
|
e
⋂
f
|
|
e
|
where |e| denotes a number of users that used entity e in an event, and |e ∩ f| denotes a number of users that used both an entity facet pair (e, f) in an event. Again, this is merely an example of a particular single-user-prone variant of a non-symmetric feature that may be utilized to facilitate or support ranking entity facets using user-click feedback, and claimed subject matter is not so limited.” TABLE 6 provides listing of conditional probabilities including: Conditional probability
P
(
e
|
f
)
, Conditional user probability
P
u
(
e
|
f
)
, Reverse conditional probability
P
(
f
|
e
)
, Reverse conditional user probability
P
u
(
f
|
e
)
, etc. [0072] “such a machine-learned function may comprise a ranking function trained or established based, at least in part, on one or more inputs or applications of user-click feedback in conjunction with one or more statistical features extracted or derived from one or more ranking corpora.” Further see [0067].)
Regarding Claim 8, Pueyo teaches the elements of claim 1 as outlined above, and further teaches:
wherein the relevance query includes a target class of entities in the database, the target class of entities including the target entity, (Pueyo, [0024]-[0027] “Results of such ranking may be implemented, in whole or in part, for use with a search engine or other like information management systems, for example, responsive to search queries... In the context of a search, a query may be submitted via an interface, such as a graphical user interface (GUI), for example, by entering certain words or phrases to be queried, and a search engine may return a search results...” [0014]-[0017] “Entities may comprise, for example, celebrities, movies, locations, points of interest, events, or the like, just to name a few examples... As used herein, “information corpus” or in the plural form, “information corpora,” may refer to an organized collection of any type of information accessible over the Internet or associated with an intranet(s), such as, for example, one or more electronic documents, web sites, databases (e.g., user-generated, service provider-generated, etc.)... Vocabularies of information corpora may, although not necessarily, be organized around domain-specific topics and may include many entity classes or types (e.g., cities, people, landmarks, locations, animals, jobs, holidays, etc.) having a large number of relations (e.g., subsumed, subordinate, dependent, curative, hierarchical, associational, etc.), as was also indicated.” [0028] “For example, a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). Thus, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.”) [Examiner’s Note: The reference defines the search query as the query entity, the facet types/groups within the information corpora (e.g., related persons, related movies, related locations, etc.) represents a target class of entities in the database and the target entity can be either specific person location, or service.] and the method further includes:
determining a relevance ranking amongst entities in the target class of entities in the database based on the determined relevance of the target entity; (Pueyo, [0027] “As was indicated, in one particular implementation, a ranking function may comprise, for example, a machine-learned function trained to predict or estimate an actual CTR on a facet for a given entity representative of a query, though claimed subject matter is not so limited. Following the above discussion, in processing a query, a search engine may place documents that are deemed to be more likely to be relevant or useful in a higher position or slot on a returned search results page. In turn, documents that are deemed to be less likely to be relevant or useful may be placed in lower positions or slots among search results, for example. A user or client, thus, may receive and view a page or other electronic document that may include a listing of search results presented, for example, in decreasing order of relevance, just to illustrate one possible implementation.” [0036] “Accordingly, IIS 102 may employ one or more ranking functions, indicated generally in dashed lines at 132, to rank search results in an order that may, for example, be based, at least in part, on a relevance to a query. In one particular implementation, ranking function(s) 132 may determine relevance of one or more facets based, at least in part, on user-click feedback information in conjunction with one or more statistical features capturing relevance between facets and a query, as will be described in greater detail below.” [0083] “Accordingly, as particularly seen in FIG. 3A, since there is a diversity in the facet types for the query entity “Daniel Day-Lewis” (e.g., in a dictionary or pool of facets, etc.), the facets are not shown in an original ranking, but according to a ranking after grouping by type, such as, for example, “Related People,” shown at 306, and “Relater Movies,” shown at 308 Optionally or alternatively, related facets may be ranked without such a grouping, as illustrated in FIG. 3B.”) [Examiner’s Note: the system computes a relevance ranking across the entity facets (i.e., from the target class) based on their relevance to the query entity, the relevance ranking relevance from each facet’s determined relevance score (i.e., predicted CTR).] and
providing a response to the relevance query based on the determined relevance ranking. (Pueyo, [0027] “A user or client, thus, may receive and view a page or other electronic document that may include a listing of search results presented, for example, in decreasing order of relevance, just to illustrate one possible implementation.” [0085] “With regard to operation 406, one or more digital signals representing a listing of ranked facets may be transmitted to a user or client device via a communication interface.”)
Regarding Claim 9, Pueyo teaches the elements of claim 8 as outlined above, and further teaches:
wherein the method further includes at least one of: taking an action based on a most relevant entity in the relevance ranking; and presenting, in a user interface, a most relevant subset of entities in the relevance ranking. (Pueyo, [0027] “Accordingly, a search engine may employ one or more functions or operations to rank documents estimated to be relevant or useful such that, for example, more relevant or useful documents are presented or displayed more prominently among a listing of search results (e.g., more likely to be seen by a user or client, more likely to be clicked on, etc.)... A user or client, thus, may receive and view a page or other electronic document that may include a listing of search results presented, for example, in decreasing order of relevance, just to illustrate one possible implementation.” [0085] “With regard to operation 406, one or more digital signals representing a listing of ranked facets may be transmitted to a user or client device via a communication interface.” Further see [0083].)
Regarding Claim 11, Pueyo teaches the elements of claim 8 as outlined above, and further teaches:
wherein:
the target class of entities includes geographic location entities previously referenced by a user of a device; (Pueyo, [0014] “Entities may comprise, for example, celebrities, movies, locations, points of interest, events, or the like, just to name a few examples.” [0047] “As a way of illustration, during a query session, a user may, for example, first search for “India,” then may expand a query into “Bangalore, India,” and then may decide to search for “Cubbon park” within a certain time frame defining an event space (e.g., 15 minutes, etc.), just to illustrate one possible example. Here, for example, following information may be collected for such a query session: ...” [0021]-[0023] “More specifically, as illustrated in example implementations described herein, one or more entities or faceted relationships (e.g., entity-facet pairs, etc.) may be extracted or obtained, for example, from one or more extraction corpora so as to create a dictionary or pool of facets for an entity of interest. As will be seen, in one particular implementation, such extraction corpora may comprise, for example, a collective knowledge of user-generated content created by one or more on-line or virtual communities... Also, in certain implementations, user-click feedback information in the form of “click” or “view” statistics in relation to a particular facet for a given entity may be collected or obtained from one or more query logs, and one or more statistical values may be computed or estimated.”) [Examiner’s Note: Pueyo explicitly identifies “locations, points of interest” and “persons/celebrities” as facet entity classes that are ranked in response to a query entity (see e.g., [0014], [0028], and [0083]). Pueyo also defines the facets being derived and obtained from prior user interactions logged or generated from the user’s device.]
the relevance query requests a ranking of predicted relevance amongst the target class; (Pueyo, [0024]-[0027] “For a given entity representative of a query, then, relevant entity facets may be ranked using one or more established ranking functions... a ranking function may comprise, for example, a machine-learned function trained to predict or estimate an actual CTR on a facet for a given entity representative of a query, ...”) and
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (Pueyo, [0028] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). Thus, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” [0084] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). As such, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” Further see [0083].)
Regarding Claim 12, Pueyo teaches the elements of claim 8 as outlined above, and further teaches:
wherein:
the target class of entities includes person entities previously referenced by a user of a device; (Pueyo, [0014] “Entities may comprise, for example, celebrities, movies, locations, points of interest, events, or the like, just to name a few examples.” [0083] “Accordingly, as particularly seen in FIG. 3A, since there is a diversity in the facet types for the query entity “Daniel Day-Lewis” (e.g., in a dictionary or pool of facets, etc.), the facets are not shown in an original ranking, but according to a ranking after grouping by type, such as, for example, “Related People,” shown at 306, and “Relater Movies,” shown at 308 Optionally or alternatively, related facets may be ranked without such a grouping, as illustrated in FIG. 3B.” [0021]-[0023] “More specifically, as illustrated in example implementations described herein, one or more entities or faceted relationships (e.g., entity-facet pairs, etc.) may be extracted or obtained, for example, from one or more extraction corpora so as to create a dictionary or pool of facets for an entity of interest. As will be seen, in one particular implementation, such extraction corpora may comprise, for example, a collective knowledge of user-generated content created by one or more on-line or virtual communities... Also, in certain implementations, user-click feedback information in the form of “click” or “view” statistics in relation to a particular facet for a given entity may be collected or obtained from one or more query logs, and one or more statistical values may be computed or estimated.”) [Examiner’s Note: Pueyo explicitly identifies “locations, points of interest” and “persons/celebrities” as facet entity classes that are ranked in response to a query entity (see e.g., [0014], [0028], and [0083]). Pueyo also defines the facets being derived and obtained from prior user interactions logged or generated from the user’s device.]
the relevance query requests a ranking of predicted relevance amongst the target class; (Pueyo, [0024]-[0027] “For a given entity representative of a query, then, relevant entity facets may be ranked using one or more established ranking functions... a ranking function may comprise, for example, a machine-learned function trained to predict or estimate an actual CTR on a facet for a given entity representative of a query, ...”) and
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (Pueyo, [0028] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). Thus, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” [0084] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). As such, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” Further see [0083].)
Regarding Claim 14,
The claim recites substantially similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a method, and claim 14 is directed to a system.
Pueyo also discloses A system, comprising: a processor; and a memory storing instructions, that when executed by the processor,... (Pueyo, [0087] “Computing environment system 500 may include, for example, a first device 502 and a second device 504, which may be operatively coupled together via a network 506. In an embodiment, first device 502 and second device 504 may be representative of any electronic device, appliance, or machine that may have capability to exchange information over network 506. Network 506 may represent one or more communication links, processes, or resources having capability to support exchange or communication of information between first device 502 and second device 504. Second device 504 may include at least one processing unit 508 that may be operatively coupled to a memory 510 through a bus 512. Processing unit 508 may represent one or more circuits to perform at least a portion of one or more information computing procedures or processes.”)
Regarding Claim 15,
The claim recites substantially similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding Claim 16,
The claim recites substantially similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Claim 17,
The claim recites substantially similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding Claim 18,
The claim recites substantially similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding Claim 20,
The claim recites substantially similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a method, and claim 20 is directed to a non-transitory computer readable memory.
Pueyo also discloses a non-transitory computer readable memory storing instructions that, when executed by a processor, cause the processor to:... (Pueyo, [0089] “Computer-readable medium 518 may include, for example, any medium that can store or provide access to information, code or instructions for one or more devices in system 500. It should be understood that a storage medium may typically, although not necessarily, be non-transitory or may comprise a non-transitory device. In this context, a non-transitory storage medium may include, for example, a device that is physical or tangible, meaning that the device has a concrete physical form, although the device may change state.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6-7, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pueyo et al., (Pub. No.: US 20120030152 A1) in view of Gong et al., (Pub. No.: US 20210004682 A1).
Regarding Claim 6, Pueyo teaches the elements of claim 1 as outlined above:
While Pueyo teaches a collective knowledge of user-generated content and knowledge database that servers as corpus of knowledge from which the system learns relationships, determines relevancy, and provides ranking. Pueyo does not appear to explicitly teach:
wherein the database is a personal knowledge graph of a user, and the target entity and the context entities are aspects of a device associated with the user.
However, Pueyo in view of Gong teaches the limitation:
wherein the database is a personal knowledge graph of a user, and the target entity and the context entities are aspects of a device associated with the user. (Gong, [0033] “Application provider service module 162 may use the output of prediction model 166 to rank available applications from the application provider service in order of relevance to a current context of a user of computing device 110.” [0035] “Contextual information may include: device location and/or sensory information, user topics of interest (e.g., a user's favorite “things” typically maintained as a user interest graph or some other type of data structure), contact information associated with users... Contextual information may include information about the operating state of a computing device. For example, an application that is executed at a given time or in a particular location is an example of information about the operating state of a computing device. Other examples of contextual information based on the operating state of a computing device include, but are not limited to, positions of switches, battery levels, whether a device is plugged into a wall outlet or otherwise operably coupled to another device and/or machine, user authentication information (e.g., which user is currently authenticated-on or is the current user of the device), whether a device is operating in “airplane” mode, in standby mode, in full-power mode, the operational state of radios, communication units, input devices and output devices, etc.” [0065] “Feature module 263 may collect and store the contextual information, associated feature embeddings, or other information at context data store 267. Feature module 263 may organize the contextual information, associated feature embeddings, or other information stored at context data store 267 such that the information stored at context data store 267 is easily searchable and retrievable.” [0155] “Training data 391 used by training process 390 can include, upon user permission for use of such data for training, anonymized usage logs of sharing flows, e.g., content items that were shared together, bundled content pieces already identified as belonging together, e.g., from entities in a knowledge graph, etc.” [0042] “Such feature embeddings may be temporal or sequential type embeddings or other (i.e., non-temporal and non-sequential) types of embeddings, like application feature embeddings or user feature embeddings. Some examples of temporal or sequential type feature embeddings include: most recently installed application(s), most frequently executed application(s), most frequently executed game(s), and most frequently accessed media (e.g., whether song, album, video, show, e-book, or other media).”)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Pueyo and Gong, to incorporate the method for predicting future device interactions as taught by Gong. One would have been motivated to make such a combination in order to enable systems to easily account for changes in user behavior and interest, over time. Doing so would improve the accuracy of prediction or ranking systems (Gong [0004]).
Regarding Claim 7,
Pueyo in view of Gong teaches the elements of claim 6 as outlined above, and further teaches:
wherein the aspects of the device include one or more of: a time period of a day the device is used, a day of a week the device is used, a location the device is used, an application used on the device, a motion of the device, or a Wi-Fi state of the device. (Gong, [0035] “For example, an application that is executed at a given time or in a particular location is an example of information about the operating state of a computing device. Other examples of contextual information based on the operating state of a computing device include, but are not limited to, positions of switches, battery levels, whether a device is plugged into a wall outlet or otherwise operably coupled to another device and/or machine, user authentication information (e.g., which user is currently authenticated-on or is the current user of the device), whether a device is operating in “airplane” mode, in standby mode, in full-power mode, the operational state of radios, communication units, input devices and output devices, etc.” [0036] “The context may indicate characteristics associated with the physical and/or virtual environment of the user and/or the computing device at various locations and times. ... the context of a computing device may specify a calendar event, a meeting, or other event associated with a location and/or time.”)
Regarding Claim 10, Pueyo teaches the elements of claim 1 as outlined above:
Pueyo also teaches:
the relevance query requests a ranking of predicted relevance amongst the target class; (Pueyo, [0024]-[0027] “For a given entity representative of a query, then, relevant entity facets may be ranked using one or more established ranking functions... a ranking function may comprise, for example, a machine-learned function trained to predict or estimate an actual CTR on a facet for a given entity representative of a query, ...”) and
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (Pueyo, [0028] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). Thus, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” [0084] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). As such, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” Further see [0083].)
Pueyo does not appear to explicitly teach: wherein the target class of entities includes software application entities previously installed on a device. However, it would have been obvious in view of Gong. Hereinafter, Pueyo in view of Gong teaches
wherein: the target class of entities includes software application entities previously installed on a device; (Gong, [0042]-[0044] “Some examples of temporal or sequential type feature embeddings include: most recently installed application(s), most frequently executed application(s), most frequently executed game(s), and most frequently accessed media (e.g., whether song, album, video, show, e-book, or other media)... For example, feature module 163 may label, as primary sequence embeddings, one or more of: most recently installed application, most frequently executed application(s), most frequently executed game(s), and most frequently accessed media (e.g., whether song, album, video, show, e-book, or other media).” [0075]-[0076] “For example, sequence model 265 may calculate a cosine similarity matrix on feature embeddings of previously retrieved items from system 260 (e.g., all applications downloaded from computing system 160 to computing device 110). The self-attention model may concatenate the inputted sequential or temporal feature embeddings with the cosine similarity matrix... After weighting, each previously retrieved item's (e.g., each previously installed application's) individual context is appended to a respective item embedding (e.g., an application embedding) for that item.”)
the relevance query requests a ranking of predicted relevance amongst the target class; (Gong, [0033] “Application provider service module 162 may use the output of prediction model 166 to rank available applications from the application provider service in order of relevance to a current context of a user of computing device 110.” [0048] “Once trained, prediction model 166 may determine the characteristics of past user interactions with the application provider service that resulted in conversions of the applications for various different contexts. Prediction model 166 may rank or provide information for application provider service module 162 to rank, available applications to determine an application recommendation for a user at a particular time.” Further see [0063] and [0070].) and
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (Gong, [0052] “Application provider service module 162 may communicate via network 130 with application provider client module 120 about the prediction made be prediction module 166. Application service module 162 may send an indication of the particular application predicted by prediction module 166 along with instructions that cause application service client module 120 to modify, based on the indication of the particular application, user interface 114 such that the particular application is presented within user interface 114 more prominently than one or more other applications from the application provider service.” [0175]-[0176] “User interface 414A includes information indicative of a recommendation for a particular item that may interest a user of computing device 110, given a current context. User interface 414A may include a highest-ranking item (e.g., application) from the service provided by computing system 160, given the sequence output provided from sequence model 165 to prediction model 166, which is based on features extracted from contextual information associated with computing device 110 (e.g., by feature module 163).”)
The same motivation that was utilized for combining Pueyo and Gong as set forth in claim 6 is equally applicable to claim 10.
Regarding Claim 19,
The claim recites substantially similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Pueyo et al., (Pub. No.: US 20120030152 A1) in view of Diaz et al., (Pub. No.: US 20200175047 A1).
Regarding Claim 13,
Pueyo teaches the elements of claim 8 as outlined above.
Pueyo also teaches:
wherein: entities in the database include attributes; the relevance query includes a query attribute; (Pueyo, [0014]-[0015] “faceted relationships may describe recognized associational attributes between or among entities and facets or refer to some characteristic of mutual dependency between or among entities and facets. As will be described in greater detail below, faceted relationships may be represented, for example, via one or more entity-facet pairs associated with or extracted from the vocabulary of one or more information corpora, such as, for example, one or more extraction corpora... “Facet” or “entity facet,” as the terms used herein, may refer to one or more lexical objects representative of one or more concepts, aspects, properties, attributes, or characteristics of an entity that may be defined, for example, via a directed relationship between an entity e and an entity facet f, such as, for example, in a faceted relationship or relation (e, f).”)
the relevance query requests a ranking of predicted relevance amongst the target class; (Pueyo, [0024]-[0027] “For a given entity representative of a query, then, relevant entity facets may be ranked using one or more established ranking functions... a ranking function may comprise, for example, a machine-learned function trained to predict or estimate an actual CTR on a facet for a given entity representative of a query, ...”) and
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (Pueyo, [0028] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). Thus, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” [0084] “a displayed page may include one or more segmented portions incorporating various facets representing search results grouped, at least in part, by a type of faceted relationships (e.g., related persons, related movies, related locations, etc.). As such, facets may be ranked with respect to a particular entity (e.g., a query) in relation to one or more other facets within such one or more groups.” Further see [0083].)
Pueyo does not appear to explicitly teach: wherein the target class of entities includes a subset of person entities having an attribute that matches the query attribute. However, it would have been obvious in view of Diaz. Hereinafter, Pueyo in view of Diaz teaches:
wherein: entities in the database include attributes; the relevance query includes a query attribute; the target class of entities includes a subset of person entities having an attribute that matches the query attribute; (Diaz, [0036]-[0037] “For a user u∈U, a description, du, consists of a set of descriptive attributes. These attributes may be binary, scalar, categorical, and/or free text. For example, binary attributes may indicate whether the user is a smoker and/or whether the user has children. Scalar attributes may indicate, for example, age, height, weight, income, number of children, and/or number of photos... A query, qu, may define an ideal match in terms of a set of constraints (e.g., preferences) on attribute values (e.g., scalar and categorical attributes) that are desired of a potential “matching” entity. Those entities that are potential matches may be referred to as candidates. Each of the set of constraints of a query may be binary, scalar, or categorical. Binary constraints may indicate that a certain attribute be present in a candidate record or profile.” [0044] “These preferences are encoded as binary features, one for each possible attribute value. For example, if a user is interested in matches with red or blonde hair, then the features hair_red and hair_blonde may be set to true (1) and all other hair color features (e.g., hair_black) may be set to false (0)... Here, “must match” attributes are those that the querier requires to be satisfied for a match to be relevant... A set of match features can represent how well each attribute matches between a user's query and a candidate profile, as well as the importance of the attribute preferences of the querier.”)
the relevance query requests a ranking of predicted relevance amongst the target class (Diaz, [0006] “In one embodiment, the probability of relevance (i.e., predicted relevance) of a plurality of matches for a particular entity (e.g., querying entity) may be determined based at least in part on one or more of a plurality of behavioral features, where each of the matches is defined by a pair of entities including the querying entity and one of a plurality of entities. Based upon the probability of relevance for each of the plurality of matches, the plurality of matches may be ranked. At least a subset of the ranked matches may then be provided to the querying entity.” [0018] “Each entity may thereafter request, receive, and/or view their matches (e.g., in accordance with one or more target profiles). The matches for a particular entity may be provided automatically (e.g., via electronic mail or upon logging in to the match-making web site) or in real-time in response to a request received from the entity or a representative (e.g., individual) of the entity.”) and
the action includes presenting, on a user interface of the device, one or more of the entities in the target class according to the relevance ranking. (Diaz, [0006] “In one embodiment, the probability of relevance (i.e., predicted relevance) of a plurality of matches for a particular entity (e.g., querying entity) may be determined based at least in part on one or more of a plurality of behavioral features, where each of the matches is defined by a pair of entities including the querying entity and one of a plurality of entities. Based upon the probability of relevance for each of the plurality of matches, the plurality of matches may be ranked. At least a subset of the ranked matches may then be provided to the querying entity.” [0018] “Each entity may thereafter request, receive, and/or view their matches (e.g., in accordance with one or more target profiles). The matches for a particular entity may be provided automatically (e.g., via electronic mail or upon logging in to the match-making web site) or in real-time in response to a request received from the entity or a representative (e.g., individual) of the entity.” Further see [0060].)
Pueyo and Diaz are from the same field of endeavor and their disclosure generally relates to (determining or predicting the relevance).
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art to modify the combination of Pueyo and Diaz to incorporate the method for predicting relevance of matches as taught by Diaz. One would have been motivated to make such a combination in order to automatically identify and/or rank matches for entities, regardless of whether the entities are single users or other entities (Diaz [0016]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(Pub. No.: US 20150026105 A1) – “Christopher J. Henrichsen” relates to “Systems and method for determining influence of entities with respect to contexts.”
(Pub. No.: US 20220237246 A1) – “Ihab Francis Ilyas” relates to “Techniques for presenting content to a user based on the user's preferences.”
(Pub. No.: US 20170075910 A1) – “Leonardo A. Soto Matamala” relates to “App recommendation using crowd-sourced localized app usage data.”
(Pub. No.: US 20230306023 A1) – “Taesik NA” relates to “Training a machine learned model to determine relevance of items to a query using different sets of training data from a common domain.”
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/S.A.A./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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