DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Non-Final, first Office Action responsive to Applicant’s communication of 6/24/24, in which applicant filed the application. Claims 1-13 are pending in the instant application and have been rejected below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 recites the limitation "creating an entity vector" in the preamble; “providing an entity vector” in line 2; “creating an entity vector for a particular entity by encoding information about the particular entity into the entity vector” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim. It is unclear if there is one, two, or three “entity vectors.” It is unclear which vector “the entity vector” in line 5 refers to. Examiner’s best guess at interpreting the claim is: “creating an entity vector" in the preamble; “providing an entity vector” in line 2; “creating [[an]] the entity vector for a particular entity by encoding information about the particular entity into the provided entity vector” in lines 4-5. Additional corrections may be needed to dependent claims, depending on the amendments made.
Claim 9 recites similar limitations and is rejected for the same reasons.
Claims 2-8, 10-13 depend from claim 1 or claim 9 and are rejected for the same reasons.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One – MPEP 2106.04 - The claim 1 recites–
“A method for creating an entity vector, comprising:
providing an entity vector having one or more marketing oriented dimension data fields, each data field capable of storing a set of one or more real numbers;
creating an entity vector for a particular entity by encoding information about the particular entity into the entity vector, the information being one or more real numbers; and
displaying the created entity vector for the particular entity”.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” (commercial interactions – marketing or sales activities or behavior; or business relations). The claim is providing values for a company (i.e. entity), numerical values for their marketing details, then creating a vector (i.e. a numerical value) by “encoding” [where encoding is explained in dependent claim as including having a value between 0 and 1 representing how likely the company belongs to an industry (specification paragraph 33 as published explains this is assessing categories such as retail, financial services). Accordingly, claim 1 is directed to an abstract idea.
Step 2A, Prong Two – MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – “displaying” the created entity vector. Even if this claim is amended to explicitly recite a computer and a display of the result, and using a processor, to perform each step, the processor and display would be recited at a high-level of generality (i.e., as a generic processor performing each step) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. The claim is directed to an abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of just “displaying”, if interpreted/amended to be a computer, is/would be “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Examiner still suggests as a starting point, to at least recite a computer performing each limitation, as supported by the specification.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 9 is directed to a method at step 1, which is a statutory category. Claim 9 recites similar limitation as claim 1 above and is rejected for similar reasons. In addition, Step 2A, Prong One - The claim 1 recites–
A method for updating an entity vector, comprising:
retrieving an entity vector, the entity vector having one or more marketing oriented dimension data fields, each data field capable of storing a set of one or more real numbers;
selecting one or more dimension values of the entity vector to be updated; and
updating each selected dimension value of the entity vector..
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” (commercial interactions – marketing or sales activities or behavior; or business relations). The claim is providing values for a company (i.e. entity), numerical values for their marketing details, then selecting a value to update. As explained with regards to claim 1, the vectors represent company’s likelihood/values of being in an industry category.
Regarding 2a, prong 2 and step 2B, notably, claim 9 does not have any “additional elements” at this time – there is not even a computer recited. Should claim 9 be amended to include a “computer” performing each step, then the analysis above for claim 1, applies for claim 9, in that just having a computer perform each step would be “apply it” on a computer at step 2a, prong 2 and step 2b. Examiner still suggests as a starting point, to at least recite a computer performing each limitation, as supported by the specification.
Claims 2-8, 10-13, further narrow the abstract idea. Claims 2-4 recite that there are numerical values involved, but this is just narrowing the abstract idea. Claim 5 narrow the abstract idea by stating that there will also be a “cross” category, which is just stating that there is additional categorical structures that are considered. Claims 6-8, 13, describe entities that could be considered as well as example names of dimensions. This is just naming the data and is just narrowing the abstract idea. Claims 10-11 further narrow the abstract idea by stating details of how the values are updated (updating information or using a fitness function). Claim 12 further narrow the abstract idea by stating there are plural entities (i.e. companies) as well as relationships between companies, which is further narrowing the abstract idea.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
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 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.
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.
Claims 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Love (US 9,787,705) in view of Shacham (US 2017/0344902).
Concerning claim 1, Love discloses:
A method for creating an entity vector (Love - See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be… for nodes representing businesses), comprising:
providing an entity vector having one or more marketing oriented dimension data fields (Claim 7 gives examples of dimensions as including “revenue,” headcount, and “marketing competencies”) - Love discloses the limitations – See col. 6, lines 65-67; col. 7, lines 1-10 – In some cases, attributes may be identified by generating an attribute vector for each node, with the attribute values corresponding to dimensions in the attribute vector space. See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be similarly various, examples including, for nodes representing businesses, measures of the performance of the business, like profitability, revenue, amount of funding raised, employee count (disclosing headcount), employee turnover, market rank, markets served (disclosing marketing competencies), geolocation, and the like.).
Love discloses having attributes for the nodes of a company network, such as nodes representing businesses where the attributes could be revenue, funding raised, market rank (See col. 5, lines 35-57) or total investment amount (col. 3, lines 55-65). While Love discloses having values for the nodes and attribute vectors (See col. 6, lines 65-67; col. 7, lines 1-10), it is not explicitly clear if this is “stored.”
Shacham discloses:
“each data field capable of storing a set of one or more real numbers” (Shacham – See par 28 – data layer includes profile database 218 for storing profile for various organizations; when a representative of an organization initially registers the organization with the social networking service, the representative may be prompted to provide certain information about the organization. This information may be stored, for example, in the profile database 218, or another database (not shown). See par 50-51 - The similarity between a given industry and company may then be calculated as the cosine similarity cos (p.sub.i, p.sub.c); see par 51 - Thus, for example, the industry “computer networking” may have a cosine similarity of 0.566 to the term “backup”, a cosine similarity of 0.546 to the term “WAN”; Based on this cosine similarity, some or all of these terms may be selected to be candidates for new industries).
Love in combination with Shacham discloses:
creating an entity vector for a particular entity by encoding information about the particular entity into the entity vector, the information being one or more real numbers (Love – See col. 5, lines 13-28 - In other examples, the graph nodes may represent various non-document entities, such as people, governments, geographic regions, countries, demographic groups, and the like. In some cases, the relationships between these entities, for instance encoded, as edge weights, may include things like transactions between the entities, such as frequency or magnitude of transactions, co-occurrences of entities, similarities between entities, dissimilarities between entities, and the like. See col. 15, lines 52-60 - Various encodings may be selected to improve the functioning of a computer system. In some cases, values of matrices, like weights, may be normalized, for example, ranging between zero and one; to extent Shacham discloses storing, Shacham also discloses the limitation - See par 54 - one or both of the classifiers 300, 302 may also output a confidence score for each of the one or more industry predictions 324, 326, respectively, that it outputs. For example, a confidence score may be generated between 0.0 and 1.0, with 0.0 representing no confidence in the prediction and 1.0 representing full confidence in the prediction); and
displaying the created entity vector for the particular entity (Love Col. 7, lines 21-36 – FIG. 1 – identify first subset of nodes as having anomalous values; second subset as having representative values; Col. 7, lines 37-55 - some embodiments may send instructions to a client device to display a representation of the graph that visually distinguishes the first subset and the second subset, as indicated by block 20. Examples of a graphical representation of the graph are described below with reference to FIG. 4. In some cases, the graph may visually represent clusters of the nodes, in some cases showing the nodes, or in other cases not showing the nodes, to make the image easier to visually parse, though embodiments are consistent with the techniques. In some embodiments, the first subset of nodes may be visually identified differently depending upon whether the members of the first subset are outliers on a high side or outliers in a low side. Similarly, some embodiments may visually distinguish members of the first subset for members of the second subset, and members of both subsets from the clusters. In some cases, the graphical representation may be sent for display in a web browser, for instance encoded with instructions that engage a graphics processing unit for faster rendering).
Both Love and Shacham are analogous art as they are directed to modeling and assessing industries that companies belong to (See Love col. 5, lines 35-57; Shacham par 18). Love discloses having attributes for the nodes of a company network, such as nodes representing businesses where the attributes could be revenue, funding raised, market rank (See col. 5, lines 35-57) or total investment amount (col. 3, lines 55-65). Love also discloses encoding weights between 0 and 1, where the weights represent similarities between entities (See col. 5, lines 13-28; col. 15, lines 52-60). Shacham improves upon Love by disclosing having a database to store profile information for organizations (see par 28) where the information for a company further includes numerical values of similarity to different industries (See par 50-51) and also stating that there is a value between 0.0 and 1.0 for predicting an industry of a company (See par 54). One of ordinary skill in the art would be motivated to further include having a database as well as numerical values for industries of a company to efficiently store the assessments of markets served by a business in Love as values between 0 and 1 for what industry it belongs.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of having nodes representing businesses with vector attributes of Love to further store the profile information of the company and also have values between 0 and 1 for the business belonging to a named industry as disclosed in Shacham, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 9, Love discloses:
A method for updating an entity vector (Love – See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be… for nodes representing businesses; See Col. 21, lines 53-65 - Some embodiments may account for changes in topic associations with n-grams over time.), comprising:
retrieving an entity vector (Love – see FIG. 1, Col. 4, lines 61-67 – process being with obtaining a clustered graph 12 that includes nodes; See col. 6, lines 65-67; col. 7, lines 1-10 – In some cases, attributes may be identified by generating an attribute vector for each node), the entity vector having one or more marketing oriented dimension data fields (Love discloses the limitations – See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be similarly various, examples including, for nodes representing businesses, measures of the performance of the business, like profitability, revenue, amount of funding raised, employee count (disclosing headcount), employee turnover, market rank, markets served (disclosing marketing competencies), geolocation, and the like).
Love discloses having attributes for the nodes of a company network, such as nodes representing businesses where the attributes could be revenue, funding raised, market rank (See col. 5, lines 35-57) or total investment amount (col. 3, lines 55-65). While Love discloses having values for the nodes and attribute vectors (See col. 6, lines 65-67; col. 7, lines 1-10), it is not explicitly clear if this is “stored.”
Shacham discloses:
each data field capable of storing a set of one or more real numbers (Shacham – See par 28 – data layer includes profile database 218 for storing profile for various organizations; when a representative of an organization initially registers the organization with the social networking service, the representative may be prompted to provide certain information about the organization. This information may be stored, for example, in the profile database 218, or another database (not shown). See par 50-51 - The similarity between a given industry and company may then be calculated as the cosine similarity cos (p.sub.i, p.sub.c); see par 51 - Thus, for example, the industry “computer networking” may have a cosine similarity of 0.566 to the term “backup”, a cosine similarity of 0.546 to the term “WAN”; Based on this cosine similarity, some or all of these terms may be selected to be candidates for new industries);
Love in combination with Shacham discloses:
selecting one or more dimension values of the entity vector to be updated; and updating each selected dimension value of the entity vector (Love – See Col. 21, lines 53-65 - Some embodiments may account for changes in topic associations with n-grams over time. In some cases, a plurality of sets of n-grams pertaining to a given topic may be determined, each set of documents being associated with a duration of time, such as continuous ranges of time, like by year. In some cases, a user may select a particular time range for a particular topic or set of topics, and the corresponding time-range specific topic vectors may be selected for subsequent processing; See col. 20, lines 3-32 – learning topics and iterating through topics and adjust the model parameters to increase agreement with a training set; to extent Shacham discloses storing, Shacham also discloses the limitation - See par 18 - In an example embodiment, multiple computer implemented classifiers are used as part of a machine learning solution to predict industries for companies. This allows for periodic updating of industries listed in profiles or other documents for companies without the need for human intervention; see par 52 - For example, all existing companies in a company taxonomy (not pictured) may be periodically passed through the classifiers 300, 302 to predict new industries and/or industry changes for the companies, and corresponding profiles may then be automatically updated to reflect such changes.).
It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1.
Concerning claim 2, Love discloses having attribute vectors for each nodes (representing businesses) where the attributes can measure markets served. See col. 5, lines 35-57. However, Love does not explicitly disclose:
“The method of claim 1, wherein creating the entity vector further comprises generating an industry sub-vector that is part of the entity vector for the particular entity, the industry sub-vector having a plurality of industry categories wherein each industry category has a real number value.”
Shacham discloses the limitations (Shacham – fig. 3, 6, par 48-51 – complex features, such as from profile descriptions or tags, used to create Word2Vec model; Word2Vec models can be used to map each word to a vector of many elements, which represents the word's relation to other words. This vector is the neural network's hidden layer. See par 51 - the industry “computer networking” may have a cosine similarity of 0.566 to the term “backup”, a cosine similarity of 0.546 to the term “WAN;” Based on this cosine similarity, some or all of these terms may be selected to be candidates for new industries. See par 54, FIG. 6 – industry predictions 324, 326 have confidence level 300, 302; See par 56 - Thus, in this example, the existing industry classifier 300 has predicted “Security & Investigation” 610C with a high degree of confidence, and “Security & Investigation” 610C does not share a mutual parent with “Network Security” 608. See FIG. 6 – any of 606, 608, 610, and bubbles under “Finance” 602 are considered “industry sub-vectors”
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It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1. In addition, Love discloses having attribute vectors for each nodes (representing businesses) where the attributes can measure markets served. See col. 5, lines 35-57. Shacham improves upon Love by explicitly disclosing having sub-industries (See e.g. FIG. 6) where the sub-industries can be represented mathematically as to whether they belong (See par 48-51, 54). One of ordinary skill in the art would be motivated to further include using sub-industries to efficiently assess attributes of the entities of Love that are appropriate.
Concerning claim 3, Love and Shacham discloses:
The method of claim 1, wherein encoding information about the particular entity further comprising assigning a real number between a zero value and a one value, wherein the zero value indicates an entity with no membership in a dimension and the one value indicates a maximal membership in the dimension (Love - See col. 15, lines 52-60 - Various encodings may be selected to improve the functioning of a computer system. In some cases, values of matrices, like weights, may be normalized, for example, ranging between zero and one; See also Shacham – See par 54 - one or both of the classifiers 300, 302 may also output a confidence score for each of the one or more industry predictions 324, 326, respectively, that it outputs. This confidence score reflects the corresponding classifier's 300, 302 confidence level in the prediction. For example, a confidence score may be generated between 0.0 and 1.0, with 0.0 representing no confidence in the prediction and 1.0 representing full confidence in the prediction).
It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1.
Concerning claim 4, Love discloses:
The method of claim 2, wherein encoding information about the particular entity further comprising assigning a real number between a zero value and a one value, wherein the zero value indicates an entity with no membership in a dimension and the one value indicates a maximal membership in the dimension (Love - See col. 15, lines 52-60 - Various encodings may be selected to improve the functioning of a computer system. In some cases, values of matrices, like weights, may be normalized, for example, ranging between zero and one; See also Shacham – See par 54 - one or both of the classifiers 300, 302 may also output a confidence score for each of the one or more industry predictions 324, 326, respectively, that it outputs. This confidence score reflects the corresponding classifier's 300, 302 confidence level in the prediction. For example, a confidence score may be generated between 0.0 and 1.0, with 0.0 representing no confidence in the prediction and 1.0 representing full confidence in the prediction).
It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1.
Concerning claim 5, Love discloses having attribute vectors for each nodes (representing businesses) where the attributes can measure markets served. See col. 5, lines 35-57. However, Love does not explicitly disclose the limitations.
Shacham discloses the limitations:
The method of claim 1, wherein creating the entity vector further comprises encoding the information about the particular entity using a cross category encoding when a category structure interacts with a second category structure (Shacham – See par 5 - it is important for new categories and subcategories of industries to be applied to existing companies, if relevant. par 36 - These changes may include, for example, adding new industries in the taxonomy 304, typically as subcategories of existing categories or subcategories in the industry taxonomy 304. FIG. 3, par 41 - The new industry classifier 302 utilizes a second set of features 316 extracting from training data 318. In some instances the training data 318 may be the same as training data 306 or there may be some, but not complete, overlap; fig. 3, 6, par 48-51 – complex features, such as from profile descriptions or tags, used to create Word2Vec model; Word2Vec models can be used to map each word to a vector of many elements, which represents the word's relation to other words. This vector is the neural network's hidden layer. See par 51 - the industry “computer networking” may have a cosine similarity of 0.566 to the term “backup”, a cosine similarity of 0.546 to the term “WAN;” Based on this cosine similarity, some or all of these terms may be selected to be candidates for new industries. See par 55-57, FIG. 6- Here, with the same industry taxonomy 304, the existing industry classifier 300 has predicted an industry of “Computer Networking” 606C for a company called “Safe Networks,” while the new industry classifier 302 has predicted an industry of “Network Security” 608 for the same company. Since these predictions share the same parent (“IT” 600), the industry selection component 328 selects both of them as industries for “Safe Networks).
It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1 and claim 2. In addition, Love discloses having attribute vectors for each nodes (representing businesses) where the attributes can measure markets served. See col. 5, lines 35-57. Shacham improves upon Love by explicitly disclosing applying multiples categories and subcategories (See par 5, 36) and making a prediction for multiple subcategories for a company (See par 57). One of ordinary skill in the art would be motivated to further include combining categories and forming new subcategories to efficiently assess the attributes of the businesses of Love that are appropriate.
Concerning claim 6, Love discloses:
The method of claim 1, wherein the particular entity is one of a company, a group within a company, a product, product lines, a service, service lines, an organization, people, a team, capital, content, a school and a capital source (Love – See col. 4, lines 61-67 – nodes can correspond to a document (disclosing alternative of “content”); See Col. 5, lines 12-28 – nodes can represent people, demographic groups (disclosing alternative of people and group); See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be… for nodes representing businesses (disclosing alternative of company, organization)).
Claim 13 recites similar limitations as claim 6. Claim 13 is rejected for the same reasons as claim 6.
Concerning claim 7, Love discloses:
The method of claim 1, wherein the one or more dimensions are an industry category, headcount, revenue, growth rate, B2B-B2C, marketing competencies and technologies (Love – See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be similarly various, examples including, for nodes representing businesses, measures of the performance of the business, like profitability, revenue, amount of funding raised, employee count (disclosing headcount), employee turnover, market rank, markets served (disclosing marketing competencies), geolocation, and the like.
Concerning claim 8, Love discloses:
The method of claim 1 further comprising specifying the one or more dimensions for the particular entity (Love - See Col. 5, lines 35-57 - In some cases, where the nodes represent nondocument items, the attributes may be similarly various, examples including, for nodes representing businesses, measures of the performance of the business, like profitability, revenue, amount of funding raised, employee count (disclosing headcount), employee turnover, market rank, markets served (disclosing marketing competencies), geolocation, and the like;
Shacham – See par 52 - all existing companies in a company taxonomy (not pictured) may be periodically passed through the classifiers 300, 302 to predict new industries and/or industry changes for the companies, and corresponding profiles may then be automatically updated to reflect such changes).
Concerning claim 10, Love discloses:
The method of claim 9, wherein updating the selected dimension value further comprises executing an explicit update in which information in the entity vector is updated by directly computing updated information for the entity vector (Love – See Col. 21, lines 53-65 - Some embodiments may account for changes in topic associations with n-grams over time. In some cases, a plurality of sets of n-grams pertaining to a given topic may be determined, each set of documents being associated with a duration of time, such as continuous ranges of time, like by year. In some cases, a user may select a particular time range for a particular topic or set of topics, and the corresponding time-range specific topic vectors may be selected for subsequent processing; See also Shacham – See par 18 - In an example embodiment, multiple computer implemented classifiers are used as part of a machine learning solution to predict industries for companies. This allows for periodic updating of industries listed in profiles or other documents for companies without the need for human intervention).
It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1.
Concerning claim 11, Love discloses “over time” updating the associations of entities (See col. 21, lines 53-65) where the nodes/entities represent businesses (See col. 5, lines 35-57). Love further discloses using unsupervised learning techniques for learning topics (See col. 18, lines 7-31) as well as learning by iterating through topics and changing parameters to increase agreement with a training set (See col. 20, lines 3-32). While highly suggested, it is not explicitly clear if it discloses the limitations.
Shacham discloses the limitations:
The method of claim 9, wherein updating the selected dimension further comprises executing an implicit update using a fitness function wherein a value in a first entity vector influences a value of the entity vector being updated (Specification paragraph 46 as published gives examples - The fitness function may be any of a number of well-known functions such as self-consistency and/or accuracy relative to a known benchmark for all or a sample of the entity vector values… Instead, the closeness of one entity to another (using any of a number of well-known graph distance metrics, such as geodesic distance) is used to allow the values for one entity vector to influence the values of another entity… In one embodiment the approach is to create a machine learning model that estimates each of the entity vector values for each entity in the network using all of the other values for entities connected to the entity whose value is being estimated… One example of a fitness function is "maximum confidence" where each entity vector value has an associated confidence score and the fitness of a set of values is simply an aggregate measure such as "total confidence" or "median confidence".
Shacham discloses the limitations – See par 38 - The existing industry classifier 300 is trained via a first machine learning algorithm 308 to classify a candidate company into an existing industry. The new industry classifier 302, on the other hand, is trained by a second machine learning algorithm 310 to classify a candidate company into a new industry. see par 52 - For example, all existing companies in a company taxonomy (not pictured) may be periodically passed through the classifiers 300, 302 to predict new industries and/or industry changes for the companies, and corresponding profiles may then be automatically updated to reflect such changes.
It would have been obvious to combine Love and Shacham for the same reasons as discussed with regards to claim 1. In addition, Love discloses “over time” updating the associations of entities (See col. 21, lines 53-65) where the nodes/entities represent businesses (See col. 5, lines 35-57). Love further discloses using unsupervised learning techniques for learning topics (See col. 18, lines 7-31) as well as learning by iterating through topics and changing parameters to increase agreement with a training set (See col. 20, lines 3-32). Shacham improves upon Love by explicitly disclosing having classifiers using machine learning to update and predict industries for companies (See par 38, 52). One of ordinary skill in the art would be motivated to further include using classifiers that use machine learning to efficiently update the industries and details of the entities of Love that are appropriate.
Concerning claim 12, Love discloses:
The method of claim 9, wherein retrieving the entity vector further comprising retrieving a set of entities, an entity vector for each entity (Love – see FIG. 1, Col. 4, lines 61-67 – process being with obtaining a clustered graph 12 that includes nodes; See col. 6, lines 65-67; col. 7, lines 1-10 – In some cases, attributes may be identified by generating an attribute vector for each node) and relationship data of the set of entities (Love – see col. 5, lines 1-13 - In some embodiments, the edges are weighted edges, with scores indicating the strength of relationships between the nodes. See col. 10, lines 20-33 - in some embodiments, users may submit commands to view (or otherwise interrogate, e.g., search) trends, entity relationships).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blume 6,839,682 – disclosing dot-product for merchant being a member of a segment (see col. 15, lines 54-67) and having dot product between vectors for merchant vectors (See col. 30, lines 33-67)
Oehrle 9,569,729 – disclosing data objects able to represent sets of feature value pairs (See col. 28, lines 19-47)
Anderson 7,376,618 – disclosing forming vectors for merchants and merchant clusters (See col. 9, lines 25-31, FIG. 2)
Fang 2017/0154295 – disclosing determining new industry classifications for a company based on company data and profile data of employees and using an industry taxonomy database (See abstract)
Bothwell 2017/0046787 – disclosing selecting industries, supersectors and predicting likelihood of how well an industrial classification describes the entity (See par 71)
Psota 2017/0091320 – disclosing using natural language processing for entity resolution as well as providing correlations between two industry-related coding systems (See Abstract, par 149-151)
Abrahams, “Audience targeting by B-to-B advertisement classification: A neural network approach,” 2013, Expert Systems with Applications, Vol. 40, pages 2777-2791 – disclosing resolving industry classification (See page 2781) and sub-categories (See Appendix C, page 2786).
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619