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 .
Status of Claims
This action is in reply to the application filed on 08/27/24.
Claims 1-7 are currently pending and have been examined.
Priority Date
Applicant’s claim to the benefit of and priority to US Provisional Application 63/541,108 is acknowledged. Accordingly, a priority date of 09/28/23 has been given to this application.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 USC 112(a) issues that can arise when claims are amended without adequate support in the specification. The Examiner thanks the Applicant in advance.
Drawing Objections
The drawings are objected to because:
Fig. 6 contains a spelling mistake as it reads “sentense" at the bottom rather than “sentence”.
Fig. 9 appears to contain a typographical error in the fourth and fifth lines from the bottom of the page, where it reads “at least once a day for a consecutive days”. Examiner interprets “a” as being a typographical error for what was intended to be a number.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1, 6, 7 are objected to because of the following informalities:
Claim 1, Line 8 recites “the information”. While this does not cause true antecedent basis issues, as it is being interpreted to refer to the information accepted in lines 3-4 and to “the accepted information” in lines 6-7, Examiner recommends amending line 8 to recite “the accepted information” for improved coherence and clarity of the claim language.
Claims 6 and 7 contain similar recitations at lines 7 and 8, respectively. The discussion above with respect to Claim 1 is equally applicable to Claims 6-7.
Dependent claims 2-5 are objected to as they inherit the deficiencies of parent Claim 1.
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 1-7 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-5 are drawn to a system, Claim 6 is drawn to a method, and Claim 7 is drawn to a non-transitory computer readable medium, each of which are within the four statutory categories. Claims 1-7 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
receiving at least one of information related to age, gender, race or ethnicity, and region of a user who uses a lifestyle improvement support service, and
generating a question sentence to provide instruction on using a model for making a change into an expression which is appropriate for a user layer identified by the received information related to age, gender, race or ethnicity, and region of a user (“user layer” is given its broadest reasonable interpretation in view of the specification, and is interpreted as a “user group” related to age, gender, race, etc.)
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Obtaining user information such as age, gender, or race and requesting an expression change which is appropriate for a user of the obtained information (e.g., age, gender or race) using a model is a personal behavior that may be performed by a healthcare provider.
Independent claims 6 and 7 recites similar limitations and also recites an abstract idea under the same analysis.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
at least one processor as implementing the steps of the abstract idea (Claim 1)
a non-transitory computer readable medium storing a program that causes a computer to execute information processing as implementing the steps of the abstract idea (Claim 7)
a machine learning model, the machine learning model learning at least an expression that is in accordance with the information as implementing the step of making a change into an expression which is appropriate for a user layer identified by the accepted information (Claims 1, 6, 7)
The broad recitation of general purpose computing elements (at least one processor, non-transitory computer readable medium storing a program, machine learning model) at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools.
Regarding the processor and non-transitory CRM, these are understood to be general purpose computing elements functioning in their ordinary capacities to implement the steps of the abstract idea (see instant specification, paras. [0050]-[0052]; see [0123], “The processor in the above-described exemplary embodiments refers to a processor in a broad sense including, in addition to a general-purpose processor (for example, a Central Processing Unit (CPU)), a dedicated processor (for example, a Graphical Processing Unit (GPU), an Application specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a program logic device, etc.)”. No particulars of the non-transitory CRM are provided other than reiterating claim language ([0008], [0151]); as such it is given its broadest reasonable interpretation as a general purpose computing element and only amounts to mere instructions to apply the abstract idea.
Regarding the machine learning model/machine learning model learning at least an expression that is in accordance with the information, the specification does not provide any specifics of the machine learning model. The broad recitation of a machine learning model, in this case to make a change in expression appropriate for a particular user group based on age, gender, race, etc., only amounts to using the machine learning model as a tool to apply data to a model and generate a result (see MPEP 2106.05(f)(2)).
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1, 6 and 7 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Depending Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims:
Claim 2 recites limitations pertaining to using the question sentence and a first message addressed to the user in the model, and transmitting a second message whose main body is a reply sentence from the model or a second message attached with the reply sentence to the user, which is also certain methods of organizing human activity including managing personal behaviors, as a healthcare provider could apply the question sentence and first message to a model and then provide (broadest reasonable interpretation of “transmit”) a reply sentence or second message generated by the model to the user.
Recitation of “the processor” and “the machine learning model” amount to mere instructions to apply the abstract idea as discussed above with respect to Claim 1; see MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 3 recites limitations pertaining to using the question sentence and a first message that is in accordance with information related to the user’s lifestyle in the model, and transmitting the second message whose main body is the reply sentence from the model or the second message attached with the reply sentence to the user, which is also certain methods of organizing human activity including managing personal behaviors, as a healthcare provider could apply the question sentence and first message in accordance with information related to the user’s lifestyle to a model and then provide (broadest reasonable interpretation of “transmit”) a reply sentence or second message generated by the model to the user.
Recitation of “the processor” and “the machine learning model” amount to mere instructions to apply the abstract idea as discussed above with respect to Claim 1; see MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 4 recites limitations pertaining to generating the first message in accordance with a diagnosis result of the user’s lifestyle, which is also certain methods of organizing human activity including managing personal behavior, as a doctor may generate a message pertaining to a diagnosis result of the user’s lifestyle. Recitation of “the processor” amounts to mere instructions to apply the abstract idea as discussed above with respect to Claim 1; see MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 5 recites limitations pertaining to generating the first message in accordance with the user’s history of activity for a diagnosis result of the user’s lifestyle, which is also certain methods of organizing human activity including managing personal behavior, as a doctor may generate a message pertaining to the user’s activity history for a diagnosis result of the user’s lifestyle. Recitation of “the processor” amounts to mere instructions to apply the abstract idea as discussed above with respect to Claim 1; see MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claim 1. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-5 recite additional subject matter which amounts to additional elements consistent with those identified in the analysis of Claim 1 above. As discussed above with respect to Claim 1 and integration of the abstract idea into a practical application, recitation of these additional elements (e.g., processor, machine learning model) only amounts to invoking computers as a tool to perform the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Dependent claims 2-5, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-7 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bradea et. al. (US Publication 20250068893A1) in view of Wada et. al. (US Publication 20160342763A1).
Regarding Claim 1, Bradea discloses:
at least one processor ([0099]), wherein the at least one processor accepts at least one of information related to age, gender, race or ethnicity, and region of a user ([0021] teaches on the personalization module receiving information about a segment to which a user belongs; a logged in user may be identified as being female and age 32; [0023] teaches on providing an example prompt for a 32 year old woman – e.g., age and gender have been received in order to know this is audience for generating tailored content; [0087] teaches on the personalization module, which is understood to be part of the computing system architecture which includes a processor per Fig. 6, receiving a set of attributes comprising information about the user which may include “demographic data” which includes “age and gender” per [0090]; [0079]-[0080] teach on an example of receiving user information for a 32 year old female; see Fig. 4/block 410) who uses a [website] , and
generates a question sentence to instruct a machine learning model to make a change into an expression which is appropriate for a user layer identified by the accepted information ([0023] teaches on inputting to an AI model (“machine learning model”), a tuning parameter and a prompt comprising the set of attributes and information about the segment; per [0090] “segments” may include demographic attributes like age and gender, and as such, “segment” is interpreted as being a “user layer” identified by the accepted information (age and gender); a prompt (“question sentence”) sent to the generative AI may be “Generate a product description for a grey hooded fleece for a woman of age 32…”; [0047] teaches on a similar example of prompting the generative AI to write a description for a 37 year old female; [0082], see Fig. 4B, box 450, showing 4 possible updates to the same product description which are vary from the original description by tuning parameter “temperature value” ranging from 0-1 in which a value of 0 corresponds to minimal change and value of 1 corresponds to the most change; the varied product descriptions 480, 482, 484, 488, are interpreted as “changes” in “expression”; [0082] teaches on box 449 in Fig. 4B being an updated description based on the user’s information (32/female) in box 460); the machine learning model learning at least an expression that is in accordance with the information (Fig. 4A, box 447 is original content per [0083]; Fig. 4B shows box 450 with 4 different updates to original product description in which the content expression has been tuned by varying degrees “T” as well as box 449, which is th updated description based on the accepted user information (female/age 32) in box 460 per para. [0082] – 449 is the expression in accordance with the information).
Bradea’s system is directed to modifying content for a user of a shopping webpage. Bradea does not explicitly teach on a user of a lifestyle improvement support service, but Wada, which is directed to a health monitoring assist system, teaches:
a user who uses a lifestyle improvement support service ([0080] teaches on a health monitoring assist system, which takes account of a user’s activities and provides assist information (“lifestyle improvement support”) in accordance with personal characteristics, specifically, the relationship between exercise/walking and hypotensive effect using time-series data relating physical activity and effect on blood pressure in that user; [0131]-[0132] teach on the system generating a “user assisting message” based on the user’s results for presentation to a user to monitor his health or continue to exercise; the messages may include different types of information such as an evaluation of exercise carried out and/or advice for future exercise, and the effect of the exercise on the user; as the system is described as providing information on exercise and health effect to provide assist to a user, it is interpreted as a “lifestyle improvement support service”).
As stated above, Bradea’s system is directed to modifying content for a user of a shopping webpage based on the user’s demographic information. Wada’s system is directed towards a computerized health monitoring assist program. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate Wada’s health monitoring/assisting content into the framework of Bradea’s system, which collects user demographic information and modifies content so it is appropriate for the user’s demographic such that Wada’s health content is customized to an individual user’s demographics, with the motivation of applying Bradea’s user-specific customized content to provision health assist information suited to personal needs of individual users (Wada [0080]).
Regarding Claim 2, Bradea/Wada teach the limitations of Claim 1. Bradea further discloses wherein the processor provides the machine learning model with the question sentence and a first message addressed to the user ([0047] teaches on an example where the question sentence instructs the generative AI model to generate a new product description for a 37 year old female in which a current description (“first message addressed to the user”) is “‘Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!’ The user is a 37 year old female” (question sentence); the prompt (question sentence) and tuning parameter temperature are provided to the generative AI), and transmits a second message whose main body is a reply sentence from the machine learning model or a second message attached with the reply sentence to the user ([0048] teaches on, responsive to receiving the prompt (question sentence) described in preceding limitation with respect to [0047], the generative AI system outputs “personalized content” responsive to the tuning parameter and the prompt, which is “Discover Elegance and Ease in our Fleece Hoodie—the Supreme Sweatshirt for Hiking” (interpreted as “second message”), which is used to update product description in dynamic content field of a display, which is interpreted as “transmitting” the second message to the user if it is output for display on the user’s display).
Regarding Claim 3, Bradea/Wada teach the limitations of Claim 1. Bradea further discloses wherein the processor provides the machine learning model with the question sentence and a first message that is in accordance with information related to the user’s lifestyle ([0047] teaches on an example where the prompt (question sentence instructs the generative AI model to “Generate a new product description fir a grey hooded fleece”; the prompt includes “The user is a 37 year old female. She is a Samsung phone user and enjoys mountain activities and trekking”; the prompt also includes the current description which is “Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!”; Examiner interprets the current description with the wording “active woman” to read on “first message that is in accordance with information related to the user’s lifestyle”, e.g., an “active” lifestyle; the prompt (question sentence) and tuning parameter are provided to the generative AI), and transmits the second message whose main body is the reply sentence from the machine learning model or the second message attached with the reply sentence to the user ([0048] teaches on, responsive to receiving the prompt described in preceding limitation with respect to [0047], a “personalized content” responsive to the tuning parameter and the prompt, which is “Discover Elegance and Ease in our Fleece Hoodie—the Supreme Sweatshirt for Hiking” (interpreted as “second message” where “hiking” is understood to be related to user’s lifestyle information, “active woman” from the first message), which is used to update product description in dynamic content field of a display, which is interpreted as “transmitting” the second message to the user if it is output for display on the user’s display).
Regarding Claim 4, Bradea/Wada teach the limitations of Claim 3. Bradea does not disclose, but Wada teaches: wherein the processor generates the message in accordance with a diagnosis result of the user’s lifestyle (see Fig. 12, which shows in the first row of the table, for a change in blood pressure > 15, a message of “Look at the period of <$SP-posi>. The effect of exercise is remarkable! You are of a type who feels the effect of exercise more, the more you do it. Keep going!”; para. [0133] describes Fig. 12 and teaches on a plurality of templates corresponding to a particular condition using different values such as blood pressure, change in blood pressure, etc.; [0135] teaches on SP-posi indicating a date range from Feb 5, 2013 to Feb 12, 2013; Examiner interprets the message being generated in accordance with a diagnosis result of the user’s lifestyle, as the user’s exercise (lifestyle) has improved their blood pressure by a change greater than 15 (diagnosis result); the second row of the table in Fig. 12 indicates a similar condition as the top line, except that Db is >10, e.g., less of a change in blood pressure and the message generated is accordingly, e.g., using language such as “very good” rather than “remarkable” - interpreted as generating a message specifically based on the diagnosis result).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify Bradea/Wada with these teachings of Wada to generate a message (e.g., the first message of Bradea) in accordance with a diagnosis result of the user’s lifestyle, with the motivation of presenting to the user, a causal relationship between an activity (e.g., lifestyle - exercise/walking) and an effect (diagnosis result) (Wada [0062]).
Regarding Claim 5, Bradea/Wada teach the limitations of Claim 3. Bradea does not disclose, but Wada teaches: wherein the processor generates the first message in accordance with the user’s history of activity for a diagnosis result of the user’s lifestyle (see Fig. 12, which shows in the first row of the table, for a change in blood pressure > 15, a message of “Look at the period of <$SP-posi>. The effect of exercise is remarkable! You are of a type who feels the effect of exercise more, the more you do it. Keep going!”; para. [0133] describes Fig. 12 and teaches on a plurality of templates corresponding to a particular condition using different values such as blood pressure, change in blood pressure, etc.; [0135] teaches on SP-posi indicating a date range from Feb 5, 2013 to Feb 12, 2013 which is interpreted as “user’s history of activity” for the week of Feb 5-12, 2013; Examiner interprets the message being generated in accordance with a diagnosis result of the user’s lifestyle, as the user’s exercise (lifestyle) has improved their blood pressure (diagnosis result) over the course of the week (history of activity); the second row of the table in Fig. 12 indicates a similar condition as the top line, except that Db is >10, e.g., less of a change in blood pressure and the message generated is accordingly, e.g., using language such as “very good” rather than “remarkable” – interpreted as generating a message specifically based on the user’s history and diagnosis result).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify Bradea/Wada with these teachings of Wada to generate a first message (e.g., original message of Bradea) in accordance with the user’s history of activity for a diagnosis result of the user’s lifestyle, with the motivation of presenting to the user, a causal relationship between an activity (e.g., lifestyle - exercise/walking) and an effect (diagnosis result) when there may be a time lag between activity and effect, so the user can stay motivated to carry on the activity (Wada [0062]).
Regarding Claim 6, Bradea/Wada teach the limitations of Claim 1. Claim 6 recites limitations that are the same or substantially similar to Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 6. Claim 6 is rejected for the same reasons as Claim 1.
Regarding Claim 7, Bradea/Wada teach the limitations of Claim 1. Claim 7 recites limitations that are the same or substantially similar to Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 7. Claim 7 is rejected for the same reasons as Claim 1. Claim 7 additionally recites the following limitations which are also taught by Bradea: a non-transitory computer readable medium storing a program that causes a computer to execute an information processing method ([0100]-[0102]).
Conclusion
In the interest of expediting prosecution, Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20100223341, teaching on electronic messaging tailored to user interest
US Publication 20210334831, teaching on a system and method of identifying audience demographics and delivering relative content to audience
US Publication 20160203267, teaching on systems and methods for generating customized medical media
US Publication 20220270744, teaching on generating and delivering psychographically segmented content to targeted user devices
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE-MARIE K ALDERSON whose telephone number is (571)272-3370. The examiner can normally be reached on Mon-Fri 9:00am-5:00pm EST and generally schedules interviews in the timeframe of 2:00-5:00pm EST.
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682